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 12billswithmgl.csv