updated pfdr readme

app.py

@@ -8,7 +8,6 @@ import numpy as np
 from abc import ABC, abstractmethod
 from typing import List, Dict, Any, Tuple
 from collections import defaultdict
-from tqdm import tqdm
 import pandas as pd
 from datetime import datetime, date
 from datasets import load_dataset, load_from_disk
@@ -300,28 +299,64 @@ st.image('local_files/pathfinder_logo.png')
 
 st.expander("What is Pathfinder / How do I use it?", expanded=False).write(
         """
-        Pathfinder v2.0 is a framework for searching and visualizing astronomy papers on the [arXiv](https://arxiv.org/) and [ADS](https://ui.adsabs.harvard.edu/) using the context
-        sensitivity from modern large language models (LLMs) to better parse patterns in paper contexts.
+        # Welcome to Pathfinder
 
-        This tool was built during the [JSALT workshop](https://www.clsp.jhu.edu/2024-jelinek-summer-workshop-on-speech-and-language-technology/) to do awesome things.
+        ## Discover the Universe Through AI-Powered Astronomy ReSearch
 
-        **👈 Use the sidebar to tweak the search parameters to get better results**.
+        ### What is Pathfinder?
 
-        ### Tool summary:
-        - Please wait while the initial data loads and compiles, this takes about a minute initially.
+        Pathfinder (https://pfdr.app) harnesses the power of modern large language models (LLMs) in combination with papers on the [arXiv](https://arxiv.org/) and [ADS](https://ui.adsabs.harvard.edu/) to navigate the vast expanse of astronomy literature.
+        Our tool empowers researchers, students, and astronomy enthusiasts to get started on their journeys to find answers to complex research questions quickly and efficiently.
 
-        This is not meant to be a replacement to existing tools like the
-        [ADS](https://ui.adsabs.harvard.edu/),
-        [arxivsorter](https://www.arxivsorter.org/), semantic search or google scholar, but rather a supplement to find papers
-        that otherwise might be missed during a literature survey.
-        It is trained on astro-ph (astrophysics of galaxies) papers up to last-year-ish mined from arxiv and supplemented with ADS metadata,
-        if you are interested in extending it please reach out!
+        This is not meant to be a replacement to existing tools like the [ADS](https://ui.adsabs.harvard.edu/), [arxivsorter](https://www.arxivsorter.org/), semantic search or google scholar, but rather a supplement to find papers that otherwise might be missed during a literature survey. It is trained on astro-ph papers up to July 2024.
 
-        Also add: feedback form, socials, literature, contact us, copyright, collaboration, etc.
+        ### How to Use Pathfinder
 
-        The image below shows a representation of all the astro-ph.GA papers that can be explored in more detail
-        using the `Arxiv embedding` page. The papers tend to cluster together by similarity, and result in an
-        atlas that shows well studied (forests) and currently uncharted areas (water).
+        You can use pathfinder to find papers of interest with natural-language questions, and generate basic answers to questions using the retrieved papers. Try asking it questions like
+
+        - What is the value of the Hubble Constant?
+        - Are there open source radiative transfer codes for planetary atmospheres?
+        - Can I predict a galaxy spectrum from an image cutout? Please reply in Hindi.
+        - How would galaxy evolution differ in a universe with no dark matter?
+
+        **👈 Use the sidebar to tweak the search parameters to get better results**. Changing the number of retrieved papers (**top-k**), weighting by keywords, time, or citations, or changing the prompt type might help better refine the paper search and synthesized answers for your specific question.
+
+        1. **Enter Your Query**: Type your astronomy question in the search bar & hit `run pathfinder`.
+        2. **Review Results**: Pathfinder will analyze relevant literature and present you with a concise answer.
+        3. **Explore Further**: Click on provided links to delve deeper into the source material on ADS.
+        4. **Refine Your Search**: Use our advanced filters to narrow down results by date, author, or topic.
+        5. **Download results:** You can download the results of your query as a json file.
+
+        ### Why Use Pathfinder?
+
+        - **Time-Saving**: Get started finding answers that would take hours of manual research.
+        - **Comprehensive**: Access information from papers across a large database of astronomy literature.
+        - **User-Friendly**: Intuitive interface designed for researchers at all levels.
+        - **Constantly Updated**: Our database is regularly refreshed with the latest publications.
+
+        ### Learn More
+
+        - Read our paper on [arXiv](https://arxiv.org/abs/2408.01556) to understand the technology behind Pathfinder.
+        - Discover how Pathfinder was developed in collaboration with [UniverseTBD](https://www.universetbd.org) on its mission is to democratise science for everyone, and [JSALT](https://www.clsp.jhu.edu/2024-jelinek-summer-workshop-on-speech-and-language-technology/).
+
+        ---
+
+        ### Copyright and Terms of Use
+
+        © 2024 Pathfinder. All rights reserved.
+
+        Pathfinder is provided "as is" without warranty of any kind. By using this service, you agree to our [Terms of Service] and [Privacy Policy].
+
+        ### Contact Us
+
+        Have questions or feedback? We'd love to hear from you!
+        - Email: [email protected]
+        - Twitter: [@universe_tbd](https://twitter.com/universe_tbd)
+        - Huggingface: [https://huggingface.co/spaces/kiyer/pathfinder/](https://huggingface.co/spaces/kiyer/pathfinder/)
+
+        ---
+
+        *Empowering astronomical discoveries, one query at a time.*
         """
     )
 
@@ -433,7 +468,8 @@ def run_agent_qa(query):
         st.session_state.agent = agent
 
     if 'agent_executor' not in st.session_state:
-        agent_executor = AgentExecutor(agent=st.session_state.agent, tools=st.session_state.tools, verbose=True, handle_parsing_errors=True, callbacks=CallbackManager([file_handler]))
+        # agent_executor = AgentExecutor(agent=st.session_state.agent, tools=st.session_state.tools, verbose=True, handle_parsing_errors=True, callbacks=CallbackManager([file_handler]))
+        agent_executor = AgentExecutor(agent=st.session_state.agent, tools=st.session_state.tools, handle_parsing_errors=True, callbacks=CallbackManager([file_handler]))
         st.session_state.agent_executor = agent_executor
 
     answer = st.session_state.agent_executor.invoke({"input": query,})