app.py

import os
import glob
from pathlib import Path
import gradio as gr
import nest_asyncio
import dill as pickle
import streamlit as st

# Ensure async compatibility in Jupyter
nest_asyncio.apply()

# Import OpenAI key with helper function
from helper import get_openai_api_key
OPENAI_API_KEY = get_openai_api_key()

# Define the path to the directory containing the PDF files
folder_path = 'Ehlers-Danlos-1'

# Get the list of all PDF files in the directory
pdf_files = glob.glob(os.path.join(folder_path, '*.pdf'))
print(pdf_files)

# Extract just the filenames (optional)
pdf_filenames = [os.path.basename(pdf) for pdf in pdf_files]
print(pdf_filenames)

# Import utilities
from utils import get_doc_tools

# Truncate function names if necessary
def truncate_function_name(name, max_length=64):
    return name if len(name) <= max_length else name[:max_length]

# Path to save/load serialized tools
tools_cache_path = 'tools_cache.pkl'

# Initialize paper_to_tools_dict
paper_to_tools_dict = {}

# Check if the cache file exists and is not empty
if os.path.exists(tools_cache_path) and os.path.getsize(tools_cache_path) > 0:
    try:
        with open(tools_cache_path, 'rb') as f:
            paper_to_tools_dict = pickle.load(f)
    except EOFError:
        print("Cache file is corrupted. Recreating tools.")
        paper_to_tools_dict = {}
else:
    print("Cache file does not exist or is empty. Recreating tools.")

# Create tools for each PDF if not loaded from cache
if not paper_to_tools_dict:
    for pdf in pdf_files:
        print(f"Getting tools for paper: {pdf}")
        vector_tool, summary_tool = get_doc_tools(pdf, Path(pdf).stem)
        paper_to_tools_dict[pdf] = [vector_tool, summary_tool]
    
    # Save tools to cache
    with open(tools_cache_path, 'wb') as f:
        pickle.dump(paper_to_tools_dict, f)


# Combine all tools into a single list
all_tools = [t for pdf in pdf_files for t in paper_to_tools_dict[pdf]]

# Define an object index and retriever over these tools
from llama_index.core import VectorStoreIndex
from llama_index.core.objects import ObjectIndex

obj_index = ObjectIndex.from_objects(
    all_tools,
    index_cls=VectorStoreIndex,
)

obj_retriever = obj_index.as_retriever(similarity_top_k=3)

# Initialize the OpenAI LLM
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")

# Set up the agent
from llama_index.core.agent import FunctionCallingAgentWorker
from llama_index.core.agent import AgentRunner

agent_worker = FunctionCallingAgentWorker.from_tools(
    tool_retriever=obj_retriever,
    llm=llm, 
    verbose=True
)
agent = AgentRunner(agent_worker)

# Define the function to query the agent
def ask_agent(question):
    response = agent.query(question)
    return str(response)

# Streamlit interface
st.title("EDS Research Agent")

question = st.text_input("Ask a question:")
if question:
    answer = ask_agent(question)
    st.write(answer)