Upload folder using huggingface_hub

.devcontainer/devcontainer.json

@@ -0,0 +1,33 @@
+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "demoapp/app.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run demoapp/app.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

.gitattributes

@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+Documentation/Documentation[[:space:]]for[[:space:]]MAPLE.pdf filter=lfs diff=lfs merge=lfs -text
+demoapp/all_bills.csv filter=lfs diff=lfs merge=lfs -text

.github/workflows/add_collaborators.yml

@@ -0,0 +1,51 @@
+# Author: Rishab 
+name: Add Collaborators v1.1
+
+on: 
+  push:
+    branches:
+      - master
+      - main
+      - dev
+
+jobs:
+  add-collaborators:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v2
+      - name: Add Collaborators
+        uses: actions/[email protected]
+        with:
+          github-token: ${{secrets.ADMIN_GITHUB_TOKEN}}
+          script: |
+            const fs = require('fs');
+            const readline = require('readline');
+            const readInterface = readline.createInterface({
+              input: fs.createReadStream('./COLLABORATORS'),
+              output: process.stdout,
+              console: false
+            });
+            readInterface.on('line', async function(line) {
+              try {
+                await github.repos.checkCollaborator({
+                            owner: context.repo.owner,
+                            repo: context.repo.repo,
+                            username: line,
+                            });
+              } catch(err) {
+                console.log(err)
+                if(err.toString() === "HttpError: Not Found"){
+                  await github.repos.addCollaborator({
+                            owner: context.repo.owner,
+                            repo: context.repo.repo,
+                            username: line,
+                            permission: 'push'
+                            });
+                } else { 
+                  core.setFailed(err.toString())
+                }
+
+              }
+            });
+

.github/workflows/release_archive.yml

@@ -0,0 +1,18 @@
+name: Archive Repository into Release v1.0
+on: 
+  push:
+    tags:
+      - "spark-archive-*"
+jobs:
+  tagged-release:
+    name: "Tagged Release"
+    runs-on: "ubuntu-latest"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+      - name: Release
+        uses: softprops/action-gh-release@v1
+        with:
+          files: |
+            "*.md"
+            "*.MD"

.gitignore

@@ -0,0 +1,173 @@
+
+
+
+# Jetbrains Products
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+
+# VS Code Standard
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+
+
+# Mac OS
+# Source : https://raw.githubusercontent.com/github/gitignore/main/Global/macOS.gitignore
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+# Windows 
+# Source: https://raw.githubusercontent.com/github/gitignore/main/Global/Windows.gitignore
+# Windows thumbnail cache files
+Thumbs.db
+Thumbs.db:encryptable
+ehthumbs.db
+ehthumbs_vista.db
+
+# Dump file
+*.stackdump
+
+# Folder config file
+[Dd]esktop.ini
+
+# Recycle Bin used on file shares
+$RECYCLE.BIN/
+
+# Windows Installer files
+*.cab
+*.msi
+*.msix
+*.msm
+*.msp
+
+# Windows shortcuts
+*.lnk
+
+# Linux
+# Source: https://raw.githubusercontent.com/github/gitignore/main/Global/Linux.gitignore
+*~
+
+# temporary files which can be created if a process still has a handle open of a deleted file
+.fuse_hidden*
+
+# KDE directory preferences
+.directory
+
+# Linux trash folder which might appear on any partition or disk
+.Trash-*
+
+# .nfs files are created when an open file is removed but is still being accessed
+.nfs*
+
+__pycache__/
+env/

COLLABORATORS

@@ -0,0 +1,4 @@
+Vy Nguyen - Github Username: vynpt  
+Andy Yang - Github Username: ayang903   
+Gauri Bhandarwar - Github Username: gbhandarwar    
+Weining Mai - Github Username: weibb123

Deployment/readme.md

@@ -0,0 +1 @@
+Demo app: https://maple-billsummarization.streamlit.app/

Documentation/Documentation for MAPLE.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6668bf5eb5b8f1f12b97e4a8a9d233fb56fb9820c0335f8f2ed14490d43b9c8b
+size 4408831

Documentation/Research.md

@@ -0,0 +1,97 @@
+# Research MAPLE
+
+**Research Methods (MAPLE Project)**
+
+**Team members:** Andy Yang, Gauri Bhandarwar, Weining Mai, Vy Nguyen
+
+1. **Overview**
+
+This paper goes over the methods for summarization and tagging of Massachusetts bills for the MAPLE Platform. The goal is to simplify the legal language and content to make it comprehensible for a broader audience (9th-grade comprehension level). This summarization has to be done while still retaining the key information about the bills and maintaining full accuracy. This involves utilizing Large Language Models (LLMs) to summarize and categorize the content effectively. This paper will summarize some of the methods, processes, considerations, and evaluation metrics used during our research process of finding the most suitable method to execute the project.
+
+1. **Preliminary Research on LLMs and Performance Criteria Definition**
+
+Before we dive into our research of LLMs we have defined a few criteria that will help us evaluate the models we are potentially using. These criteria and metrics are listed below:
+
+- **Comprehensibility: Model's ability to generate text that is comprehensible (9th grade level)**
+- **Content Accuracy: The model's ability to retain critical legal details and nuances in the summarization process.**
+- **Scalability: The ability of the model to handle a large number of documents efficiently.**
+- **Customizability: The ease with which the model can be fine-tuned or adapted to specific requirements of the task such as focusing on certain aspects of legal bills.**
+- **Cost Efficiency: Considers the full expense of using the model including : training, fine-tuning, and runtime inference costs.**
+- **Overall Suitability: A general suitability measure for the model, considering all the factors above.**
+
+Based on these criteria and reading up on the various LLMs from different sites and papers (refer to sources), we have come up with a general table which contains all the models to whom we have assigned a high, medium or low grade for each of the criteria.
+
+![alt text](https://lh7-us.googleusercontent.com/3dFnsfy5fdB-shdqnwpRB-Ol1rJ6QAbFrnUtGZ5J4hKMth8p4UlOvMui9Bo5qw5h-NEempkDnTNr3ma8xex4vCB2NzhzgfAYKo49h8pgK9BbcBttGFmQg12JPi6299D4BPXweCmNxiTjlMPjnzQgo1w)
+
+**Figure 1** : Comparison of LLMs and methods based on key evaluation metrics
+
+Above we have provided a succinct table generalizing the use-cases for each LLM / method based on criteria such as Comprehensibility, Content Accuracy, Scalability, Cost Efficiency, and given them a final sum of scores based on the factors that are most important for this project. These metrics will help us choose the best model for our application needs. Content Accuracy is one of the most important factors as we are dealing with legislation. This need for accuracy was also highlighted by the client as it is imperative for the summaries to be accurate to maintain the trustworthiness of the MAPLE platform.
+
+In addition to that, comprehensibility is a close second however not as important because we can hopefully tune each model to summarize in a more simple manner. Due to the fact that cost is also another consideration, we have given High scores to BERT, T5, and GPT-3.5. However, we have not tested how far tuning some models like LLAMA and customized transformers can go yet. Regarding the Customizability criteria, LLAMA, T5, and Customer Training with Transformation models seem to offer high customizability as they can be fine-tuned for specific tasks and domains. Moreover, GPT-3.5, LLAMA, and T5 are moderately cost-efficient, considering their performance while XLNet, BERT, and Customer Training with Transformation might be costlier due to resource-intensive fine-tuning and training processes. We will also aditionally explore using GPT-4 and GPT-3.5 Turbo given the new OpenAI updates.
+
+Overall, LLAMA, T5, and Customer Training with Transformer models appear to be the most suitable choices for simplifying Massachusetts bills since they excel in comprehensibility, content accuracy, customizability, and overall performance. Hence, we will put more emphasis on these models while conducting performance tests and cost analysis.
+
+1. **Preliminary Testing**
+
+![alt text](https://lh7-us.googleusercontent.com/se1QGXdHkGWoeaHh9Rpb9ow5zMnrWx4a83BVUXXGcStAfK4ni83xDTmnkuvk22E8CHJnO87aEYsSXScpc2ExcErr9xcQqPlH592iLX4vsKSb9g1bSM2h646F2w-UdvXEWMo7Wo0jR6DRgipbZvb7Q6U)
+
+**Figure 2:** Rough Sketch of Development Process
+
+![alt text](https://lh7-us.googleusercontent.com/_w1LP_RRoSIny-A-yS-d8C4RMyAZgDnr_TS1ny_a22vI3Giml-Rhm1kOaHA_ZxNjjUavWom3T9NJFMR6ORlfX_bop86Q6BN-xq_gUvxBnV47eX_rXuRxoU9kIuVfFw-I5Ru0sHW9wyUgEOLVRHw7ALg)
+
+**Figure 3** : Scraping, Summarizing and tagging bills with a sample set of 10 bills
+
+We were able to successfully use the API to retrieve key data for ten bills including the : Title, Bill Number, Docket Number, General Court Number and Document Text. We mainly used the document text and title, feeding it through GPT-3.5 to create a Web-app (see Appendix) to demonstrate the summaries that were outputted. We generated summaries based on their degree of comprehensibility – Kid level, College level and Professional (legal) level. For each bill in our sample set, we generated 3 types of summaries and evaluated them.
+
+Right now we’re using ten bills for testing. We can pull all 6.5k bills using the /Documents endpoint, then passing each BillNumber into the /Documents/{document_number} endpoint. The final goal here would be a csv of bills, with different features so that we can do EDA and eventually use to feed into our models.
+
+
+We noticed that in most of the bills the models were just summarizing the bills vaguely and not including important details such as the main amendment the bill was making, the sector the bill was affecting (ex:Automotive industry, Home, etc), the city or town the bill is affecting and other important information. In addition to that, we noticed that as we decreased the degree (College level to Kid level), the model got less specific and gave more vague summaries that did not relate to the specific key point of the bill but talked more about the general issue that it was tackling. As to improve, we will experiment with more prompts making sure summaries can capture main ideas. For example, we can give a large language model a persona or a role such as attorney and be specific on the requirements.
+
+1. **Research Methods/tools for Improvement and Evaluation of Summaries**
+
+We have included the summary of one interesting research paper that has to deal with the evaluation of the quality of summaries based on a method that relies on reference summaries created by humans.
+
+**"ROUGE: A Package for Automatic Evaluation of Summaries" by Chin-Yew Lin:**
+
+Link : [https://aclanthology.org/W04-1013.pdf](https://aclanthology.org/W04-1013.pdf)
+
+(Summary of Paper Generated by SciSummary Tool)
+
+This paper introduces ROUGE (Recall-Oriented Understudy for Gisting Evaluation), a toolkit used for the evaluation of automatic summarization and machine translation software. The focus of ROUGE is to determine the quality of summaries by comparing them to reference summaries created by humans (usually professional abstractors). The paper emphasizes the importance of recall in summary evaluation, considering the coverage of information as critical.
+
+ROUGE employs various metrics, including ROUGE-N (overlap of N-grams between the system and reference summaries), ROUGE-L (longest common subsequence), ROUGE-W (weighted longest common subsequence), and others. These metrics provide different perspectives for assessing the content similarity between generated and reference summaries. Experiments using ROUGE on different summarization tasks have demonstrated its efficacy and correlation with human judgments, validating its utility as an automatic evaluation method for various kinds of summaries (extractive, abstractive, single-document, or multi-document). The paper concludes that ROUGE is a valuable, scalable, and reliable tool in the development and assessment cycle of automatic summarization systems.
+
+**V. Conclusion**
+
+Based off of our current findings and knowledge, we have one major takeaway which is to drive focus towards generating summaries that have the key important details such as the main amend the bill was making, the sector the bill was affecting (ex:Automotive industry, Home, etc), the city or town the bill is affecting and other important information. We hope to research more evaluation methods and techniques that will help us achieve this goal in the context of legal documents.
+
+**Works Cited / Sources / Appendix**
+
+**Sources for :**  Figure 1 : Comparison of LLMs and methods based on key evaluation metrics (Numbered 1-11 as shown on Figure 1)
+
+1. Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. OpenAI Blog. Link: [https://arxiv.org/abs/2005.14165](https://arxiv.org/abs/2005.14165)
+2. OpenAI API Documentation. Link: [https://beta.openai.com/docs/](https://beta.openai.com/docs/)
+3. Yang, Z., et al. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. Link: [https://arxiv.org/abs/1906.08237](https://arxiv.org/abs/1906.08237)
+4. XLNet Documentation, Hugging Face. Link: [https://huggingface.co/transformers/model_doc/xlnet.html](https://huggingface.co/transformers/model_doc/xlnet.html)
+5. LLAMA: A Multi-Task Meta Learning Approach for Few-Shot Classification, 2021. Link: [https://arxiv.org/abs/2112.01547](https://arxiv.org/abs/2112.01547)
+6. Devlin, J., et al. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Link: [https://arxiv.org/abs/1810.04805](https://arxiv.org/abs/1810.04805)
+7. BERT Documentation, Hugging Face. Link: [https://huggingface.co/transformers/model_doc/bert.html](https://huggingface.co/transformers/model_doc/bert.html)
+8. Raffel, C., et al. (2019). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Link: [https://arxiv.org/abs/1910.10683](https://arxiv.org/abs/1910.10683)
+9. T5 Documentation, Hugging Face. Link: [https://huggingface.co/transformers/model_doc/t5.html](https://huggingface.co/transformers/model_doc/t5.html)
+10. Vaswani, A., et al. (2017). Attention Is All You Need. Link: [https://arxiv.org/abs/1706.03762](https://arxiv.org/abs/1706.03762)
+11. Custom Training of Transformers, Hugging Face. Link:[https://huggingface.co/transformers/custom_datasets.html](https://huggingface.co/transformers/custom_datasets.html)
+
+**More Article Sources for LLMs + Implementation methods:**
+
+[https://medium.com/analytics-vidhya/text-summarization-using-bert-gpt2-xlnet-5ee80608e961](https://medium.com/analytics-vidhya/text-summarization-using-bert-gpt2-xlnet-5ee80608e961)
+
+[https://medium.com/@ps.augereau/the-power-of-named-entity-recognition-ner-with-llm-2ff413360c5#:~:text=It%20refers%20to%20the%20method,understanding%20and%20analyzing%20text%20data](https://medium.com/@ps.augereau/the-power-of-named-entity-recognition-ner-with-llm-2ff413360c5#:~:text=It%20refers%20to%20the%20method,understanding%20and%20analyzing%20text%20data).
+
+**Appendix**
+
+GPT 3.5 Web app Demo screenshot
+
+![alt text](https://lh7-us.googleusercontent.com/VSlaYRa6BNNDIczT_O54r43UPZYhbvMpA0y_jcaqnHKd22HSCpxxfmJKntk3rliGoTZgZqfNZVnn1gMBuQH1ob1OC3k4l_ZcY5ZS3lCmVxTkX7CpO3Jhpc0jnGkaZeCOaGSevud1zaSjKfisAKoNxVk)
+
+![alt text](https://lh7-us.googleusercontent.com/Ps0_uWz9BCgyWSRtfZTBJWyATV5fUcRAJaANSqWDQfKNWib3sI6zKqMZ21zkU-YJbPFIQOJrYqQptJAljuEfHpGoJBxoiJzTNkmmqha-z-3Y6YJIA2iwZXfnQfDSCFHpw-t1fcgCkhWYV_OeVchhHrU)

EDA/top2vec.ipynb

@@ -0,0 +1,582 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BlYwantP4fpF",
+        "outputId": "32e70ec1-c239-452f-8cb2-c6646522ac62"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting top2vec\n",
+            "  Downloading top2vec-1.0.29-py3-none-any.whl (26 kB)\n",
+            "Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from top2vec) (1.23.5)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from top2vec) (1.5.3)\n",
+            "Requirement already satisfied: scikit-learn>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from top2vec) (1.2.2)\n",
+            "Requirement already satisfied: gensim>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from top2vec) (4.3.2)\n",
+            "Collecting umap-learn>=0.5.1 (from top2vec)\n",
+            "  Downloading umap-learn-0.5.4.tar.gz (90 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.8/90.8 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting hdbscan>=0.8.27 (from top2vec)\n",
+            "  Downloading hdbscan-0.8.33.tar.gz (5.2 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m53.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.10/dist-packages (from top2vec) (1.9.2)\n",
+            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim>=4.0.0->top2vec) (1.11.3)\n",
+            "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim>=4.0.0->top2vec) (6.4.0)\n",
+            "Collecting cython<3,>=0.27 (from hdbscan>=0.8.27->top2vec)\n",
+            "  Using cached Cython-0.29.36-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (1.9 MB)\n",
+            "Requirement already satisfied: joblib>=1.0 in /usr/local/lib/python3.10/dist-packages (from hdbscan>=0.8.27->top2vec) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.2.0->top2vec) (3.2.0)\n",
+            "Requirement already satisfied: numba>=0.51.2 in /usr/local/lib/python3.10/dist-packages (from umap-learn>=0.5.1->top2vec) (0.56.4)\n",
+            "Collecting pynndescent>=0.5 (from umap-learn>=0.5.1->top2vec)\n",
+            "  Downloading pynndescent-0.5.10.tar.gz (1.1 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m45.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from umap-learn>=0.5.1->top2vec) (4.66.1)\n",
+            "Requirement already satisfied: tbb>=2019.0 in /usr/local/lib/python3.10/dist-packages (from umap-learn>=0.5.1->top2vec) (2021.10.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->top2vec) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->top2vec) (2023.3.post1)\n",
+            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from wordcloud->top2vec) (9.4.0)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from wordcloud->top2vec) (3.7.1)\n",
+            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.51.2->umap-learn>=0.5.1->top2vec) (0.39.1)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba>=0.51.2->umap-learn>=0.5.1->top2vec) (67.7.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->top2vec) (1.16.0)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (1.1.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (4.43.1)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (23.2)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->wordcloud->top2vec) (3.1.1)\n",
+            "Building wheels for collected packages: hdbscan, umap-learn, pynndescent\n",
+            "  Building wheel for hdbscan (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for hdbscan: filename=hdbscan-0.8.33-cp310-cp310-linux_x86_64.whl size=3039150 sha256=9f003292f1f8cb7ad7c75405b0ae9c5dd34781ed3d3e84b67c79cbb555901c90\n",
+            "  Stored in directory: /root/.cache/pip/wheels/75/0b/3b/dc4f60b7cc455efaefb62883a7483e76f09d06ca81cf87d610\n",
+            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for umap-learn: filename=umap_learn-0.5.4-py3-none-any.whl size=86770 sha256=32645d482bfb04f8b261441c153ed2211fcacdb89e09dd301d22767f4bb4e117\n",
+            "  Stored in directory: /root/.cache/pip/wheels/fb/66/29/199acf5784d0f7b8add6d466175ab45506c96e386ed5dd0633\n",
+            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for pynndescent: filename=pynndescent-0.5.10-py3-none-any.whl size=55615 sha256=34156dbda65a71550da9cc4bf7d24d226a288d55fc81e4d3db3a8a182871d238\n",
+            "  Stored in directory: /root/.cache/pip/wheels/4a/38/5d/f60a40a66a9512b7e5e83517ebc2d1b42d857be97d135f1096\n",
+            "Successfully built hdbscan umap-learn pynndescent\n",
+            "Installing collected packages: cython, pynndescent, hdbscan, umap-learn, top2vec\n",
+            "  Attempting uninstall: cython\n",
+            "    Found existing installation: Cython 3.0.4\n",
+            "    Uninstalling Cython-3.0.4:\n",
+            "      Successfully uninstalled Cython-3.0.4\n",
+            "Successfully installed cython-0.29.36 hdbscan-0.8.33 pynndescent-0.5.10 top2vec-1.0.29 umap-learn-0.5.4\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install top2vec"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "from top2vec import Top2Vec\n",
+        "import nltk\n",
+        "import re"
+      ],
+      "metadata": {
+        "id": "PnJXA9RQ9ACj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv('all_bills.csv')"
+      ],
+      "metadata": {
+        "id": "ei_AkGL74iks"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['DocumentText']"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "g9VfgRw9PDgQ",
+        "outputId": "6df29022-8d82-4fb2-9d4a-03bf045d9022"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0                                                     NaN\n",
+              "1       \\tParagraph (c) of Section 91 of chapter 32 of...\n",
+              "2       \\tSECTION 1. Chapter 20 Section 23 of the Gene...\n",
+              "3       \\tSECTION 1. Chapter 26 of the General Laws is...\n",
+              "4       \\tSECTION 1. Chapter 32A of the General Laws i...\n",
+              "                              ...                        \n",
+              "6591    \\tSECTION 1. Section 3 of chapter 258B, as app...\n",
+              "6592    \\tSECTION 1. Section 222 of chapter 6 of the G...\n",
+              "6593    \\tSECTION 1. Chapter 12 of the General Laws, a...\n",
+              "6594    \\tSECTION 1. Section 172(a)(3) of chapter 6, a...\n",
+              "6595    \\tSection 127I of Chapter 111 of the General L...\n",
+              "Name: DocumentText, Length: 6596, dtype: object"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nltk.download('punkt')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rM2quh8P9Q8p",
+        "outputId": "c9d4dfe6-3bc4-4b17-f955-b41c59f4ce7e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "documents = df['DocumentText'].tolist()"
+      ],
+      "metadata": {
+        "id": "5AJtHmk35Pjz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from nltk.corpus import stopwords\n",
+        "\n",
+        "nltk.download('stopwords')\n",
+        "stop_words = set(stopwords.words('english'))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_woW4lDxPTa1",
+        "outputId": "6dbebac9-3dc8-420c-abf0-d1978b9e978a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
+            "[nltk_data]   Package stopwords is already up-to-date!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def tokenize(text):\n",
+        "    if not isinstance(text, str):  # Check if the input is a string, could be null\n",
+        "        return []\n",
+        "\n",
+        "    wordstoremove = []\n",
+        "\n",
+        "    all_stopwords = stop_words.union(wordstoremove) # remove stopwords\n",
+        "\n",
+        "    text = re.sub(r'(section|chapter)\\s+(\\d+|\\d+[a-zA-Z])', r'\\1_\\2', text, flags=re.IGNORECASE) #combine \"section\" and \"chapter\" followed by a number into one token\n",
+        "\n",
+        "    tokens = [word for word in nltk.word_tokenize(text) if word.lower() not in all_stopwords and word.isalnum()] #only alphanumeric\n",
+        "    tokens = map(str.lower, tokens)\n",
+        "\n",
+        "    return tokens\n",
+        "\n",
+        "tokenized_documents = [\" \".join(tokenize(doc)) for doc in documents]\n"
+      ],
+      "metadata": {
+        "id": "W9_54EGS9W8C"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "len(tokenized_documents)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cne6GU2k6OJI",
+        "outputId": "b2a8a635-f35c-436c-a899-dd52d10b3199"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6596"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "tokenized_documents[5]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 160
+        },
+        "id": "E7FVyM1bSySo",
+        "outputId": "023d7323-6202-4703-a3d3-632edcee0ce0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'general laws appearing 2020 official edition hereby amended striking first sentence second paragraph inserting words commission shall provide child adult 26 years age younger active retired employee commonwealth insured group insurance commission coverage cost 1 hearing aid per ear hearing aid defined every 36 months upon written statement child treating physician hearing aids necessary regardless etiology general laws appearing 2020 official edition hereby amended striking first sentence second paragraph inserting words individual group health maintenance contract except contracts providing supplemental coverage medicare governmental programs shall provide coverage benefits children 21 years age younger insured contracts expenses incurred cost 1 hearing aid per hearing impaired ear hearing aid defined every 36 months upon written statement child treating physician hearing aids necessary regardless etiology'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = Top2Vec(tokenized_documents)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WcNxyEeA4_Al",
+        "outputId": "da47e569-f7b1-4370-dfe0-e44a7a5295b3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "2023-10-26 18:48:40,257 - top2vec - INFO - Pre-processing documents for training\n",
+            "INFO:top2vec:Pre-processing documents for training\n",
+            "2023-10-26 18:48:50,371 - top2vec - INFO - Creating joint document/word embedding\n",
+            "INFO:top2vec:Creating joint document/word embedding\n",
+            "2023-10-26 19:01:10,148 - top2vec - INFO - Creating lower dimension embedding of documents\n",
+            "INFO:top2vec:Creating lower dimension embedding of documents\n",
+            "2023-10-26 19:01:54,142 - top2vec - INFO - Finding dense areas of documents\n",
+            "INFO:top2vec:Finding dense areas of documents\n",
+            "2023-10-26 19:01:54,586 - top2vec - INFO - Finding topics\n",
+            "INFO:top2vec:Finding topics\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 86 topics found\n",
+        "model.get_num_topics()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f6K7dLRr5HzO",
+        "outputId": "ad6600c3-f4c2-49c9-8340-5aa5cb45a634"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "86"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "topic_sizes, topic_nums = model.get_topic_sizes()"
+      ],
+      "metadata": {
+        "id": "nBOvsphU6TsK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "topic_sizes, topic_nums"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_ficy58j6Val",
+        "outputId": "c3aa223b-78b8-4bec-a7d2-8419d038abd4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([269, 222, 184, 179, 168, 161, 157, 149, 149, 144, 138, 128, 122,\n",
+              "        118, 105, 101,  97,  95,  95,  90,  90,  89,  88,  88,  84,  83,\n",
+              "         83,  80,  79,  77,  76,  75,  74,  73,  73,  73,  72,  71,  69,\n",
+              "         69,  69,  68,  66,  65,  65,  64,  64,  62,  62,  62,  61,  60,\n",
+              "         59,  55,  54,  53,  52,  50,  49,  49,  49,  48,  48,  48,  48,\n",
+              "         47,  46,  44,  44,  41,  40,  37,  37,  36,  36,  35,  35,  33,\n",
+              "         32,  30,  29,  29,  25,  25,  24,  24]),\n",
+              " array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+              "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+              "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
+              "        51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
+              "        68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
+              "        85]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "topic_words, word_scores, topic_nums = model.get_topics(86)\n"
+      ],
+      "metadata": {
+        "id": "incHyPTC6Wky"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "topic_words"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "z9iNedwx6bIO",
+        "outputId": "4b3ecf45-bfe7-4763-8360-ae52d75df083"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([['senate', 'representatives', 'recommendations', ...,\n",
+              "        'massachusetts', 'member', 'vice'],\n",
+              "       ['taxable', 'taxpayer', 'tax', ..., 'deducted', 'percent',\n",
+              "        'assessors'],\n",
+              "       ['ballot', 'election', 'voters', ..., 'names', 'choice', 'papers'],\n",
+              "       ...,\n",
+              "       ['pesticide', 'pesticides', 'applicator', ..., 'contain',\n",
+              "        'application', 'polyfluoroalkyl'],\n",
+              "       ['affordable', 'real', 'transfers', ..., 'respect', 'excise',\n",
+              "        'barnstable'],\n",
+              "       ['detail', 'police', 'superannuation', ..., 'eeo', 'overtime',\n",
+              "        'passage']], dtype='<U15')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# example of a topic\n",
+        "topic_words[15]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2rnYtcMsXeFj",
+        "outputId": "8f52f099-c97a-4023-c003-d768ba546278"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['assessors', 'exemption', 'valuation', 'qualify', 'domicile',\n",
+              "       'exemptions', 'levy', 'taxes', 'taxation', 'real', 'occupied',\n",
+              "       'town', 'assessed', 'abatement', 'tax', 'deferral', 'city',\n",
+              "       'deadline', 'excessive', 'parcel', 'property', 'domiciled',\n",
+              "       'senior', 'surcharge', 'condominium', 'seeks', 'raised', 'estate',\n",
+              "       'clause', 'assessor', 'homeowners', 'voters', 'bears', 'income',\n",
+              "       'owned', 'shift', 'estates', 'residential', 'deed', 'filed',\n",
+              "       'eighteenth', 'lien', 'decedent', 'certifies', 'year', 'find',\n",
+              "       'qualifies', 'nonpayment', 'mayor', 'question'], dtype='<U15')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "word_scores"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "z4l9d3jT6b7b",
+        "outputId": "f4f2a0f0-fd81-4da3-f4a8-6f3c00069e73"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.8107485 , 0.76834613, 0.75448215, 0.7522937 , 0.7377519 ,\n",
+              "        0.73589534, 0.6997856 , 0.6921088 , 0.6573265 , 0.63992095,\n",
+              "        0.63835424, 0.63353044, 0.6259784 , 0.6161628 , 0.6136363 ,\n",
+              "        0.61158836, 0.5917918 , 0.59075636, 0.58117676, 0.56409115,\n",
+              "        0.5382238 , 0.53799826, 0.53545713, 0.5059549 , 0.4826639 ,\n",
+              "        0.47157508, 0.47056037, 0.44637784, 0.42377606, 0.42022526,\n",
+              "        0.4182046 , 0.41616178, 0.41021925, 0.40735632, 0.38740572,\n",
+              "        0.3687165 , 0.36273247, 0.360592  , 0.3579704 , 0.35582146,\n",
+              "        0.35553864, 0.3502974 , 0.34837744, 0.34191263, 0.34068382,\n",
+              "        0.33285785, 0.3286861 , 0.30501026, 0.30333635, 0.29198554],\n",
+              "       [0.6651959 , 0.65478694, 0.6534573 , 0.6209365 , 0.6145715 ,\n",
+              "        0.60944825, 0.578282  , 0.5713501 , 0.56624013, 0.5658411 ,\n",
+              "        0.5317777 , 0.5303422 , 0.52419686, 0.48169795, 0.47910136,\n",
+              "        0.4783927 , 0.46999556, 0.46702635, 0.45967853, 0.4526093 ,\n",
+              "        0.43824282, 0.4347589 , 0.43464437, 0.4327737 , 0.4292552 ,\n",
+              "        0.42650297, 0.42139018, 0.41712484, 0.4164454 , 0.40693063,\n",
+              "        0.3967685 , 0.3945102 , 0.39216924, 0.390456  , 0.39004803,\n",
+              "        0.37376937, 0.36957765, 0.3680184 , 0.36738494, 0.35837635,\n",
+              "        0.33738944, 0.3371164 , 0.33683145, 0.3082167 , 0.30309388,\n",
+              "        0.30301207, 0.28206518, 0.27416578, 0.2736898 , 0.27321362]],\n",
+              "      dtype=float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def save_topics_to_txt(topic_words, word_scores, topic_nums, filename=\"bills_topics.txt\"):\n",
+        "    with open(filename, 'w') as file:\n",
+        "        for topic_num, words, scores in zip(topic_nums, topic_words, word_scores):\n",
+        "            file.write(f\"Topic {topic_num}\\n\")\n",
+        "            for word, score in zip(words, scores):\n",
+        "                file.write(f\"{word}: {score}\\n\")\n",
+        "            file.write(\"\\n\")\n",
+        "\n",
+        "save_topics_to_txt(topic_words, word_scores, topic_nums, filename=\"bills_topics.txt\")\n"
+      ],
+      "metadata": {
+        "id": "2vKfMzeA667n"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "VbuUEU2OYlVW"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+
+  17. Interpretation of Sections 15 and 16.
+
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+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
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+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
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+
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+
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+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

Project_outline.md

@@ -0,0 +1,73 @@
+# Maple-Bill Summarization and Tagging Project Document
+
+## Vy Nguyen, Gauri Bhandarwar, Andy Yang, and Weining Mai *2023-October-05*
+
+## Overview
+
+*In this document, based on the available project outline and summary of the project pitch, to the best of your abilities, you will come up with the technical plan or goals for implementing the project such that it best meets the stakeholder requirements.*
+
+### A. Provide a solution in terms of human actions to confirm if the task is within the scope of automation through AI.
+
+*To assist in outlining the steps needed to achieve our final goal, outline the AI-less process that we are trying to automate with Machine Learning. Provide as much detail as possible.*
+
+We are trying to automate the process of summarizing and tagging Massachusetts bills. For example, there are approximately thousands of bills here: [Links](https://malegislature.gov/Bills/Search)\
+Our goal will be building a system that summarizes all of these bills into more digestible pieces of texts, as well as tagging each bill so that we can separate them into different categories.
+
+### B. Problem Statement:
+
+*In as direct terms as possible, provide the “Data Science” or "Machine Learning" problem statement version of the overview. Think of this as translating the above into a more technical definition to execute on. eg: a classification problem to segregate users into one of three groups on based on the historical user data available from a publicly available database*
+
+A natural language generation problem to generate easy-understanding summaries and category tags for pending legislation based on the public data available from Massachusetts Legislature.
+
+### C. Checklist for project completion
+
+*Provide a bulleted list to the best of your current understanding, of the concrete technical goals and artifacts that, when complete, define the completion of the project. This checklist will likely evolve as your project progresses.*
+
+1. Scrape bills, tagging
+2. Data Preprocessing
+3. Summarization and tagging
+4. Deployment and automation
+
+### D. Outline a path to operationalization.
+
+We hope to have the generated summaries available in multiple formats either as csv or rich text. If time permits, it would be interesting to explore the output on a simple AI application power by Gradio or Streamlit so users can see generated summaries and tagging for the bills.
+
+We hope to have the output available in multiple formats (csv, rich text, etc). If time permits it would be interesting to explore population of the output on the website itself and automate the process of summarization and tagging using something like Apache airflow.
+
+## Resources
+
+### Data Sets
+
+- Collect bills from Massachusetts Legislature
+    
+    https://malegislature.gov/Bills/Search
+
+    The /Documents endpoint provides a list of every single publicly available document with information like BillNumber, Title, GeneralCourtNumber, etc. We will use the "BillNumber" field with the /Documents/{document_number} endpoint to get the text for all the bills
+    
+    API endpoint to get the BillNumbers: https://malegislature.gov/api/Documents
+  
+    API endpoint to get the raw text: https://malegislature.gov/api/Documents/{document_number}
+
+  There are approximately 6595 bills, from the first endpoint.
+    
+- Massachusetts General Law sections and subsections as category tags
+    
+    https://malegislature.gov/Laws/GeneralLaws
+    
+    Tags in JSON found here: https://drive.google.com/drive/folders/1QVI6wbLREsU4jQ_llogiTKM0YgC_dqVL
+    
+- Additional information on this task (including examples)
+    
+    https://github.com/codeforboston/maple/issues/843
+
+- API:
+  https://malegislature.gov/api/swagger/index.html?url=/api/swagger/v1/swagger.json#/
+    
+
+### References
+
+Armand Ruiz, AI director at IBM, for his helpful guides and blogs on Generative AI.
+
+link: [nocode.ai](http://nocode.ai)
+
+# Weekly Meeting Update

Prompts Engineering/prompts.md

@@ -0,0 +1,44 @@
+## Prompt 1
+Prompt1 = 
+""""I want you to act as a text summarizer and provide a concise summary of the bill separated by ####. 
+Summarize the text so {level} can understand Your summary should be no more than 4 sentences. 
+Do not include your opinions or interpretations and do not make up any false information. 
+Also, I want you to define the main topic for this bill For example, Topic: Bills about unjustice
+
+s #### \
+{context} \
+s #### """
+
+## Prompt 2
+Prompt2 = 
+""""I want a summarizer that reads and summarizes legal bills and legislation. I want the summary to not be vague and include the general amendments, area the legislation is affecting, the purpose, the town/city it is affecting, etc.  Based on the bill text {context} given please create a concise and easy-to-understand summary with the relevant key points.
+"""
+
+## Prompt 3
+Prompt3 = 
+"""You are an experienced attorney in Massachusetts. Write a concise summary of the bill separated by #### so {level} can understand. Do not make up false information.
+Include the general amendments, area the legislation is affecting, the purpose, the town and city it is affecting. 
+
+s #### \
+{context} \
+s #### """
+
+## Prompt 4
+template = """"Your task is to generate a concise summary of a bill
+from massachusetts legislature. Make sure to capture the main idea of the bill.
+
+Summarize the bill below, delimited by triple backticks, and summarize in a way so {level} can understand.
+
+These are the informations
+bill: ```{context}``` 
+tags: #### {schema} ####
+
+provide your summary in a consistent style
+Summary: your summary
+
+category: choose one category from the list of categories, delimited by ####, that is relevant to the summary
+
+next, after you select a category, identify tags that are relevant to your summary.
+Do not make up any false information.
+
+"""

Prompts Engineering/readme.md

@@ -0,0 +1 @@
+Keep track of used prompts

README.md

@@ -0,0 +1,43 @@
+# MAPLE (Bill Summarization, Tagging, Explanation)  
+In this project, we generate summaries and category tags for of Massachusetts bills for [MAPLE Platform](https://www.mapletestimony.org/). The goal is to simplify the legal language and content to make it comprehensible for a broader audience (9th-grade comprehension level) by exploring different ML and LLM services.  
+
+This repository contains a pipeline from taking bills from Massachusetts legislature, generating summaries and category tags leveraging different the Massachusetts General Law sections, creating a dashboard to display and save the generated texts, to deploying and integrating into MAPLE platform.
+
+## Roadmap of Repository Directories
+* [Documentation](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/Documentation):  
+  ```Research.md```: our research on large language models and evaluation methods we planned to use for this project.  
+  ```Documentation MAPLE.pdf```: includes detail operation of our model for future use and improvement.
+  
+* [EDA](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/EDA): the notebook ```eda.ipynb``` includes our work from scraping data that takes bills from MAPLE Swagger API, creating a dataframe to clean and process data, making visualizations to analyze data and explore characteristics of the dataset.
+  
+* [demoapp](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/demoapp):   
+  ```app.py```: contains the codes of the LLM service we used and the wepapp we made using Streamlit. The webapp allows user to search for all bills.  
+  ```app2.py```: we test on top 12 bills from MAPLE website. We extract information from [Massachusetts General Law](https://malegislature.gov/Laws/GeneralLaws) to add context for the summaries of these bills.  
+  Other files: helper files to be imported in the above two Python app files.
+  
+* [Prompts Engineering](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/Prompts%20Engineering): ```prompts.md``` stores all prompts that we tested.
+  
+* [Tagging](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/Tagging): contains the list of categories and tags.
+  
+* [Deployment](https://github.com/vynpt/ml-maple-bill-summarization/tree/main/Deployment): contains the link of our Streamlit deployed webapp.   
+
+## Ethical Implications
+The dataset used for this project is fully open sourced and can be access through Mass General Laws API.   
+
+Our team and MAPLE agree about putting disclaimer that this text is AI-generated.  
+
+Although we make use of open source transformers to evaluate hallucination with Vectara, it is important to have experts and human evaluation to further maintain a trustworthy LLM system.
+
+## Resources and Citation
+* https://huggingface.co/docs/transformers/tasks/summarization 
+* https://huggingface.co/vectara/hallucination_evaluation_model  
+* https://github.com/vectara/hallucination-leaderboard  
+* https://www.nocode.ai/llms-undesirable-outputs/  
+* https://learn.deeplearning.ai/  
+* https://blog.langchain.dev/espilla-x-langchain-retrieval-augmented-generation-rag-in-llm-powered-question-answering-pipelines/  
+
+## Team Members
+Vy Nguyen - Email: [email protected]   
+Andy Yang - Email: [email protected]   
+Gauri Bhandarwar - Email: [email protected]    
+Weining Mai - Email: [email protected] 

Tagging/categories_tagging.md

@@ -0,0 +1,226 @@
+List of Category available: [Environmental, Education, Health Care, Criminal Justice, Taxation, Transportation, Housing, Civil Rights, Labor and Employment, Budget and Appropriations, Public Safety, Technology and Innovation, Immigration, Economic Development, Social Services]
+
+Category: Environmental
+
+Tags in Environmental
+  
+    Climate Change Mitigation
+    Renewable Energy Initiatives
+    Biodiversity Conservation
+    Water Pollution Control
+    Sustainable Agriculture Practices
+    Air Quality Improvement
+    Waste Reduction Programs
+    Coastal Erosion Management
+    Environmental Impact Assessments
+    Wildlife Habitat Protection
+
+Category: Education
+
+Tags in Education
+
+    Curriculum Development
+    Digital Learning Resources
+    Teacher Professional Development
+    Early Childhood Education
+    Special Education Services
+    Literacy Programs
+    Vocational Training
+    Education Technology Integration
+    School Infrastructure Upgrades
+    Student Mental Health Support
+
+Category: Health Care
+
+Tags in Health Care
+  
+    Universal Health Coverage
+    Healthcare Access for All
+    Mental Health Services Expansion
+    Disease Prevention Programs
+    Elderly Care Services
+    Healthcare Quality Standards
+    Health Information Technology
+    Maternal and Child Health
+    Public Health Emergency Preparedness
+    Healthcare Workforce Training
+
+Categpry: Criminal Justice
+
+Tags in Criminal Justice
+
+    Police Reforms
+    Prisoner Rehabilitation
+    Community Policing Initiatives
+    Restorative Justice Programs
+    Criminal Sentencing Reform
+    Victim Support Services
+    Legal Aid for the Underprivileged
+    Juvenile Justice System Overhaul
+    Hate Crime Prevention Measures
+    Court System Modernization
+
+Category: Taxation
+
+Tags in Taxation
+  
+    Progressive Tax Reform
+    Corporate Taxation Policies
+    Income Tax Deductions
+    Property Tax Relief
+    Sales Tax Revision
+    Tax Compliance Regulations
+    Tax Transparency Measures
+    Wealth Redistribution Initiatives
+    Small Business Tax Breaks
+    Tax Fraud Prevention Measures
+
+Category: Transportation
+
+Tags in Transportation
+  
+    Public Transit Expansion
+    Road Infrastructure Maintenance
+    Bike and Pedestrian Path Development
+    Traffic Congestion Reduction
+    Electric Vehicle Adoption Incentives
+    Freight Transportation Efficiency
+    Aviation Safety Regulations
+    Railroad Infrastructure Modernization
+    Intermodal Transportation Integration
+    Autonomous Vehicle Regulations
+
+Category: Housing
+
+Tags in Housing
+  
+    Affordable Housing Development
+    Homelessness Prevention Programs
+    Rent Control Measures
+    Fair Housing Enforcement
+    Housing Discrimination Prevention
+    Housing Voucher Program Expansion
+    Sustainable Housing Initiatives
+    Urban Redevelopment Plans
+    Eviction Moratoriums
+    Homeownership Support Programs
+
+Category: Civil Rights
+
+Tags in Civil Rights
+  
+    Anti-Discrimination Laws
+    Gender Equality Protections
+    LGBTQ+ Rights Advocacy
+    Disability Rights Enforcement
+    Indigenous Peoples' Rights
+    Religious Freedom Protection
+    Language Access Policies
+    Voting Rights Expansion
+    Equal Pay Legislation
+    Minority Rights Safeguards
+
+Category: Labor and Employment
+
+Tags in Labor and Employment
+  
+    Minimum Wage Increase
+    Occupational Safety Regulations
+    Labor Union Protections
+    Workplace Harassment Prevention
+    Job Training and Apprenticeships
+    Workforce Diversity Initiatives
+    Employee Benefits Expansion
+    Fair Working Hours Regulations
+    Unemployment Benefits Enhancement
+    Remote Work Policies
+
+Category: Budget and Appropriations
+
+Tags in Budget and Appropriations
+  
+    Government Spending Oversight
+    Emergency Fund Allocation
+    Public Debt Management
+    Fiscal Responsibility Audits
+    Public Infrastructure Investment
+    Social Welfare Program Funding
+    Pension Plan Reform
+    Local Government Grant Programs
+    Financial Aid for Disadvantaged Communities
+    Tax Revenue Allocation
+
+Category: Public Safety
+
+Tags in Public Safety
+  
+    Emergency Response Planning
+    Disaster Preparedness Training
+    Cybersecurity Protocols
+    Domestic Violence Prevention
+    Fire Safety Regulations
+    Gun Control Measures
+    Community Health and Safety Programs
+    Public Health Crisis Management
+    Hate Crime Reporting Systems
+    Crime Prevention Initiatives
+
+Category: Technology and Innovation
+
+Tags in Technology and Innovation
+  
+    Digital Privacy Laws
+    Data Security Measures
+    Innovation Investment Policies
+    Broadband Infrastructure Expansion
+    E-Government Service Enhancements
+    Technology Education in Schools
+    Artificial Intelligence Regulations
+    Privacy Protection for Biometric Data
+    Blockchain Integration Strategies
+    Open Data Initiatives
+
+Category: Immigration
+
+Tags in Immigration
+  
+    Immigration Policy Reform
+    Refugee Resettlement Programs
+    Asylum Seeker Protections
+    Migrant Worker Rights
+    Language Access Services
+    Family Reunification Initiatives
+    Pathways to Citizenship
+    Humanitarian Aid for Migrants
+    Border Security Measures
+    Anti-Trafficking Efforts
+
+Category: Economic Development
+
+Tags in Economic Development
+  
+    Small Business Support Programs
+    Export Promotion Policies
+    Rural Development Initiatives
+    Entrepreneurship Training
+    Trade Agreement Negotiations
+    Tourism Industry Growth
+    Regional Economic Integration
+    Financial Inclusion Programs
+    Economic Diversification Strategies
+    Infrastructure Investment Plans
+
+Category: Social Services
+
+Tags in Social Services
+  
+    Child Welfare Services
+    Domestic Violence Support
+    Elderly Care Programs
+    Foster Care System Reforms
+    Disability Assistance Programs
+    Community Support Services
+    Youth Mentorship Programs
+    Substance Abuse Rehabilitation
+    Home Care for the Disabled
+    Affordable Childcare Services

Tagging/prompts.md

@@ -0,0 +1,7 @@
+## Prompt 1
+prompt_template = """Use the context below to assign a category that is relevant.
+After assigning a category, I want you to assign three tags in the chosen category that are relevant to the context
+
+Context: {context}
+info: {tagging}
+"""

Tagging/readme.md

@@ -0,0 +1 @@
+List of files that are classifying taggings.

Tagging/tagging_testing.ipynb

@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting tiktoken\n",
+      "  Obtaining dependency information for tiktoken from https://files.pythonhosted.org/packages/b8/eb/234646d9eefda8a500d0fd88b05bf625a90ed18054124349db26e558276e/tiktoken-0.5.1-cp311-cp311-win_amd64.whl.metadata\n",
+      "  Downloading tiktoken-0.5.1-cp311-cp311-win_amd64.whl.metadata (6.8 kB)\n",
+      "Collecting regex>=2022.1.18 (from tiktoken)\n",
+      "  Obtaining dependency information for regex>=2022.1.18 from https://files.pythonhosted.org/packages/b8/ad/3398312096118c4e62a5827664e52a04d5068e84d04142dd4a0da8a567ae/regex-2023.10.3-cp311-cp311-win_amd64.whl.metadata\n",
+      "  Downloading regex-2023.10.3-cp311-cp311-win_amd64.whl.metadata (41 kB)\n",
+      "     ---------------------------------------- 0.0/42.0 kB ? eta -:--:--\n",
+      "     ------------------ ------------------- 20.5/42.0 kB 320.0 kB/s eta 0:00:01\n",
+      "     -------------------------------------- 42.0/42.0 kB 503.7 kB/s eta 0:00:00\n",
+      "Requirement already satisfied: requests>=2.26.0 in c:\\users\\16178\\anaconda3\\envs\\llama\\lib\\site-packages (from tiktoken) (2.31.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\16178\\anaconda3\\envs\\llama\\lib\\site-packages (from requests>=2.26.0->tiktoken) (2.0.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\16178\\anaconda3\\envs\\llama\\lib\\site-packages (from requests>=2.26.0->tiktoken) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\16178\\anaconda3\\envs\\llama\\lib\\site-packages (from requests>=2.26.0->tiktoken) (1.26.16)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\16178\\anaconda3\\envs\\llama\\lib\\site-packages (from requests>=2.26.0->tiktoken) (2023.7.22)\n",
+      "Downloading tiktoken-0.5.1-cp311-cp311-win_amd64.whl (759 kB)\n",
+      "   ---------------------------------------- 0.0/759.8 kB ? eta -:--:--\n",
+      "   --- ------------------------------------ 61.4/759.8 kB 1.7 MB/s eta 0:00:01\n",
+      "   ----------------- ---------------------- 327.7/759.8 kB 4.1 MB/s eta 0:00:01\n",
+      "   ------------------------------------ --- 686.1/759.8 kB 5.4 MB/s eta 0:00:01\n",
+      "   ---------------------------------------- 759.8/759.8 kB 5.4 MB/s eta 0:00:00\n",
+      "Downloading regex-2023.10.3-cp311-cp311-win_amd64.whl (269 kB)\n",
+      "   ---------------------------------------- 0.0/269.6 kB ? eta -:--:--\n",
+      "   ------------------------------------ --- 245.8/269.6 kB 7.4 MB/s eta 0:00:01\n",
+      "   ---------------------------------------- 269.6/269.6 kB 4.1 MB/s eta 0:00:00\n",
+      "Installing collected packages: regex, tiktoken\n",
+      "Successfully installed regex-2023.10.3 tiktoken-0.5.1\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install tiktoken chromadb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you need openai key\n",
+    "import os\n",
+    "os.environ['OPENAI_API_KEY'] = ''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.callbacks import get_openai_callback\n",
+    "from langchain.schema import StrOutputParser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category = \"\"\"\n",
+    "[Environmental, Education, Health Care, Criminal Justice, Taxation, Transportation, \n",
+    "Housing, Civil Rights, Labor and Employment, Budget and Appropriations, Public Safety, Technology and Innovation, \n",
+    "Immigration, Economic Development, Social Services]\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tagging = \"\"\"\n",
+    "[Climate Change Mitigation,\n",
+    "Renewable Energy Initiatives,\n",
+    "Biodiversity Conservation,\n",
+    "Water Pollution Control,\n",
+    "Sustainable Agriculture Practices,\n",
+    "Air Quality Improvement,\n",
+    "Waste Reduction Programs,\n",
+    "Coastal Erosion Management,\n",
+    "Environmental Impact Assessments,\n",
+    "Wildlife Habitat Protection,\n",
+    "Curriculum Development,\n",
+    "Digital Learning Resources,\n",
+    "Teacher Professional Development,\n",
+    "Early Childhood Education,\n",
+    "Special Education Services,\n",
+    "Literacy Programs,\n",
+    "Vocational Training,\n",
+    "Education Technology Integration,\n",
+    "School Infrastructure Upgrades,\n",
+    "Student Mental Health Support,\n",
+    "Universal Health Coverage,\n",
+    "Healthcare Access for All,\n",
+    "Mental Health Services Expansion,\n",
+    "Disease Prevention Programs,\n",
+    "Elderly Care Services,\n",
+    "Healthcare Quality Standards,\n",
+    "Health Information Technology,\n",
+    "Maternal and Child Health,\n",
+    "Public Health Emergency Preparedness,\n",
+    "Healthcare Workforce Training,\n",
+    "Police Reforms\n",
+    "Prisoner Rehabilitation\n",
+    "Community Policing Initiatives\n",
+    "Restorative Justice Programs\n",
+    "Criminal Sentencing Reform\n",
+    "Victim Support Services\n",
+    "Legal Aid for the Underprivileged\n",
+    "Juvenile Justice System Overhaul\n",
+    "Hate Crime Prevention Measures\n",
+    "Court System Modernization,\n",
+    "Progressive Tax Reform\n",
+    "Corporate Taxation Policies\n",
+    "Income Tax Deductions\n",
+    "Property Tax Relief\n",
+    "Sales Tax Revision\n",
+    "Tax Compliance Regulations\n",
+    "Tax Transparency Measures\n",
+    "Wealth Redistribution Initiatives\n",
+    "Small Business Tax Breaks\n",
+    "Tax Fraud Prevention Measures,\n",
+    "Public Transit Expansion\n",
+    "Road Infrastructure Maintenance\n",
+    "Bike and Pedestrian Path Development\n",
+    "Traffic Congestion Reduction\n",
+    "Electric Vehicle Adoption Incentives\n",
+    "Freight Transportation Efficiency\n",
+    "Aviation Safety Regulations\n",
+    "Railroad Infrastructure Modernization\n",
+    "Intermodal Transportation Integration\n",
+    "Autonomous Vehicle Regulations,\n",
+    "Affordable Housing Development\n",
+    "Homelessness Prevention Programs\n",
+    "Rent Control Measures\n",
+    "Fair Housing Enforcement\n",
+    "Housing Discrimination Prevention\n",
+    "Housing Voucher Program Expansion\n",
+    "Sustainable Housing Initiatives\n",
+    "Urban Redevelopment Plans\n",
+    "Eviction Moratoriums\n",
+    "Homeownership Support Programs,\n",
+    "Anti-Discrimination Laws\n",
+    "Gender Equality Protections\n",
+    "LGBTQ+ Rights Advocacy\n",
+    "Disability Rights Enforcement\n",
+    "Indigenous Peoples' Rights\n",
+    "Religious Freedom Protection\n",
+    "Language Access Policies\n",
+    "Voting Rights Expansion\n",
+    "Equal Pay Legislation\n",
+    "Minority Rights Safeguards,\n",
+    "Minimum Wage Increase\n",
+    "Occupational Safety Regulations\n",
+    "Labor Union Protections\n",
+    "Workplace Harassment Prevention\n",
+    "Job Training and Apprenticeships\n",
+    "Workforce Diversity Initiatives\n",
+    "Employee Benefits Expansion\n",
+    "Fair Working Hours Regulations\n",
+    "Unemployment Benefits Enhancement\n",
+    "Remote Work Policies,\n",
+    "Government Spending Oversight\n",
+    "Emergency Fund Allocation\n",
+    "Public Debt Management\n",
+    "Fiscal Responsibility Audits\n",
+    "Public Infrastructure Investment\n",
+    "Social Welfare Program Funding\n",
+    "Pension Plan Reform\n",
+    "Local Government Grant Programs\n",
+    "Financial Aid for Disadvantaged Communities\n",
+    "Tax Revenue Allocation,\n",
+    "Emergency Response Planning\n",
+    "Disaster Preparedness Training\n",
+    "Cybersecurity Protocols\n",
+    "Domestic Violence Prevention\n",
+    "Fire Safety Regulations\n",
+    "Gun Control Measures\n",
+    "Community Health and Safety Programs\n",
+    "Public Health Crisis Management\n",
+    "Hate Crime Reporting Systems\n",
+    "Crime Prevention Initiatives,\n",
+    "Digital Privacy Laws\n",
+    "Data Security Measures\n",
+    "Innovation Investment Policies\n",
+    "Broadband Infrastructure Expansion\n",
+    "E-Government Service Enhancements\n",
+    "Technology Education in Schools\n",
+    "Artificial Intelligence Regulations\n",
+    "Privacy Protection for Biometric Data\n",
+    "Blockchain Integration Strategies\n",
+    "Open Data Initiatives,\n",
+    "Immigration Policy Reform\n",
+    "Refugee Resettlement Programs\n",
+    "Asylum Seeker Protections\n",
+    "Migrant Worker Rights\n",
+    "Language Access Services\n",
+    "Family Reunification Initiatives\n",
+    "Pathways to Citizenship\n",
+    "Humanitarian Aid for Migrants\n",
+    "Border Security Measures\n",
+    "Anti-Trafficking Efforts,\n",
+    "Small Business Support Programs\n",
+    "Export Promotion Policies\n",
+    "Rural Development Initiatives\n",
+    "Entrepreneurship Training\n",
+    "Trade Agreement Negotiations\n",
+    "Tourism Industry Growth\n",
+    "Regional Economic Integration\n",
+    "Financial Inclusion Programs\n",
+    "Economic Diversification Strategies\n",
+    "Infrastructure Investment Plans,\n",
+    "Child Welfare Services\n",
+    "Domestic Violence Support\n",
+    "Elderly Care Programs\n",
+    "Foster Care System Reforms\n",
+    "Disability Assistance Programs\n",
+    "Community Support Services\n",
+    "Youth Mentorship Programs\n",
+    "Substance Abuse Rehabilitation\n",
+    "Home Care for the Disabled\n",
+    "Affordable Childcare Services\n",
+    "]\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# LLM\n",
+    "prompt_template = \"\"\"Use the context below to assign a category that is relevant to the category, which is delimited by ####\n",
+    "Also, I want you to use the tagging, which is delimited by ****, and give top 3 tags that fit the context.\n",
+    "\n",
+    "Context: {context}\n",
+    "Category: {category}\n",
+    "tagging: {tags}\n",
+    "Do not make up false information\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Tokens: 1069\n",
+      "Prompt Tokens: 1040\n",
+      "Completion Tokens: 29\n",
+      "Total Cost (USD): $0.0016180000000000003\n",
+      "\n",
+      "SECTION 1. Chapter 20 Section 23 of the General Laws, as appearing in the 2020 Official Edition, is hereby amended by inserting the following after section (f): -\n",
+      "\t(g) Notwithstanding any general or special law to the contrary, the department of agricultural resources, \n",
+      "    with the approval of the co-holder, if any, in its sole discretion, may allow for storm water mitigation or nitrogen mitigation purposes, \n",
+      "    in nitrogen sensitive embayment watersheds, uncultivated, planted or other storm water mitigation infrastructure within 200 feet of any resource area in any parcel \n",
+      "    that has been accepted into the Massachusetts Agricultural Preservation Restriction program.\n",
+      "Category: Environmental\n",
+      "\n",
+      "Top 3 tags: \n",
+      "1. Water Pollution Control\n",
+      "2. Sustainable Agriculture Practices\n",
+      "3. Coastal Erosion Management\n"
+     ]
+    }
+   ],
+   "source": [
+    "info = \"\"\"\n",
+    "SECTION 1. Chapter 20 Section 23 of the General Laws, as appearing in the 2020 Official Edition, is hereby amended by inserting the following after section (f): -\n",
+    "\t(g) Notwithstanding any general or special law to the contrary, the department of agricultural resources, \n",
+    "    with the approval of the co-holder, if any, in its sole discretion, may allow for storm water mitigation or nitrogen mitigation purposes, \n",
+    "    in nitrogen sensitive embayment watersheds, uncultivated, planted or other storm water mitigation infrastructure within 200 feet of any resource area in any parcel \n",
+    "    that has been accepted into the Massachusetts Agricultural Preservation Restriction program.\"\"\"\n",
+    "\n",
+    "\n",
+    "PROMPT = PromptTemplate(template=prompt_template, input_variables=[\"context\", \"category\", 'tags'])\n",
+    "\n",
+    "with get_openai_callback() as cb:\n",
+    "    llm = LLMChain(\n",
+    "            llm = ChatOpenAI(openai_api_key='',\n",
+    "                     temperature=0.01, model=\"gpt-3.5-turbo\"), prompt=PROMPT)\n",
+    "        \n",
+    "    response = llm.predict(context=info, category=category, tags=tagging)\n",
+    "    print(f\"Total Tokens: {cb.total_tokens}\")\n",
+    "    print(f\"Prompt Tokens: {cb.prompt_tokens}\")\n",
+    "    print(f\"Completion Tokens: {cb.completion_tokens}\")\n",
+    "    print(f\"Total Cost (USD): ${cb.total_cost}\")\n",
+    "    \n",
+    "print(info)\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llama",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

demoapp/MGL_extract.ipynb

@@ -0,0 +1,827 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>BillNumber</th>\n",
+       "      <th>DocketNumber</th>\n",
+       "      <th>GeneralCourtNumber</th>\n",
+       "      <th>PrimarySponsor</th>\n",
+       "      <th>Cosponsors</th>\n",
+       "      <th>JointSponsor</th>\n",
+       "      <th>BillHistory</th>\n",
+       "      <th>LegislationTypeName</th>\n",
+       "      <th>Pinslip</th>\n",
+       "      <th>DocumentText</th>\n",
+       "      <th>EmergencyPreamble</th>\n",
+       "      <th>RollCalls</th>\n",
+       "      <th>Attachments</th>\n",
+       "      <th>CommitteeRecommendations</th>\n",
+       "      <th>Amendments</th>\n",
+       "      <th>List References</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2299</td>\n",
+       "      <td>An Act relative to collective bargaining right...</td>\n",
+       "      <td>H3069</td>\n",
+       "      <td>HD2435</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>{'Id': 'PJK1', 'Name': 'Patrick Joseph Kearney...</td>\n",
+       "      <td>[{'Id': 'PJK1', 'Name': 'Patrick Joseph Kearne...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://malegislature.gov/api/GeneralCourts/193...</td>\n",
+       "      <td>Bill</td>\n",
+       "      <td>By Representative Kearney of Scituate, a petit...</td>\n",
+       "      <td>\\tSECTION 1. Section 1 of Chapter 150E of the ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[['1', '150E'], ['23', '268A']]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2358</td>\n",
+       "      <td>An Act relative to the open meeting law</td>\n",
+       "      <td>H3121</td>\n",
+       "      <td>HD2204</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>{'Id': 'E_U1', 'Name': 'Erika Uyterhoeven', 'T...</td>\n",
+       "      <td>[{'Id': 'E_U1', 'Name': 'Erika Uyterhoeven', '...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>http://malegislature.gov/api/GeneralCourts/193...</td>\n",
+       "      <td>Bill</td>\n",
+       "      <td>By Representative Uyterhoeven of Somerville, a...</td>\n",
+       "      <td>\\tSECTION 18 of chapter 30A of the General Law...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[['18', '30A']]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0                                              Title BillNumber  \\\n",
+       "0        2299  An Act relative to collective bargaining right...      H3069   \n",
+       "1        2358            An Act relative to the open meeting law      H3121   \n",
+       "\n",
+       "  DocketNumber  GeneralCourtNumber  \\\n",
+       "0       HD2435               193.0   \n",
+       "1       HD2204               193.0   \n",
+       "\n",
+       "                                      PrimarySponsor  \\\n",
+       "0  {'Id': 'PJK1', 'Name': 'Patrick Joseph Kearney...   \n",
+       "1  {'Id': 'E_U1', 'Name': 'Erika Uyterhoeven', 'T...   \n",
+       "\n",
+       "                                          Cosponsors JointSponsor  \\\n",
+       "0  [{'Id': 'PJK1', 'Name': 'Patrick Joseph Kearne...          NaN   \n",
+       "1  [{'Id': 'E_U1', 'Name': 'Erika Uyterhoeven', '...          NaN   \n",
+       "\n",
+       "                                         BillHistory LegislationTypeName  \\\n",
+       "0  http://malegislature.gov/api/GeneralCourts/193...                Bill   \n",
+       "1  http://malegislature.gov/api/GeneralCourts/193...                Bill   \n",
+       "\n",
+       "                                             Pinslip  \\\n",
+       "0  By Representative Kearney of Scituate, a petit...   \n",
+       "1  By Representative Uyterhoeven of Somerville, a...   \n",
+       "\n",
+       "                                        DocumentText  EmergencyPreamble  \\\n",
+       "0  \\tSECTION 1. Section 1 of Chapter 150E of the ...                NaN   \n",
+       "1  \\tSECTION 18 of chapter 30A of the General Law...                NaN   \n",
+       "\n",
+       "  RollCalls Attachments CommitteeRecommendations Amendments  \\\n",
+       "0        []          []                       []         []   \n",
+       "1        []          []                       []         []   \n",
+       "\n",
+       "                   List References  \n",
+       "0  [['1', '150E'], ['23', '268A']]  \n",
+       "1                  [['18', '30A']]  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"list_regex_format_12bills.csv\")\n",
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "List References: section number and chapter format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                       [['1', '150E'], ['23', '268A']]\n",
+       "1                                       [['18', '30A']]\n",
+       "2                                                   NaN\n",
+       "3     [['2', '15D'], ['2', '18B'], ['11', '15D'], ['...\n",
+       "4                                       [['10', '70B']]\n",
+       "5                                        [['77', '54']]\n",
+       "6                          [['1', '125'], ['73', '7C']]\n",
+       "7                       [['1', '150E'], ['23', '268A']]\n",
+       "8                [['7', '4'], ['7', '4'], ['10', '66']]\n",
+       "9                                                   NaN\n",
+       "10    [['2', '15D'], ['2', '18B'], ['11', '15D'], ['...\n",
+       "11                                       [['77', '54']]\n",
+       "Name: List References, dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['List References'] "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "List References: section number and chapter format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>BillNumber</th>\n",
+       "      <th>DocketNumber</th>\n",
+       "      <th>DocumentText</th>\n",
+       "      <th>List References</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>An Act relative to collective bargaining right...</td>\n",
+       "      <td>H3069</td>\n",
+       "      <td>HD2435</td>\n",
+       "      <td>\\tSECTION 1. Section 1 of Chapter 150E of the ...</td>\n",
+       "      <td>[['1', '150E'], ['23', '268A']]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>An Act relative to the open meeting law</td>\n",
+       "      <td>H3121</td>\n",
+       "      <td>HD2204</td>\n",
+       "      <td>\\tSECTION 18 of chapter 30A of the General Law...</td>\n",
+       "      <td>[['18', '30A']]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>An Act relative to vehicle recalls</td>\n",
+       "      <td>H400</td>\n",
+       "      <td>HD1240</td>\n",
+       "      <td>\\tSECTION 1. Chapter 90 of the General Laws, a...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>An Act providing affordable and accessible hig...</td>\n",
+       "      <td>H489</td>\n",
+       "      <td>HD2794</td>\n",
+       "      <td>\\tSECTION 1. The seventh paragraph of section ...</td>\n",
+       "      <td>[['2', '15D'], ['2', '18B'], ['11', '15D'], ['...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>An Act to Improve access, opportunity, and cap...</td>\n",
+       "      <td>H538</td>\n",
+       "      <td>HD485</td>\n",
+       "      <td>\\tSECTION 1. To create a new capital infrastru...</td>\n",
+       "      <td>[['10', '70B']]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>An Act providing a local option for ranked cho...</td>\n",
+       "      <td>H711</td>\n",
+       "      <td>HD1183</td>\n",
+       "      <td>\\tSECTION 1. Section 44A of chapter 43 of the ...</td>\n",
+       "      <td>[['77', '54']]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>An Act establishing a jail and prison construc...</td>\n",
+       "      <td>S1979</td>\n",
+       "      <td>SD661</td>\n",
+       "      <td>\\tSECTION 1. Chapter 7C of the General Laws is...</td>\n",
+       "      <td>[['1', '125'], ['73', '7C']]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>An Act relative to collective bargaining right...</td>\n",
+       "      <td>S2014</td>\n",
+       "      <td>SD1208</td>\n",
+       "      <td>\\tSECTION 1. Section 1 of chapter 150E of the ...</td>\n",
+       "      <td>[['1', '150E'], ['23', '268A']]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>An Act extending the public records law to the...</td>\n",
+       "      <td>S2064</td>\n",
+       "      <td>SD390</td>\n",
+       "      <td>\\tSECTION 1. Section 7 of chapter 4 of the Gen...</td>\n",
+       "      <td>[['7', '4'], ['7', '4'], ['10', '66']]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>An Act to end discriminatory outcomes in vocat...</td>\n",
+       "      <td>S257</td>\n",
+       "      <td>SD2312</td>\n",
+       "      <td>\\tSECTION 1. Chapter 74 of the General Laws is...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>An Act providing affordable and accessible hig...</td>\n",
+       "      <td>S301</td>\n",
+       "      <td>SD667</td>\n",
+       "      <td>\\tSECTION 1. The seventh paragraph of section ...</td>\n",
+       "      <td>[['2', '15D'], ['2', '18B'], ['11', '15D'], ['...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>An Act providing a local option for ranked cho...</td>\n",
+       "      <td>S433</td>\n",
+       "      <td>SD391</td>\n",
+       "      <td>\\tSECTION 1. Section 44A of chapter 43 of the ...</td>\n",
+       "      <td>[['77', '54']]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                Title BillNumber DocketNumber  \\\n",
+       "0   An Act relative to collective bargaining right...      H3069       HD2435   \n",
+       "1             An Act relative to the open meeting law      H3121       HD2204   \n",
+       "2                  An Act relative to vehicle recalls       H400       HD1240   \n",
+       "3   An Act providing affordable and accessible hig...       H489       HD2794   \n",
+       "4   An Act to Improve access, opportunity, and cap...       H538        HD485   \n",
+       "5   An Act providing a local option for ranked cho...       H711       HD1183   \n",
+       "6   An Act establishing a jail and prison construc...      S1979        SD661   \n",
+       "7   An Act relative to collective bargaining right...      S2014       SD1208   \n",
+       "8   An Act extending the public records law to the...      S2064        SD390   \n",
+       "9   An Act to end discriminatory outcomes in vocat...       S257       SD2312   \n",
+       "10  An Act providing affordable and accessible hig...       S301        SD667   \n",
+       "11  An Act providing a local option for ranked cho...       S433        SD391   \n",
+       "\n",
+       "                                         DocumentText  \\\n",
+       "0   \\tSECTION 1. Section 1 of Chapter 150E of the ...   \n",
+       "1   \\tSECTION 18 of chapter 30A of the General Law...   \n",
+       "2   \\tSECTION 1. Chapter 90 of the General Laws, a...   \n",
+       "3   \\tSECTION 1. The seventh paragraph of section ...   \n",
+       "4   \\tSECTION 1. To create a new capital infrastru...   \n",
+       "5   \\tSECTION 1. Section 44A of chapter 43 of the ...   \n",
+       "6   \\tSECTION 1. Chapter 7C of the General Laws is...   \n",
+       "7   \\tSECTION 1. Section 1 of chapter 150E of the ...   \n",
+       "8   \\tSECTION 1. Section 7 of chapter 4 of the Gen...   \n",
+       "9   \\tSECTION 1. Chapter 74 of the General Laws is...   \n",
+       "10  \\tSECTION 1. The seventh paragraph of section ...   \n",
+       "11  \\tSECTION 1. Section 44A of chapter 43 of the ...   \n",
+       "\n",
+       "                                      List References  \n",
+       "0                     [['1', '150E'], ['23', '268A']]  \n",
+       "1                                     [['18', '30A']]  \n",
+       "2                                                 NaN  \n",
+       "3   [['2', '15D'], ['2', '18B'], ['11', '15D'], ['...  \n",
+       "4                                     [['10', '70B']]  \n",
+       "5                                      [['77', '54']]  \n",
+       "6                        [['1', '125'], ['73', '7C']]  \n",
+       "7                     [['1', '150E'], ['23', '268A']]  \n",
+       "8              [['7', '4'], ['7', '4'], ['10', '66']]  \n",
+       "9                                                 NaN  \n",
+       "10  [['2', '15D'], ['2', '18B'], ['11', '15D'], ['...  \n",
+       "11                                     [['77', '54']]  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_cleaned = df[['Title', 'BillNumber', 'DocketNumber', 'DocumentText', 'List References']]\n",
+    "df_cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ast \n",
+    "\n",
+    "new_df = df_cleaned.copy()\n",
+    "\n",
+    "new_df['List References'] = df_cleaned['List References'].apply(lambda x: \"None\" if pd.isna(x) else ast.literal_eval(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                       [[1, 150E], [23, 268A]]\n",
+       "1                                   [[18, 30A]]\n",
+       "2                                          None\n",
+       "3     [[2, 15D], [2, 18B], [11, 15D], [19, 66]]\n",
+       "4                                   [[10, 70B]]\n",
+       "5                                    [[77, 54]]\n",
+       "6                          [[1, 125], [73, 7C]]\n",
+       "7                       [[1, 150E], [23, 268A]]\n",
+       "8                    [[7, 4], [7, 4], [10, 66]]\n",
+       "9                                          None\n",
+       "10    [[2, 15D], [2, 18B], [11, 15D], [19, 66]]\n",
+       "11                                   [[77, 54]]\n",
+       "Name: List References, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df['List References']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['2', '15D'], ['2', '18B'], ['11', '15D'], ['19', '66']]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df['List References'].values[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def make_api_call(item):\n",
+    "    result = \"\"\"\"\"\"\n",
+    "    try:\n",
+    "        # unpack section and chapter for example: ['2', '15D']\n",
+    "        section, chapter = item\n",
+    "        r = requests.get(f'https://malegislature.gov/api/Chapters/{chapter}/Sections/{section}', verify=False)\n",
+    "        r = r.json()\n",
+    "\n",
+    "        # fields to extract\n",
+    "        result =  r.get(\"Text\", np.nan)\n",
+    "        return result\n",
+    "    except:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Section 2. There shall be in the executive office of education a department of early education and care, in this chapter called the department, which shall be the state agency responsible for compliance with early education and care services under the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (Pub. L. 104&ndash;193), or any successor federal statute. The department shall be the state education agency for the purposes of early education and care services under federal law. The department shall seek, apply for and encourage the use of any federal funds for early education and care services, and shall facilitate the coordination of federal, state, and local policies concerning early education and care. The department shall be under the supervision and management of the commissioner of early education and care, in this chapter called the commissioner.  \n",
+      "\n",
+      "The department shall:&mdash;  \n",
+      "\n",
+      "(a) be the lead agency of the commonwealth for administering and providing early education and care programs and services to children;  \n",
+      "\n",
+      "(b) provide early education and care programs and mental health consultation and other support services for children in the commonwealth through grants, contracting for those programs and services, and providing vouchers to participants, and promote the coordination of all such programs and services;  \n",
+      "\n",
+      "(c) license or approve child care centers, school-aged child care programs, family child care homes and large family child care homes, family foster care which is not supervised and approved by a placement agency, placement agencies, group care facilities, or temporary shelter facilities;  \n",
+      "\n",
+      "(d) develop and maintain a current consolidated waiting list for all subsidized early education and care programs, and services in the commonwealth;  \n",
+      "\n",
+      "(e) establish and develop a schedule for revising: (1) a rate structure for voucher and contracted payments to providers of subsidized early education and care programs and services on behalf of low-income and other at-risk children; and (2) a sliding fee scale for participants in those programs. A public hearing under chapter 30A and the approval of the board shall be required before the establishment or revision of the rate structure and sliding fee scale;  \n",
+      "\n",
+      "(f) manage and implement the Massachusetts universal pre-kindergarten program, established in section 13, that may be phased in over a period of time as determined by the board, and ensure the universal accessibility to the program by using the sliding fee scale developed for early education and care programs;  \n",
+      "\n",
+      "(g) after a public hearing, adopt criteria including income eligibility requirements, for determining eligibility for an early education and care program or service, including the universal pre-kindergarten program under this chapter and develop a schedule for revising such criteria. Income eligibility requirements shall include a maximum allowable income for working families;  \n",
+      "\n",
+      "(h) monitor and evaluate on an ongoing basis all early education and care programs and services, including program outcomes in meeting the developmental and educational needs of all children;  \n",
+      "\n",
+      "(i) analyze and evaluate all budget requests for early education and care programs and services, including requests from secretaries, departments, agencies, or other offices within the commonwealth and make recommendations to the secretary of education, general court, appropriate secretaries, departments, agencies, or other offices regarding coordination and approval of those budget requests;  \n",
+      "\n",
+      "(j) lease, purchase, hold and dispose of personal and real property it considers necessary to carry out this chapter;  \n",
+      "\n",
+      "(k) seek to increase the availability of early education and care programs and services and encourage all providers of those programs and services to work together to create an array of options allowing families to select programs that fit with their schedules;  \n",
+      "\n",
+      "(l) provide information and referral to persons seeking early education and care programs and services;  \n",
+      "\n",
+      "(m) work in conjunction with the department of transitional assistance to obtain federal reimbursement under the federal Social Security Act for all participants in publicly-funded early education and care programs and services who are eligible;  \n",
+      "\n",
+      "(n) promote the development of early education and care services for children by seeking and accepting federal grants as well as assisting other agencies of the commonwealth and local agencies to take full advantage of all federal funds available for those services;  \n",
+      "\n",
+      "(o) provide technical assistance and consultation to providers and potential providers of early education and care services;  \n",
+      "\n",
+      "(p) facilitate the development of the early education and care workforce, and, when appropriate, provide for training programs and professional development for persons offering early education and care programs and services;  \n",
+      "\n",
+      "(q) establish and regularly update: (1) a comprehensive database of early childhood educators and providers, hereinafter referred to as the educator database, for the purpose of enhancing the workforce development system; and (2) a comprehensive database of children both waiting for and receiving early education and care services, in this chapter called the student database, that is compatible with relevant databases at the department of elementary and secondary education and the executive office of health and human services; and  \n",
+      "\n",
+      "(r) collect and disseminate information to assist parents in nurturing their children's development and education. This information shall be made widely available in written form and accessible through the department's website, in English and other commonly spoken languages in the commonwealth.  \n",
+      "\n",
+      "(s) plan for and address the unique needs of families with infants and toddlers, including providing parent education, early literacy services and meaningful opportunities for families not enrolled in early education and care to support their children's development.  \n",
+      "\n",
+      "(t) subject to appropriation, provide consultation services and workforce development to meet the behavioral health needs of children in early education and care programs, giving preference to those services designed to prevent expulsions and suspensions.  \n",
+      "\n",
+      "(u) subject to appropriation and notwithstanding any general or special law to the contrary, assure quality early education and care provided to children through family child care providers by working cooperatively with family child care providers to build upon the existing system and continuously improve the delivery of high quality early education and care services for eligible low income families through providers who have the requisite skills and training.   \n",
+      "\n",
+      "Section 2. The department shall provide and administer a comprehensive child welfare program for children and families, including the following services:&mdash;  \n",
+      "\n",
+      "(1) casework or counseling, including services to families, foster families or individuals;  \n",
+      "\n",
+      "(2) protective services for children;  \n",
+      "\n",
+      "(3) legal services for families, children or individuals who are clients of the department;  \n",
+      "\n",
+      "(4) adoption services;  \n",
+      "\n",
+      "(5) information and referral services;  \n",
+      "\n",
+      "(6) foster family care for children and specialized foster family care for children with special needs;  \n",
+      "\n",
+      "(7) residential care for children with special needs who are not suited for foster family care or specialized foster family care;  \n",
+      "\n",
+      "(8) informal education and group activities;  \n",
+      "\n",
+      "(9) training in parenthood and home management for parents, foster parents and prospective parents;  \n",
+      "\n",
+      "(10) family services intended to prevent the need for foster care and services to children in foster care;  \n",
+      "\n",
+      "(11) temporary residential programs providing counseling and supportive assistance for families in transition and their children who, because of domestic violence, homelessness, or other situations, require temporary shelter and assistance;  \n",
+      "\n",
+      "(12) camping services;  \n",
+      "\n",
+      "(13) information and referral services;  \n",
+      "\n",
+      "(14) services for families and individuals in emergency and transitional housing;  \n",
+      "\n",
+      "(15) comprehensive youth development services;  \n",
+      "\n",
+      "(16) access to and coordination of medical, dental and mental health services for children in foster care whose families are receiving services from other state agencies; and  \n",
+      "\n",
+      "(17) child care placements for children whose families have an open case with the department.   \n",
+      "\n",
+      "Section 11. The board, after holding a public hearing, shall adopt, and develop a schedule for revising, program quality standards and requirements that any early education and care program or provider shall meet. The department shall develop separate but related standards for children in the following developmental stages: infant and toddler, pre-school, early elementary and older school-age; and for additional developmental stages that the department may determine relevant based on research and best practices.  These standards and requirements shall incorporate essential elements of high-quality early education and care that promote healthy, cognitive, linguistic, social, emotional and physical outcomes, and school readiness based on curriculum frameworks. Standards and requirements shall build upon the licensure regulations promulgated under section 8.  \n",
+      "\n",
+      "(a) The standards for all developmental stages and any succeeding developmental stages adopted by the department shall be incorporated into a single document and linked by a common philosophy and consistent goals and guiding principles. This document shall also include the policy developed jointly with the department and board of elementary and secondary education required under section 13 to ensure smooth transitions between infant and toddler programs, preschool and kindergarten. The standards shall be regularly updated to reflect applicable research and best practices. The board shall submit standards and requirements, or revisions of them, to the joint committee on education and the house and senate committees on ways and means at least 60 days before adoption. The joint committee on education shall review and comment on the rules and regulations during that time period.  \n",
+      "\n",
+      "(b) The board shall include, in its adoption and revision of program quality standards, a specific focus on the unique requirements and needs of preschool-aged children including, but not limited to, rigorous guidelines for preschool learning experiences. Every early education and care program or provider shall be required to meet these standards in order to participate in the Massachusetts universal pre-kindergarten program, established in section 13.  \n",
+      "\n",
+      "(c) The department, with approval of the board, shall develop a consistent set of learning standards for all preschool programs in the commonwealth, to be included in the program quality standards. The standards shall be consistent with the curriculum frameworks developed by the department of elementary and secondary education, shall be research-based and shall be updated regularly to reflect best practices in the field of early education and care. The standards shall guarantee, at a minimum, that every program participating in the Massachusetts universal pre-kindergarten program shall include consultation and intervention services for children at risk for expulsion.  \n",
+      "\n",
+      "(d) With the approval of the board, the department shall develop and establish a schedule for revising a comprehensive set of developmental benchmarks which may be incorporated into the quality standards, and which may be used by all early education and care programs in measuring children's developmental progress utilizing the healthy, cognitive, linguistic, social, emotional and physical outcomes for developing the quality standards. In developing the benchmarks, the department shall collaborate with pediatricians, child psychologists, and researchers within the field of child development and developmental psychology, in order to ensure that the document reflects best practices in the field and the most recent evidence provided by science relative to early childhood development.   \n",
+      "\n",
+      "Section 19. (a) When designing or acquiring an electronic record keeping system or database, records access officers shall, consistent with section 17 of chapter 110G, consult with their chief executive officer, chief administrative officer or the Massachusetts office of information technology pursuant to chapter 7D to ensure, to the extent feasible, that the system or database is capable of providing data in a commonly available electronic, machine readable format. Such database designs or acquisitions shall allow for, to the extent feasible, information storage and retrieval methods that permit the segregation and retrieval of public records and redacting of exempt information in order to provide maximum public access. No agency or municipality shall enter into a contract for the storage of electronic records containing public records if the contract prevents or unduly restricts the records access officer from providing the public records in accordance with this chapter.  \n",
+      "\n",
+      "(b) Every agency shall provide on a searchable website electronic copies, accessible in a commonly available electronic format, of the following types of records, provided that any agency may withhold any record or portion thereof in accordance with state or federal law:  \n",
+      "\n",
+      "(i) final opinions, decisions, orders, or votes from agency proceedings;  \n",
+      "\n",
+      "(ii) annual reports;  \n",
+      "\n",
+      "(iii) notices of regulations proposed under chapter 30A;  \n",
+      "\n",
+      "(iv) notices of hearings;  \n",
+      "\n",
+      "(v) winning bids for public contracts;  \n",
+      "\n",
+      "(vi) awards of federal, state and municipal government grants;  \n",
+      "\n",
+      "(vii) minutes of open meetings;  \n",
+      "\n",
+      "(viii) agency budgets; and  \n",
+      "\n",
+      "(ix) any public record information of significant interest that the agency deems appropriate to post.   \n",
+      "\n",
+      "\n",
+      "14007\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample_1 = new_df['List References'].values[3]\n",
+    "\n",
+    "result = \"\"\"\"\"\"\n",
+    "# Storing and printing each pair\n",
+    "formatted_data = []\n",
+    "for pair in sample_1:\n",
+    "    string = make_api_call(pair)\n",
+    "    result += string\n",
+    "    \n",
+    "\n",
+    "print(result)\n",
+    "print(len(result))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "this is in the format of section, chapter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\16178\\anaconda3\\envs\\maple\\Lib\\site-packages\\urllib3\\connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'malegislature.gov'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_df['MGL'] = new_df['List References'].apply(lambda pairs: [make_api_call(item) for item in pairs if item is not None])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Title              An Act providing affordable and accessible hig...\n",
+       "BillNumber                                                      H489\n",
+       "DocketNumber                                                  HD2794\n",
+       "DocumentText       \\tSECTION 1. The seventh paragraph of section ...\n",
+       "List References            [[2, 15D], [2, 18B], [11, 15D], [19, 66]]\n",
+       "MGL                [Section 2. There shall be in the executive of...\n",
+       "Name: 3, dtype: object"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df.iloc[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[\"Section 2. There shall be in the executive office of education a department of early education and care, in this chapter called the department, which shall be the state agency responsible for compliance with early education and care services under the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (Pub. L. 104&ndash;193), or any successor federal statute. The department shall be the state education agency for the purposes of early education and care services under federal law. The department shall seek, apply for and encourage the use of any federal funds for early education and care services, and shall facilitate the coordination of federal, state, and local policies concerning early education and care. The department shall be under the supervision and management of the commissioner of early education and care, in this chapter called the commissioner.  \\r\\n\\r\\nThe department shall:&mdash;  \\r\\n\\r\\n(a) be the lead agency of the commonwealth for administering and providing early education and care programs and services to children;  \\r\\n\\r\\n(b) provide early education and care programs and mental health consultation and other support services for children in the commonwealth through grants, contracting for those programs and services, and providing vouchers to participants, and promote the coordination of all such programs and services;  \\r\\n\\r\\n(c) license or approve child care centers, school-aged child care programs, family child care homes and large family child care homes, family foster care which is not supervised and approved by a placement agency, placement agencies, group care facilities, or temporary shelter facilities;  \\r\\n\\r\\n(d) develop and maintain a current consolidated waiting list for all subsidized early education and care programs, and services in the commonwealth;  \\r\\n\\r\\n(e) establish and develop a schedule for revising: (1) a rate structure for voucher and contracted payments to providers of subsidized early education and care programs and services on behalf of low-income and other at-risk children; and (2) a sliding fee scale for participants in those programs. A public hearing under chapter 30A and the approval of the board shall be required before the establishment or revision of the rate structure and sliding fee scale;  \\r\\n\\r\\n(f) manage and implement the Massachusetts universal pre-kindergarten program, established in section 13, that may be phased in over a period of time as determined by the board, and ensure the universal accessibility to the program by using the sliding fee scale developed for early education and care programs;  \\r\\n\\r\\n(g) after a public hearing, adopt criteria including income eligibility requirements, for determining eligibility for an early education and care program or service, including the universal pre-kindergarten program under this chapter and develop a schedule for revising such criteria. Income eligibility requirements shall include a maximum allowable income for working families;  \\r\\n\\r\\n(h) monitor and evaluate on an ongoing basis all early education and care programs and services, including program outcomes in meeting the developmental and educational needs of all children;  \\r\\n\\r\\n(i) analyze and evaluate all budget requests for early education and care programs and services, including requests from secretaries, departments, agencies, or other offices within the commonwealth and make recommendations to the secretary of education, general court, appropriate secretaries, departments, agencies, or other offices regarding coordination and approval of those budget requests;  \\r\\n\\r\\n(j) lease, purchase, hold and dispose of personal and real property it considers necessary to carry out this chapter;  \\r\\n\\r\\n(k) seek to increase the availability of early education and care programs and services and encourage all providers of those programs and services to work together to create an array of options allowing families to select programs that fit with their schedules;  \\r\\n\\r\\n(l) provide information and referral to persons seeking early education and care programs and services;  \\r\\n\\r\\n(m) work in conjunction with the department of transitional assistance to obtain federal reimbursement under the federal Social Security Act for all participants in publicly-funded early education and care programs and services who are eligible;  \\r\\n\\r\\n(n) promote the development of early education and care services for children by seeking and accepting federal grants as well as assisting other agencies of the commonwealth and local agencies to take full advantage of all federal funds available for those services;  \\r\\n\\r\\n(o) provide technical assistance and consultation to providers and potential providers of early education and care services;  \\r\\n\\r\\n(p) facilitate the development of the early education and care workforce, and, when appropriate, provide for training programs and professional development for persons offering early education and care programs and services;  \\r\\n\\r\\n(q) establish and regularly update: (1) a comprehensive database of early childhood educators and providers, hereinafter referred to as the educator database, for the purpose of enhancing the workforce development system; and (2) a comprehensive database of children both waiting for and receiving early education and care services, in this chapter called the student database, that is compatible with relevant databases at the department of elementary and secondary education and the executive office of health and human services; and  \\r\\n\\r\\n(r) collect and disseminate information to assist parents in nurturing their children's development and education. This information shall be made widely available in written form and accessible through the department's website, in English and other commonly spoken languages in the commonwealth.  \\r\\n\\r\\n(s) plan for and address the unique needs of families with infants and toddlers, including providing parent education, early literacy services and meaningful opportunities for families not enrolled in early education and care to support their children's development.  \\r\\n\\r\\n(t) subject to appropriation, provide consultation services and workforce development to meet the behavioral health needs of children in early education and care programs, giving preference to those services designed to prevent expulsions and suspensions.  \\r\\n\\r\\n(u) subject to appropriation and notwithstanding any general or special law to the contrary, assure quality early education and care provided to children through family child care providers by working cooperatively with family child care providers to build upon the existing system and continuously improve the delivery of high quality early education and care services for eligible low income families through providers who have the requisite skills and training.   \\r\\n\\r\\n\",\n",
+       " 'Section 2. The department shall provide and administer a comprehensive child welfare program for children and families, including the following services:&mdash;  \\r\\n\\r\\n(1) casework or counseling, including services to families, foster families or individuals;  \\r\\n\\r\\n(2) protective services for children;  \\r\\n\\r\\n(3) legal services for families, children or individuals who are clients of the department;  \\r\\n\\r\\n(4) adoption services;  \\r\\n\\r\\n(5) information and referral services;  \\r\\n\\r\\n(6) foster family care for children and specialized foster family care for children with special needs;  \\r\\n\\r\\n(7) residential care for children with special needs who are not suited for foster family care or specialized foster family care;  \\r\\n\\r\\n(8) informal education and group activities;  \\r\\n\\r\\n(9) training in parenthood and home management for parents, foster parents and prospective parents;  \\r\\n\\r\\n(10) family services intended to prevent the need for foster care and services to children in foster care;  \\r\\n\\r\\n(11) temporary residential programs providing counseling and supportive assistance for families in transition and their children who, because of domestic violence, homelessness, or other situations, require temporary shelter and assistance;  \\r\\n\\r\\n(12) camping services;  \\r\\n\\r\\n(13) information and referral services;  \\r\\n\\r\\n(14) services for families and individuals in emergency and transitional housing;  \\r\\n\\r\\n(15) comprehensive youth development services;  \\r\\n\\r\\n(16) access to and coordination of medical, dental and mental health services for children in foster care whose families are receiving services from other state agencies; and  \\r\\n\\r\\n(17) child care placements for children whose families have an open case with the department.   \\r\\n\\r\\n',\n",
+       " \"Section 11. The board, after holding a public hearing, shall adopt, and develop a schedule for revising, program quality standards and requirements that any early education and care program or provider shall meet. The department shall develop separate but related standards for children in the following developmental stages: infant and toddler, pre-school, early elementary and older school-age; and for additional developmental stages that the department may determine relevant based on research and best practices.  These standards and requirements shall incorporate essential elements of high-quality early education and care that promote healthy, cognitive, linguistic, social, emotional and physical outcomes, and school readiness based on curriculum frameworks. Standards and requirements shall build upon the licensure regulations promulgated under section 8.  \\r\\n\\r\\n(a) The standards for all developmental stages and any succeeding developmental stages adopted by the department shall be incorporated into a single document and linked by a common philosophy and consistent goals and guiding principles. This document shall also include the policy developed jointly with the department and board of elementary and secondary education required under section 13 to ensure smooth transitions between infant and toddler programs, preschool and kindergarten. The standards shall be regularly updated to reflect applicable research and best practices. The board shall submit standards and requirements, or revisions of them, to the joint committee on education and the house and senate committees on ways and means at least 60 days before adoption. The joint committee on education shall review and comment on the rules and regulations during that time period.  \\r\\n\\r\\n(b) The board shall include, in its adoption and revision of program quality standards, a specific focus on the unique requirements and needs of preschool-aged children including, but not limited to, rigorous guidelines for preschool learning experiences. Every early education and care program or provider shall be required to meet these standards in order to participate in the Massachusetts universal pre-kindergarten program, established in section 13.  \\r\\n\\r\\n(c) The department, with approval of the board, shall develop a consistent set of learning standards for all preschool programs in the commonwealth, to be included in the program quality standards. The standards shall be consistent with the curriculum frameworks developed by the department of elementary and secondary education, shall be research-based and shall be updated regularly to reflect best practices in the field of early education and care. The standards shall guarantee, at a minimum, that every program participating in the Massachusetts universal pre-kindergarten program shall include consultation and intervention services for children at risk for expulsion.  \\r\\n\\r\\n(d) With the approval of the board, the department shall develop and establish a schedule for revising a comprehensive set of developmental benchmarks which may be incorporated into the quality standards, and which may be used by all early education and care programs in measuring children's developmental progress utilizing the healthy, cognitive, linguistic, social, emotional and physical outcomes for developing the quality standards. In developing the benchmarks, the department shall collaborate with pediatricians, child psychologists, and researchers within the field of child development and developmental psychology, in order to ensure that the document reflects best practices in the field and the most recent evidence provided by science relative to early childhood development.   \\r\\n\\r\\n\",\n",
+       " 'Section 19. (a) When designing or acquiring an electronic record keeping system or database, records access officers shall, consistent with section 17 of chapter 110G, consult with their chief executive officer, chief administrative officer or the Massachusetts office of information technology pursuant to chapter 7D to ensure, to the extent feasible, that the system or database is capable of providing data in a commonly available electronic, machine readable format. Such database designs or acquisitions shall allow for, to the extent feasible, information storage and retrieval methods that permit the segregation and retrieval of public records and redacting of exempt information in order to provide maximum public access. No agency or municipality shall enter into a contract for the storage of electronic records containing public records if the contract prevents or unduly restricts the records access officer from providing the public records in accordance with this chapter.  \\r\\n\\r\\n(b) Every agency shall provide on a searchable website electronic copies, accessible in a commonly available electronic format, of the following types of records, provided that any agency may withhold any record or portion thereof in accordance with state or federal law:  \\r\\n\\r\\n(i) final opinions, decisions, orders, or votes from agency proceedings;  \\r\\n\\r\\n(ii) annual reports;  \\r\\n\\r\\n(iii) notices of regulations proposed under chapter 30A;  \\r\\n\\r\\n(iv) notices of hearings;  \\r\\n\\r\\n(v) winning bids for public contracts;  \\r\\n\\r\\n(vi) awards of federal, state and municipal government grants;  \\r\\n\\r\\n(vii) minutes of open meetings;  \\r\\n\\r\\n(viii) agency budgets; and  \\r\\n\\r\\n(ix) any public record information of significant interest that the agency deems appropriate to post.   \\r\\n\\r\\n']"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df['MGL'].values[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_df['combined_MGL'] = new_df['MGL'].apply(lambda lst: ' '.join([str(item) for item in lst if item is not None]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_df['length_MGL'] = new_df['combined_MGL'].apply(lambda x: len(x) if x else None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     12170.0\n",
+       "1      4047.0\n",
+       "2         NaN\n",
+       "3     14010.0\n",
+       "4      5565.0\n",
+       "5       680.0\n",
+       "6      2813.0\n",
+       "7     12170.0\n",
+       "8     78184.0\n",
+       "9         NaN\n",
+       "10    14010.0\n",
+       "11      680.0\n",
+       "Name: length_MGL, dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df['length_MGL']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_df.to_csv(\"12billswith.csv\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "maple",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

demoapp/README.md

@@ -0,0 +1,27 @@
+# Guideline to run the DemoApp using Streamlit
+
+Using Anaconda or create an environment to run streamlit  
+* Create env:  
+    ```python3 -m venv env```   
+    ```source ./env/bin/activate```  
+* Using Anaconda:  
+    ```conda create -n maple python=3.11.5```  
+    ```conda activate maple```
+
+In the file "app.py" in "demoapp" folder:
+* ```pip install streamlit```
+* Install all imported libraries: ```pip install pandas langchain openai chromadb tiktoken``` or you can refer to the requirement.txt
+* ```streamlit run demoapp/app2.py```  
+  (The "app2.py" is our work on the most popular 12 bills. It is our latest code with RAG, vectara.)
+
+# Additional Pointers (Source:Research Paper)
+In the demo app itself we have included evaluation metrics that help gauge the quality of the generated summaries in our use-case
+* The metrics we have used are : ROUGE-L, ROUGE-1, ROUGE-2, Cosine Similarity, and Factual Consistency Score.
+* ROUGE-1 is the the overlap of unigrams (each word) between the original bill and generated summaries.
+* ROUGE-2 is the overlap of bigrams between the original bill and generated summaries.
+* ROUGE-L tell us about the Longest common subsequence, taking into account sentence-level structure similarity naturally and helps identify longest co-occurring in sequence n-grams.
+* Cosine Similarity in this case tells us about the text similarity of two documents.
+* Factual Consistency Score: We used Vectara that trained transformer model to output probability from 0 to 1 by comparing the source and summary. 0 being hallucination, 1 being factually consistent.
+
+# Understand this folder
+extracted_mgl.txt is the relevant mgl content for the 12 bills that MAPLE team wanted. Extracted from the column using the csv files <b> 12billswithmgl.csv </b>

demoapp/all_bills.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:333981b1e35247c804711f8d456ffd1eb6b2c9e75b38f51639cc051f5c6d064b
+size 43685253

demoapp/app.py

@@ -0,0 +1,247 @@
+import streamlit as st
+import pandas as pd
+import os
+__import__('pysqlite3')
+import sys
+sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain, create_tagging_chain, create_tagging_chain_pydantic
+from langchain.vectorstores import Chroma
+from langchain.chat_models import ChatOpenAI
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+from langchain.callbacks import get_openai_callback
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from rouge_score import rouge_scorer
+from sentence_transformers import CrossEncoder
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.document_loaders import TextLoader
+from sidebar import *
+from tagging import *
+
+
+
+st.set_page_config(page_title="Summarize and Tagging MA Bills", layout='wide')
+st.title('Summarize Bills')
+
+sbar()
+
+
+template = """"You are a summarizer model that summarizes legal bills and legislation. Please include the bill's main purpose, relevant key points and any amendements. 
+The summaries must be easy to understand and accurate based on the provided bill. I want you to summarize the legal bill and legislation. 
+Use the title {title} to guide your summary. Summarize the bill that reads as follows:\n{context}\n\nSummary: An Act [bill title]. This bill [key information].
+"""
+
+# model to test hallucination
+# model = CrossEncoder('vectara/hallucination_evaluation_model')
+
+# load the dataset
+df = pd.read_csv("demoapp/all_bills.csv")
+
+# Creating search bar 
+search_number = st.text_input("Search by Bill Number")
+search_title = st.text_input("Search by Bill Title")
+
+# Initial empty DataFrame
+filtered_df = df
+
+# Filtering based on inputs
+if search_number:
+    filtered_df = df[df['BillNumber'].str.contains(search_number, case=False, na=False)]
+if search_title:
+    filtered_df = df[df['Title'].str.contains(search_title, case=False, na=False)]
+
+if not filtered_df.empty:
+    # Creating selectbox options safely
+    selectbox_options = [f"Bill #{num}: {filtered_df[filtered_df['BillNumber'] == num]['Title'].iloc[0]}" 
+                         for num in filtered_df['BillNumber'] if not filtered_df[filtered_df['BillNumber'] == num].empty]
+
+    option = st.selectbox(
+        'Select a Bill',
+        selectbox_options
+    )
+
+    # Extracting the bill number, title, and content from the selected option
+    bill_number = option.split(":")[0][6:]
+    bill_title = option.split(":")[1]
+    bill_content = filtered_df[filtered_df['BillNumber'] == bill_number]['DocumentText'].iloc[0]
+    
+else:
+    if search_number or search_title:
+        st.write("No bills found matching the search criteria.")
+
+
+def generate_categories(text):
+    """
+    generate tags and categories
+    parameters:
+        text: (string)
+    """
+    try:
+        API_KEY = st.session_state["OPENAI_API_KEY"]
+    except Exception as e:
+         return st.error("Invalid [OpenAI API key](https://beta.openai.com/account/api-keys) or not found")
+    
+    # LLM
+    category_prompt = """According to this list of category {category}.
+
+        classify this bill {context} into a closest relevant category.
+
+        Do not output a category outside from the list
+    """
+
+    prompt = PromptTemplate(template=category_prompt, input_variables=["context", "category"])
+
+    
+    llm = LLMChain(
+            llm = ChatOpenAI(openai_api_key=API_KEY, temperature=0, model='gpt-4'), prompt=prompt)
+        
+    response = llm.predict(context = text, category = category_for_bill) # grab from tagging.py
+    return response
+
+def generate_tags(category, context):
+    """Function to generate tags using Retrieval Augmented Generation
+    """
+
+    try:
+        API_KEY = st.session_state["OPENAI_API_KEY"]
+        os.environ['OPENAI_API_KEY'] = API_KEY
+    except Exception as e:
+         return st.error("Invalid [OpenAI API key](https://beta.openai.com/account/api-keys) or not found")
+    
+    loader = TextLoader("demoapp/category.txt").load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    documents = text_splitter.split_documents(loader)
+    vectorstore = Chroma.from_documents(documents, OpenAIEmbeddings())
+    retriever = vectorstore.as_retriever()
+
+    # LLM
+    template = """You are a trustworthy assistant for question-answering tasks.
+    Use the following pieces of retrieved context to answer the question.
+    Question: {question}
+    Context: {context}
+    Answer:
+    
+    """
+
+    prompt = PromptTemplate.from_template(template)
+    llm = ChatOpenAI(openai_api_key=API_KEY, temperature=0, model='gpt-4')
+
+    rag_chain = (
+        {"context": retriever, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+    query = f"""Output top 3 tags from the category {category} that is relevant to the context {context}
+    """
+        
+    response = rag_chain.invoke(query)
+    return response
+
+
+def generate_response(text, title):
+    """Function to generate response"""
+    try:
+        API_KEY = st.session_state['OPENAI_API_KEY']
+    except Exception as e:
+        return st.error("Invalid [OpenAI API key](https://beta.openai.com/account/api-keys) or not found")
+    
+    prompt = PromptTemplate(input_variables=["context", "title"], template=template)
+
+    # Instantiate LLM model
+    with get_openai_callback() as cb:
+        llm = LLMChain(
+            llm = ChatOpenAI(openai_api_key=API_KEY,
+                     temperature=0.01, model="gpt-3.5-turbo-1106"), prompt=prompt)
+        
+        response = llm.predict(context=text, title=title)
+        return response, cb.total_tokens, cb.prompt_tokens, cb.completion_tokens, cb.total_cost
+
+# Function to update or append to CSV
+def update_csv(bill_num, title, summarized_bill, category, tag, csv_file_path):
+    try:
+        df = pd.read_csv(csv_file_path)
+    except FileNotFoundError:
+        # If the file does not exist, create a new DataFrame
+        df = pd.DataFrame(columns=["Bill Number", "Bill Title", "Summarized Bill", "Category", "Tags"])
+    
+    mask = df["Bill Number"] == bill_num
+    if mask.any():
+        df.loc[mask, "Bill Title"] = title
+        df.loc[mask, "Summarized Bill"] = summarized_bill
+        df.loc[mask, "Category"] = category
+        df.loc[mask, "Tags"] = tag
+    else:
+        new_bill = pd.DataFrame([[bill_num, title, summarized_bill, category, tag]], columns=["Bill Number", "Bill Title", "Summarized Bill", "Category", "Tags"])
+        df = pd.concat([df, new_bill], ignore_index=True)
+    
+    df.to_csv(csv_file_path, index=False)
+    return df
+
+
+csv_file_path = "demoapp/generated_bills.csv"
+
+answer_container = st.container()
+with answer_container:
+    submit_button = st.button(label='Summarize')
+    # col1, col2, col3 = st.columns(3, gap='medium')
+    col1, col2, col3 = st.columns([1.5, 1.5, 1])
+
+    if submit_button:
+        with st.spinner("Working hard..."):
+            
+                response, response_tokens, prompt_tokens, completion_tokens, response_cost = generate_response(bill_content, bill_title)
+                category_response = generate_categories(bill_content)
+                tag_response = generate_tags(category_response, bill_content)
+                
+                with col1:
+                    st.subheader(f"Original Bill: #{bill_number}")
+                    st.write(bill_title)
+                    st.write(bill_content)
+
+                with col2:
+                    st.subheader("Generated Text")
+                    st.write(response)
+                    st.write("###")
+                    st.write("Category:", category_response)
+                    st.write(tag_response)
+                    
+                    update_csv(bill_number, bill_title, response, category_response, tag_response, csv_file_path)
+                    st.download_button(
+                            label="Download Text",
+                            data=pd.read_csv("demoapp/generated_bills.csv").to_csv(index=False).encode('utf-8'),
+                            file_name='Bills_Summarization.csv',
+                            mime='text/csv',)
+                    
+                with col3:
+                    st.subheader("Evaluation Metrics")
+                    # rouge score addition
+                    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
+                    rouge_scores = scorer.score(bill_content, response)
+                    st.write(f"ROUGE-1 Score: {rouge_scores['rouge1'].fmeasure:.2f}")
+                    st.write(f"ROUGE-2 Score: {rouge_scores['rouge2'].fmeasure:.2f}")
+                    st.write(f"ROUGE-L Score: {rouge_scores['rougeL'].fmeasure:.2f}")
+                    
+                    # calc cosine similarity
+                    vectorizer = TfidfVectorizer()
+                    tfidf_matrix = vectorizer.fit_transform([bill_content, response])
+                    cosine_sim = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])
+                    st.write(f"Cosine Similarity Score: {cosine_sim[0][0]:.2f}")
+
+                    # # test hallucination
+                    # scores = model.predict([
+                    #     [bill_content, response]
+                    # ])
+                    # score_result = float(scores[0])
+                    # st.write(f"Factual Consistency Score: {round(score_result, 2)}")
+                    
+                    st.write("###")
+                    st.subheader("Token Usage")
+                    st.write(f"Response Tokens: {response_tokens}")
+                    st.write(f"Prompt Response: {prompt_tokens}")
+                    st.write(f"Response Complete:{completion_tokens}")
+                    st.write(f"Response Cost: $ {response_cost}")

demoapp/app2.py

@@ -0,0 +1,286 @@
+import streamlit as st
+import pandas as pd
+import os
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.vectorstores import Chroma
+from langchain.chat_models import ChatOpenAI
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+from langchain.callbacks import get_openai_callback
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from rouge_score import rouge_scorer
+from sentence_transformers import CrossEncoder
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.document_loaders import TextLoader
+
+from sidebar import *
+from tagging import *
+
+
+st.set_page_config(page_title="Summarize and Tagging MA Bills", layout='wide')
+st.title('Summarize Bills')
+
+sbar()
+
+
+# model to test hallucination
+model = CrossEncoder('vectara/hallucination_evaluation_model')
+
+# load the dataset
+df = pd.read_csv("demoapp/12billswithmgl.csv")
+
+
+def find_bills(bill_number, bill_title):
+    """input:
+    args: bill_number: (str), Use the number of the bill to find its title and content
+    """
+    bill = df[df['BillNumber'] == bill_number]['DocumentText']
+
+    try:
+         # Locate the index of the bill
+        idx = bill.index.tolist()[0]
+        # Locate the content and bill title of bill based on idx
+        content = df['DocumentText'].iloc[idx]
+        #bill_title = df['Title'].iloc[idx]
+        bill_number = df['BillNumber'].iloc[idx]
+        # laws
+        # law = df['combined_MGL'].iloc[idx]
+
+        return content, bill_title, bill_number
+    
+    except Exception as e:
+        content = "blank"
+        st.error("Cannot find such bill from the source")
+    
+
+bills_to_select = {
+    '#H3121': 'An Act relative to the open meeting law',
+    '#S2064': 'An Act extending the public records law to the Governor and the Legislature',
+    '#H711': 'An Act providing a local option for ranked choice voting in municipal elections',
+    '#S1979': 'An Act establishing a jail and prison construction moratorium',
+    '#H489': 'An Act providing affordable and accessible high-quality early education and care to promote child development and well-being and support the economy in the Commonwealth',
+    '#S2014': 'An Act relative to collective bargaining rights for legislative employees',
+    '#S301': 'An Act providing affordable and accessible high quality early education and care to promote child development and well-being and support the economy in the Commonwealth',
+    '#H3069': 'An Act relative to collective bargaining rights for legislative employees',
+    '#S433': 'An Act providing a local option for ranked choice voting in municipal elections',
+    '#H400': 'An Act relative to vehicle recalls',
+    '#H538': 'An Act to Improve access, opportunity, and capacity in Massachusetts vocational-technical education',
+    '#S257': 'An Act to end discriminatory outcomes in vocational school admissions'
+}
+
+# Displaying the selectbox
+selectbox_options = [f"{number}: {title}" for number, title in bills_to_select.items()]
+option = st.selectbox(
+    'Select a Bill',
+    selectbox_options
+)
+
+# Extracting the bill number from the selected option
+selected_num = option.split(":")[0][1:]
+selected_title = option.split(":")[1]
+
+# bill_content, bill_title, bill_number, masslaw = find_bills(selected_num, selected_title)
+bill_content, bill_title, bill_number = find_bills(selected_num, selected_title)
+
+
+def generate_categories(text):
+    """
+    generate tags and categories
+    parameters:
+        text: (string)
+    """
+    try:
+        API_KEY = st.session_state["OPENAI_API_KEY"]
+    except Exception as e:
+         return st.error("Invalid [OpenAI API key](https://beta.openai.com/account/api-keys) or not found")
+    
+    # LLM
+    category_prompt = """According to this list of category {category}.
+
+        classify this bill {context} into a closest relevant category.
+
+        Do not output a category outside from the list
+    """
+
+    prompt = PromptTemplate(template=category_prompt, input_variables=["context", "category"])
+
+    
+    llm = LLMChain(
+            llm = ChatOpenAI(openai_api_key=API_KEY, temperature=0, model='gpt-4'), prompt=prompt)
+        
+    response = llm.predict(context = text, category = category_for_bill) # grab from tagging.py
+    return response
+
+
+# def generate_tags(category, context):
+#     """Function to generate tags using Retrieval Augmented Generation
+#     """
+#     try:
+#         API_KEY = st.session_state["OPENAI_API_KEY"]
+#         os.environ['OPENAI_API_KEY'] = API_KEY
+#     except Exception as e:
+#          return st.error("Invalid [OpenAI API key](https://beta.openai.com/account/api-keys) or not found")
+    
+#     loader = TextLoader("demoapp/category.txt").load()
+#     text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+#     documents = text_splitter.split_documents(loader)
+#     vectorstore = Chroma.from_documents(documents, OpenAIEmbeddings())
+#     retriever = vectorstore.as_retriever()
+
+#     # # Instantiate LLM model
+#     # with get_openai_callback() as cb:
+
+#     template = """You are a trustworthy assistant for question-answering tasks.
+#         Use the following pieces of retrieved context to answer the question.
+#         Question: {question}
+#         Context: {context}
+#         Answer:
+#         """
+
+#     prompt = PromptTemplate.from_template(template)
+#     llm = ChatOpenAI(openai_api_key=API_KEY, temperature=0, model='gpt-4', model_kwargs={'seed': 42})
+
+#     rag_chain = (
+#             {"context": retriever, "question": RunnablePassthrough()}
+#             | prompt
+#             | llm
+#             | StrOutputParser()
+#                 )
+#     
+#     query = f"""Output top 3 tags from the category {category} that is relevant to the context {context}"""
+#     response = rag_chain.invoke(query)
+
+#     return response
+
+
+def generate_response(text, category):
+    """Function to generate response"""
+
+    API_KEY = st.session_state["OPENAI_API_KEY"]
+    os.environ['OPENAI_API_KEY'] = API_KEY
+
+    loader = TextLoader("demoapp/extracted_mgl.txt").load()
+    text_splitter = CharacterTextSplitter(chunk_size=4000, chunk_overlap=0)
+    documents = text_splitter.split_documents(loader)
+    
+    vectorstore = Chroma.from_documents(documents, OpenAIEmbeddings())
+    retriever = vectorstore.as_retriever()
+
+        
+    template = """You are a trustworthy assistant for question-answering tasks.
+        Use the following pieces of retrieved context to answer the question.
+        Question: {question}
+        Context: {context}
+        Answer:
+        """
+
+    prompt = PromptTemplate.from_template(template)
+    llm = ChatOpenAI(openai_api_key=API_KEY, temperature=0, model='gpt-4-1106-preview', model_kwargs={'seed': 42})  
+        
+    rag_chain = (
+            {"context": retriever, "question": RunnablePassthrough()}
+            | prompt
+            | llm
+            | StrOutputParser()
+        )
+    query = f""" Can you please explain what the following MA bill means to a regular resident without specialized knowledge? 
+            Please provide a one paragraph summary in 4 sentences. Please be direct and concise for the busy reader.
+            Note that the bill refers to specific existing sections of the Mass General Laws. Use the information from those sections in your context to construct your summary.
+            Summarize the bill that reads as follows:\n{text}\n\n
+            
+            After generating summary, output Category: {category}.
+            Then, output top 3 tags in this specific category from the list of tags {tags_for_bill} that are relevant to this bill. \n"""
+            # Do not output the tags outside from the list. \n
+            # """
+    with get_openai_callback() as cb:
+        response = rag_chain.invoke(query)
+        st.write(cb.total_tokens, cb.prompt_tokens, cb.completion_tokens, cb.total_cost)
+        
+    return response
+    
+
+# Function to update or append to CSV
+def update_csv(bill_num, title, summarized_bill, category, tag, csv_file_path):
+    try:
+        df = pd.read_csv(csv_file_path)
+    except FileNotFoundError:
+        # If the file does not exist, create a new DataFrame
+        df = pd.DataFrame(columns=["Bill Number", "Bill Title", "Summarized Bill", "Category", "Tags"])
+    
+    mask = df["Bill Number"] == bill_num
+    if mask.any():
+        df.loc[mask, "Bill Title"] = title
+        df.loc[mask, "Summarized Bill"] = summarized_bill
+        df.loc[mask, "Category"] = category
+        df.loc[mask, "Tags"] = tag
+    else:
+        new_bill = pd.DataFrame([[bill_num, title, summarized_bill, category, tag]], columns=["Bill Number", "Bill Title", "Summarized Bill", "Category", "Tags"])
+        df = pd.concat([df, new_bill], ignore_index=True)
+    
+    df.to_csv(csv_file_path, index=False)
+    return df
+
+csv_file_path = "demoapp/generated_bills.csv"
+
+
+answer_container = st.container()
+with answer_container:
+    submit_button = st.button(label='Summarize')
+    col1, col2, col3 = st.columns([1.5, 1.5, 1])
+
+    if submit_button:
+        with st.spinner("Working hard..."):
+
+            category_response = generate_categories(bill_content)
+            response = generate_response(bill_content, category_response)
+            #tag_response = generate_tags(category_response, bill_content)
+    
+            with col1:
+                st.subheader(f"Original Bill: #{bill_number}")
+                st.write(bill_title)
+                st.write(bill_content)
+
+            with col2:
+                st.subheader("Generated Text")
+                st.write(response)
+                st.write("###")
+                    
+                # update_csv(bill_number, bill_title, response, category_response, tag_response, csv_file_path)
+                # st.download_button(
+                #             label="Download Text",
+                #             data=pd.read_csv("demoapp/generated_bills.csv").to_csv(index=False).encode('utf-8'),
+                #             file_name='Bills_Summarization.csv',
+                #             mime='text/csv',)
+                    
+            with col3:
+                st.subheader("Evaluation Metrics")
+                # rouge score addition
+                scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
+                rouge_scores = scorer.score(bill_content, response)
+                st.write(f"ROUGE-1 Score: {rouge_scores['rouge1'].fmeasure:.2f}")
+                st.write(f"ROUGE-2 Score: {rouge_scores['rouge2'].fmeasure:.2f}")
+                st.write(f"ROUGE-L Score: {rouge_scores['rougeL'].fmeasure:.2f}")
+                    
+                # calc cosine similarity
+                vectorizer = TfidfVectorizer()
+                tfidf_matrix = vectorizer.fit_transform([bill_content, response])
+                cosine_sim = cosine_similarity(tfidf_matrix[0], tfidf_matrix[1])
+                st.write(f"Cosine Similarity Score: {cosine_sim[0][0]:.2f}")
+
+                # test hallucination
+                scores = model.predict([
+                        [bill_content, response]
+                    ])
+                score_result = float(scores[0])
+                st.write(f"Factual Consistency Score: {round(score_result, 2)}")
+                             
+                    # st.write("###")
+                    # st.subheader("Token Usage")
+                    # st.write(f"Response Tokens: {response_tokens + tag_tokens + cate_tokens}")
+                    # st.write(f"Prompt Response: {prompt_tokens + tag_tokens + cate_prompt}")
+                    # st.write(f"Response Complete:{completion_tokens +  tag_completion + cate_completion}")
+                    # st.write(f"Response Cost: $ {response_cost + tag_cost + cate_cost}")              
+                    # st.write(f"Cost: response $ {response_cost + tag_cost + cate_cost}")  

demoapp/category.txt

@@ -0,0 +1,226 @@
+List of Category available: [Environmental, Education, Health Care, Criminal Justice, Taxation, Transportation, Housing, Civil Rights, Labor and Employment, Budget and Appropriations, Public Safety, Technology and Innovation, Immigration, Economic Development, Social Services]
+
+Category: Environmental
+
+List of tags in Environmental:
+  
+    [Climate Change Mitigation,
+    Renewable Energy Initiatives,
+    Biodiversity Conservation,
+    Water Pollution Control,
+    Sustainable Agriculture Practices,
+    Air Quality Improvement,
+    Waste Reduction Programs,
+    Coastal Erosion Management,
+    Environmental Impact Assessments,
+    Wildlife Habitat Protection]
+
+Category: Education
+
+List of tags in Education:
+
+    [Curriculum Development,
+    Digital Learning Resources,
+    Teacher Professional Development,
+    Early Childhood Education,
+    Special Education Services,
+    Literacy Programs,
+    Vocational Training,
+    Education Technology Integration,
+    School Infrastructure Upgrades,
+    Student Mental Health Support]
+
+Category: Health Care
+
+List of tags in Health Care:
+  
+    [Universal Health Coverage,
+    Healthcare Access for All,
+    Mental Health Services Expansion,
+    Disease Prevention Programs,
+    Elderly Care Services,
+    Healthcare Quality Standards,
+    Health Information Technology,
+    Maternal and Child Health,
+    Public Health Emergency Preparedness,
+    Healthcare Workforce Training]
+
+Categpry: Criminal Justice
+
+List of tags in Criminal Justice
+
+    [Police Reforms,
+    Prisoner Rehabilitation,
+    Community Policing Initiatives,
+    Restorative Justice Programs,
+    Criminal Sentencing Reform,
+    Victim Support Services,
+    Legal Aid for the Underprivileged,
+    Juvenile Justice System Overhaul,
+    Hate Crime Prevention Measures,
+    Court System Modernization]
+
+Category: Taxation
+
+List of tags in Taxation
+  
+    [Progressive Tax Reform,
+    Corporate Taxation Policies,
+    Income Tax Deductions,
+    Property Tax Relief,
+    Sales Tax Revision,
+    Tax Compliance Regulations,
+    Tax Transparency Measures,
+    Wealth Redistribution Initiatives,
+    Small Business Tax Breaks,
+    Tax Fraud Prevention Measures]
+
+Category: Transportation
+
+List of tags in Transportation
+  
+    [Public Transit Expansion,
+    Road Infrastructure Maintenance,
+    Bike and Pedestrian Path Development,
+    Traffic Congestion Reduction,
+    Electric Vehicle Adoption Incentives,
+    Freight Transportation Efficiency,
+    Aviation Safety Regulations,
+    Railroad Infrastructure Modernization,
+    Intermodal Transportation Integration,
+    Autonomous Vehicle Regulations]
+
+Category: Housing
+
+List of tags in Housing
+  
+    [Affordable Housing Development,
+    Homelessness Prevention Programs,
+    Rent Control Measures,
+    Fair Housing Enforcement,
+    Housing Discrimination Prevention,
+    Housing Voucher Program Expansion,
+    Sustainable Housing Initiatives,
+    Urban Redevelopment Plans,
+    Eviction Moratoriums,
+    Homeownership Support Programs]
+
+Category: Civil Rights
+
+List of tags in Civil Rights
+  
+    [Anti-Discrimination Laws,
+    Gender Equality Protections,
+    LGBTQ+ Rights Advocacy,
+    Disability Rights Enforcement,
+    Indigenous Peoples' Rights,
+    Religious Freedom Protection,
+    Language Access Policies,
+    Voting Rights Expansion,
+    Equal Pay Legislation,
+    Minority Rights Safeguards]
+
+Category: Labor and Employment
+
+List of tags in Labor and Employment
+  
+    [Minimum Wage Increase,
+    Occupational Safety Regulations,
+    Labor Union Protections,
+    Workplace Harassment Prevention,
+    Job Training and Apprenticeships,
+    Workforce Diversity Initiatives,
+    Employee Benefits Expansion,
+    Fair Working Hours Regulations,
+    Unemployment Benefits Enhancement,
+    Remote Work Policies]
+
+Category: Budget and Appropriations
+
+List of tags in Budget and Appropriations
+  
+    [Government Spending Oversight,
+    Emergency Fund Allocation,
+    Public Debt Management,
+    Fiscal Responsibility Audits,
+    Public Infrastructure Investment,
+    Social Welfare Program Funding,
+    Pension Plan Reform,
+    Local Government Grant Programs,
+    Financial Aid for Disadvantaged Communities,
+    Tax Revenue Allocation]
+
+Category: Public Safety
+
+List of tags in Public Safety
+  
+    [Emergency Response Planning,
+    Disaster Preparedness Training,
+    Cybersecurity Protocols,
+    Domestic Violence Prevention,
+    Fire Safety Regulations,
+    Gun Control Measures,
+    Community Health and Safety Programs,
+    Public Health Crisis Management,
+    Hate Crime Reporting Systems,
+    Crime Prevention Initiatives]
+
+Category: Technology and Innovation
+
+List of tags in Technology and Innovation
+  
+    [Digital Privacy Laws,
+    Data Security Measures,
+    Innovation Investment Policies,
+    Broadband Infrastructure Expansion,
+    E-Government Service Enhancements,
+    Technology Education in Schools,
+    Artificial Intelligence Regulations,
+    Privacy Protection for Biometric Data,
+    Blockchain Integration Strategies]
+    Open Data Initiatives
+
+Category: Immigration
+
+List of tags in Immigration
+  
+    [Immigration Policy Reform,
+    Refugee Resettlement Programs,
+    Asylum Seeker Protections,
+    Migrant Worker Rights,
+    Language Access Services,
+    Family Reunification Initiatives,
+    Pathways to Citizenship,
+    Humanitarian Aid for Migrants,
+    Border Security Measures,
+    Anti-Trafficking Efforts]
+
+Category: Economic Development
+
+List of tags in Economic Development
+  
+    [Small Business Support Programs,
+    Export Promotion Policies,
+    Rural Development Initiatives,
+    Entrepreneurship Training,
+    Trade Agreement Negotiations,
+    Tourism Industry Growth,
+    Regional Economic Integration,
+    Financial Inclusion Programs,
+    Economic Diversification Strategies,
+    Infrastructure Investment Plans]
+
+Category: Social Services
+
+List of tags in Social Services
+  
+    [Child Welfare Services,
+    Domestic Violence Support,
+    Elderly Care Programs,
+    Foster Care System Reforms,
+    Disability Assistance Programs,
+    Community Support Services,
+    Youth Mentorship Programs,
+    Substance Abuse Rehabilitation,
+    Home Care for the Disabled,
+    Affordable Childcare Services]

demoapp/generated_bills.csv

@@ -0,0 +1,23 @@
+Bill Number,Bill Title,Summarized Bill,Category,Tags
+H20, An Act clarifying and normalizing non-public school service purchases,"Summary: An Act clarifying and normalizing non-public school service purchases allows members of a contributory retirement system who were previously employed as teachers in special education programs for school-aged children in non-public schools in Massachusetts to establish such service as creditable service by making a deposit into the annuity savings fund of the system. The bill specifies the conditions and terms for making such deposits and the maximum credit for service in non-public schools. It also defines ""non-public school"" and excludes activities in a residence or afterschool program from the definition.",Education,"1. Curriculum Development
+2. Teacher Professional Development
+3. Special Education Services"
+H1," An Act making appropriations for the Fiscal Year 2024 for the maintenance of the departments, boards, commissions, institutions, and certain activities of the Commonwealth, for interest, sinking fund, and serial bond requirements, and for certain permanent improvements","Summary: An Act making appropriations for the Fiscal Year 2024 for the maintenance of the departments, boards, commissions, institutions, and certain activities of the Commonwealth, for interest, sinking fund, and serial bond requirements, and for certain permanent improvements. This bill outlines the budget allocations for various departments, boards, commissions, and institutions of the Commonwealth for the fiscal year 2024. It also includes provisions for interest, sinking fund, and serial bond requirements, as well as funding for certain permanent improvements. The bill may also include any amendments made during the legislative process.",The bill cannot be classified as it is labeled as 'nan' which typically means 'not a number' or 'not available'. More information is needed to classify the bill.,"The question does not provide a specific category to select tags from. However, based on the context provided, here are the top three tags from a few categories:
+
+1. Budget and Appropriations: Government Spending Oversight, Emergency Fund Allocation, Public Debt Management
+2. Public Safety: Emergency Response Planning, Disaster Preparedness Training, Cybersecurity Protocols
+3. Environmental: Climate Change Mitigation, Renewable Energy Initiatives, Biodiversity Conservation
+4. Transportation: Public Transit Expansion, Road Infrastructure Maintenance, Bike and Pedestrian Path Development
+5. Technology and Innovation: Digital Privacy Laws, Data Security Measures, Innovation Investment Policies
+
+Please specify a category for a more precise answer."
+H10, An Act relative to the recovery of overearnings,"Summary: An Act relative to the recovery of overearnings
+
+This bill seeks to amend paragraph (c) of Section 91 of chapter 32 of the general laws. The proposed amendment allows the retirement board of which the member is a retiree to recover an amount equal to the overearnings if the excess is not recovered by the appropriate treasurer or other person responsible for the payment of the compensation. However, the amount of the recovery cannot exceed the amount of the retirement allowance paid in the year in which the overearnings occurred.",Budget and Appropriations,"1. Government Spending Oversight
+2. Public Debt Management
+3. Pension Plan Reform"
+H1000, An Act providing living organ donor protections,"Summary: An Act providing living organ donor protections
+
+This bill aims to protect living organ donors from discrimination by insurance companies. It requires the commissioner of insurance to create rules and regulations to enforce the protection of living organ donors. It also mandates that the department of health make materials related to live organ donation available to the public. Additionally, the bill prohibits insurers from declining or limiting coverage, precluding donation as a condition of coverage, or otherwise discriminating against living organ donors in policies for disability, life insurance, annuities, and long-term care insurance. Violations of these provisions are considered unfair practices under the law.",Health Care,"1. Universal Health Coverage
+2. Healthcare Access for All
+3. Health Information Technology"

demoapp/requirements.txt

@@ -0,0 +1,10 @@
+langchain==0.0.335
+openai==1.3.4
+pandas==2.1.3
+streamlit==1.28.2
+scikit-learn==1.3.2
+rouge-score==0.1.2 
+chromadb==0.4.18
+tiktoken==0.5.1
+sentence-transformers==2.2.2
+pysqlite3-binary==0.5.2

demoapp/sidebar.py

@@ -0,0 +1,30 @@
+import streamlit as st
+
+def set_openai_api_key(api_key: str):
+    try:
+        st.session_state["OPENAI_API_KEY"] = api_key
+    except Exception as e:
+       return("error")
+
+
+def sbar():
+    with st.sidebar:
+        st.markdown("## How to use\n"
+                    "1. Enter your [OpenAI API key](https://platform.openai.com/account/api-keys) below🔑\n"
+                    "2. Press enter")
+        API_KEY = st.text_input("OpenAI API Key",
+                                            placeholder="Paste your OpenAI API key here (sk-...)",
+                                            type="password")
+        
+        st.markdown("---")
+        st.markdown("# About\n")
+        st.write("input 0.0010 per 1000 tokens, output 0.0020 per 1000 tokens")
+        st.markdown("---")
+        st.markdown("Privacy")
+        st.markdown(
+            "Your API key will not saved at all."
+        )
+        st.markdown("This tool is a work in progress")
+
+        if API_KEY:
+            set_openai_api_key(API_KEY)

demoapp/tagging.py

@@ -0,0 +1,330 @@
+category_for_bill = """
+[Environmental, Education, Health Care, Criminal Justice, Taxation, Transportation, 
+Housing, Civil Rights, Labor and Employment, Budget and Appropriations, Public Safety, Technology and Innovation, 
+Immigration, Economic Development, Social Services]
+"""
+
+tags_for_bill = """
+
+{"Environmental": [Climate Change Mitigation,
+    Renewable Energy Initiatives,
+    Biodiversity Conservation,
+    Water Pollution Control,
+    Sustainable Agriculture Practices,
+    Air Quality Improvement,
+    Waste Reduction Programs,
+    Coastal Erosion Management,
+    Environmental Impact Assessments,
+    Wildlife Habitat Protection], 
+
+"Education": [Curriculum Development,
+    Digital Learning Resources,
+    Teacher Professional Development,
+    Early Childhood Education,
+    Special Education Services,
+    Literacy Programs,
+    Vocational Training,
+    Education Technology Integration,
+    School Infrastructure Upgrades,
+    Student Mental Health Support], 
+
+"Health Care": [Universal Health Coverage,
+    Healthcare Access for All,
+    Mental Health Services Expansion,
+    Disease Prevention Programs,
+    Elderly Care Services,
+    Healthcare Quality Standards,
+    Health Information Technology,
+    Maternal and Child Health,
+    Public Health Emergency Preparedness,
+    Healthcare Workforce Training], 
+
+"Criminal Justice": [Police Reforms,
+    Prisoner Rehabilitation,
+    Community Policing Initiatives,
+    Restorative Justice Programs,
+    Criminal Sentencing Reform,
+    Victim Support Services,
+    Legal Aid for the Underprivileged,
+    Juvenile Justice System Overhaul,
+    Hate Crime Prevention Measures,
+    Court System Modernization], 
+
+"Taxation": [Progressive Tax Reform,
+    Corporate Taxation Policies,
+    Income Tax Deductions,
+    Property Tax Relief,
+    Sales Tax Revision,
+    Tax Compliance Regulations,
+    Tax Transparency Measures,
+    Wealth Redistribution Initiatives,
+    Small Business Tax Breaks,
+    Tax Fraud Prevention Measures],
+
+"Transportation": [Public Transit Expansion,
+    Road Infrastructure Maintenance,
+    Bike and Pedestrian Path Development,
+    Traffic Congestion Reduction,
+    Electric Vehicle Adoption Incentives,
+    Freight Transportation Efficiency,
+    Aviation Safety Regulations,
+    Railroad Infrastructure Modernization,
+    Intermodal Transportation Integration,
+    Autonomous Vehicle Regulations],
+
+"Housing": [Affordable Housing Development,
+    Homelessness Prevention Programs,
+    Rent Control Measures,
+    Fair Housing Enforcement,
+    Housing Discrimination Prevention,
+    Housing Voucher Program Expansion,
+    Sustainable Housing Initiatives,
+    Urban Redevelopment Plans,
+    Eviction Moratoriums,
+    Homeownership Support Programs],
+
+"Civil Rights": [Anti-Discrimination Laws,
+    Gender Equality Protections,
+    LGBTQ+ Rights Advocacy,
+    Disability Rights Enforcement,
+    Indigenous Peoples' Rights,
+    Religious Freedom Protection,
+    Language Access Policies,
+    Voting Rights Expansion,
+    Equal Pay Legislation,
+    Minority Rights Safeguards],
+
+"Labor and Employment": [Minimum Wage Increase,
+    Occupational Safety Regulations,
+    Labor Union Protections,
+    Workplace Harassment Prevention,
+    Job Training and Apprenticeships,
+    Workforce Diversity Initiatives,
+    Employee Benefits Expansion,
+    Fair Working Hours Regulations,
+    Unemployment Benefits Enhancement,
+    Remote Work Policies],
+
+"Budget and Appropriations": [Government Spending Oversight,
+    Emergency Fund Allocation,
+    Public Debt Management,
+    Fiscal Responsibility Audits,
+    Public Infrastructure Investment,
+    Social Welfare Program Funding,
+    Pension Plan Reform,
+    Local Government Grant Programs,
+    Financial Aid for Disadvantaged Communities,
+    Tax Revenue Allocation]
+
+"Public Safety": [Emergency Response Planning,
+    Disaster Preparedness Training,
+    Cybersecurity Protocols,
+    Domestic Violence Prevention,
+    Fire Safety Regulations,
+    Gun Control Measures,
+    Community Health and Safety Programs,
+    Public Health Crisis Management,
+    Hate Crime Reporting Systems,
+    Crime Prevention Initiatives],
+
+"Technology and Innovation": [Digital Privacy Laws,
+    Data Security Measures,
+    Innovation Investment Policies,
+    Broadband Infrastructure Expansion,
+    E-Government Service Enhancements,
+    Technology Education in Schools,
+    Artificial Intelligence Regulations,
+    Privacy Protection for Biometric Data,
+    Blockchain Integration Strategies]
+    Open Data Initiatives
+
+"Immigration": [Immigration Policy Reform,
+    Refugee Resettlement Programs,
+    Asylum Seeker Protections,
+    Migrant Worker Rights,
+    Language Access Services,
+    Family Reunification Initiatives,
+    Pathways to Citizenship,
+    Humanitarian Aid for Migrants,
+    Border Security Measures,
+    Anti-Trafficking Efforts], 
+
+"Economic Development": [Small Business Support Programs,
+    Export Promotion Policies,
+    Rural Development Initiatives,
+    Entrepreneurship Training,
+    Trade Agreement Negotiations,
+    Tourism Industry Growth,
+    Regional Economic Integration,
+    Financial Inclusion Programs,
+    Economic Diversification Strategies,
+    Infrastructure Investment Plans],
+
+"Social Services": [Child Welfare Services,
+    Domestic Violence Support,
+    Elderly Care Programs,
+    Foster Care System Reforms,
+    Disability Assistance Programs,
+    Community Support Services,
+    Youth Mentorship Programs,
+    Substance Abuse Rehabilitation,
+    Home Care for the Disabled,
+    Affordable Childcare Services]
+"""
+
+tagging = """
+[Climate Change Mitigation,
+Renewable Energy Initiatives,
+Biodiversity Conservation,
+Water Pollution Control,
+Sustainable Agriculture Practices,
+Air Quality Improvement,
+Waste Reduction Programs,
+Coastal Erosion Management,
+Environmental Impact Assessments,
+Wildlife Habitat Protection,
+Curriculum Development,
+Digital Learning Resources,
+Teacher Professional Development,
+Early Childhood Education,
+Special Education Services,
+Literacy Programs,
+Vocational Training,
+Education Technology Integration,
+School Infrastructure Upgrades,
+Student Mental Health Support,
+Universal Health Coverage,
+Healthcare Access for All,
+Mental Health Services Expansion,
+Disease Prevention Programs,
+Elderly Care Services,
+Healthcare Quality Standards,
+Health Information Technology,
+Maternal and Child Health,
+Public Health Emergency Preparedness,
+Healthcare Workforce Training,
+Police Reforms,
+Prisoner Rehabilitation,
+Community Policing Initiatives,
+Restorative Justice Programs,
+Criminal Sentencing Reform,
+Victim Support Services,
+Legal Aid for the Underprivileged,
+Juvenile Justice System Overhaul,
+Hate Crime Prevention Measures,
+Court System Modernization,
+Progressive Tax Reform,
+Corporate Taxation Policies,
+Income Tax Deductions,
+Property Tax Relief,
+Sales Tax Revision,
+Tax Compliance Regulations,
+Tax Transparency Measures,
+Wealth Redistribution Initiatives,
+Small Business Tax Breaks,
+Tax Fraud Prevention Measures,
+Public Transit Expansion,
+Road Infrastructure Maintenance,
+Bike and Pedestrian Path Development,
+Traffic Congestion Reduction,
+Electric Vehicle Adoption Incentives,
+Freight Transportation Efficiency,
+Aviation Safety Regulations,
+Railroad Infrastructure Modernization,
+Intermodal Transportation Integration,
+Autonomous Vehicle Regulations,
+Affordable Housing Development,
+Homelessness Prevention Programs,
+Rent Control Measures,
+Fair Housing Enforcement,
+Housing Discrimination Prevention,
+Housing Voucher Program Expansion,
+Sustainable Housing Initiatives,
+Urban Redevelopment Plans,
+Eviction Moratoriums,
+Homeownership Support Programs,
+Anti-Discrimination Laws,
+Gender Equality Protections,
+LGBTQ+ Rights Advocacy,
+Disability Rights Enforcement,
+Indigenous Peoples' Rights,
+Religious Freedom Protection,
+Language Access Policies,
+Voting Rights Expansion,
+Equal Pay Legislation,
+Minority Rights Safeguards,
+Minimum Wage Increase,
+Occupational Safety Regulations,
+Labor Union Protections,
+Workplace Harassment Prevention,
+Job Training and Apprenticeships,
+Workforce Diversity Initiatives,
+Employee Benefits Expansion,
+Fair Working Hours Regulations,
+Unemployment Benefits Enhancement,
+Remote Work Policies,
+Government Spending Oversight,
+Emergency Fund Allocation,
+Public Debt Management,
+Fiscal Responsibility Audits,
+Public Infrastructure Investment,
+Social Welfare Program Funding,
+Pension Plan Reform,
+Local Government Grant Programs,
+Financial Aid for Disadvantaged Communities,
+Tax Revenue Allocation,
+Emergency Response Planning,
+Disaster Preparedness Training,
+Cybersecurity Protocols,
+Domestic Violence Prevention,
+Fire Safety Regulations,
+Gun Control Measures,
+Community Health and Safety Programs,
+Public Health Crisis Management,
+Hate Crime Reporting Systems,
+Crime Prevention Initiatives,
+Digital Privacy Laws,
+Data Security Measures,
+Innovation Investment Policies,
+Broadband Infrastructure Expansion,
+E-Government Service Enhancements,
+Technology Education in Schools,
+Artificial Intelligence Regulations,
+Privacy Protection for Biometric Data,
+Blockchain Integration Strategies,
+Open Data Initiatives,
+Immigration Policy Reform,
+Refugee Resettlement Programs,
+Asylum Seeker Protections,
+Migrant Worker Rights,
+Language Access Services,
+Family Reunification Initiatives,
+Pathways to Citizenship,
+Humanitarian Aid for Migrants,
+Border Security Measures,
+Anti-Trafficking Efforts,
+Small Business Support Programs,
+Export Promotion Policies,
+Rural Development Initiatives,
+Entrepreneurship Training,
+Trade Agreement Negotiations,
+Tourism Industry Growth,
+Regional Economic Integration,
+Financial Inclusion Programs,
+Economic Diversification Strategies,
+Infrastructure Investment Plans,
+Child Welfare Services,
+Domestic Violence Support,
+Elderly Care Programs,
+Foster Care System Reforms,
+Disability Assistance Programs,
+Community Support Services,
+Youth Mentorship Programs,
+Substance Abuse Rehabilitation,
+Home Care for the Disabled,
+Affordable Childcare Services,
+]
+"""
+
+
+