app.py

import os
import streamlit as st
from openai import OpenAI
from PyPDF2 import PdfReader
from pinecone import Pinecone
import uuid
from dotenv import load_dotenv
import time
from concurrent.futures import ThreadPoolExecutor, as_completed

load_dotenv()

# Set up OpenAI client
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

# Set up Pinecone
pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))

index_name = "main"  # Your index name
index = pc.Index(index_name)

def get_embedding(text):
    response = client.embeddings.create(input=text, model="text-embedding-3-large")
    return response.data[0].embedding

def process_pdf(file):
    reader = PdfReader(file)
    text = ""
    for page in reader.pages:
        text += page.extract_text() + "\n"
    return text

def process_upload(upload_type, file_or_link, file_name=None):
    print(f"Starting process_upload for {upload_type}")
    doc_id = str(uuid.uuid4())
    print(f"Generated doc_id: {doc_id}")

    if upload_type == "PDF":
        content = process_pdf(file_or_link)
        doc_name = file_name or "Uploaded PDF"
    else:
        print("Invalid upload type")
        return "Invalid upload type"

    content_length = len(content)
    print(f"Content extracted, length: {content_length}")

    # Dynamically adjust chunk size based on content length
    if content_length < 10000:
        chunk_size = 1000
    elif content_length < 100000:
        chunk_size = 2000
    else:
        chunk_size = 4000
    print(f"Using chunk size: {chunk_size}")

    chunks = [content[i:i+chunk_size] for i in range(0, content_length, chunk_size)]
    
    vectors = []
    with ThreadPoolExecutor() as executor:
        futures = [executor.submit(process_chunk, chunk, doc_id, i, upload_type, doc_name) for i, chunk in enumerate(chunks)]
        
        for future in as_completed(futures):
            vectors.append(future.result())
            # Update progress
            progress = len(vectors) / len(chunks)
            st.session_state.upload_progress.progress(progress)
    
    print(f"Generated {len(vectors)} vectors")
    
    index.upsert(vectors=vectors)
    print("Vectors upserted to Pinecone")
    
    return f"Processing complete for {upload_type}. Document Name: {doc_name}"

def process_chunk(chunk, doc_id, i, upload_type, doc_name):
    embedding = get_embedding(chunk)
    return (f"{doc_id}_{i}", embedding, {
        "text": chunk, 
        "type": upload_type,
        "doc_id": doc_id,
        "doc_name": doc_name,
        "chunk_index": i
    })

def get_relevant_context(query, top_k=5):
    print(f"Getting relevant context for query: {query}")
    query_embedding = get_embedding(query)
    
    search_results = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)
    print(f"Found {len(search_results['matches'])} relevant results")
    
    # Sort results by doc_id and chunk_index to maintain document structure
    sorted_results = sorted(search_results['matches'], key=lambda x: (x['metadata']['doc_id'], x['metadata']['chunk_index']))
    
    context = "\n".join([result['metadata']['text'] for result in sorted_results])
    return context, sorted_results

def chat_with_ai(message):
    print(f"Chatting with AI, message: {message}")
    context, results = get_relevant_context(message)
    print(f"Retrieved context, length: {len(context)}")
    
    messages = [
        {"role": "system", "content": "You are a helpful assistant. Use the following information to answer the user's question, but don't mention the context directly in your response. If the information isn't in the context, say you don't know."},
        {"role": "system", "content": f"Context: {context}"},
        {"role": "user", "content": message}
    ]
    
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=messages
    )
    print("Received response from OpenAI")
    
    ai_response = response.choices[0].message.content
    
    # Prepare source information
    sources = [
        {
            "doc_id": result['metadata']['doc_id'],
            "doc_name": result['metadata']['doc_name'],
            "chunk_index": result['metadata']['chunk_index'],
            "text": result['metadata']['text'],
            "type": result['metadata']['type']
        }
        for result in results
    ]
    
    return ai_response, sources

def clear_database():
    print("Clearing database...")
    index.delete(delete_all=True)
    print("Database cleared")
    return "Database cleared successfully."

# Streamlit UI
st.set_page_config(layout="wide")
st.title("Upload and Chat with PDFs")

# Create three columns
col1, col2, col3 = st.columns([1, 1, 1])

with col1:
    st.header("Upload")
    
    # PDF upload
    uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
    
    if st.button("Process All"):
        st.session_state.upload_progress = st.progress(0)
        with st.spinner("Processing uploads..."):
            results = []
            if uploaded_file:
                pdf_result = process_upload("PDF", uploaded_file, uploaded_file.name)
                results.append(pdf_result)
            
            if results:
                for result in results:
                    st.success(result)
            else:
                st.warning("No content uploaded. Please provide at least one input.")
        st.session_state.upload_progress.empty()

    if st.button("Clear Database"):
        result = clear_database()
        st.success(result)

with col2:
    st.header("Chat")
    user_input = st.text_input("Ask a question about the uploaded content:")
    if st.button("Send"):
        if user_input:
            print(f"Sending user input: {user_input}")
            st.session_state.chat_progress = st.progress(0)
            response, sources = chat_with_ai(user_input)
            st.session_state.chat_progress.progress(1.0)
            st.markdown("**You:** " + user_input)
            st.markdown("**AI:** " + response)
            
            # Store sources in session state for display in col3
            st.session_state.sources = sources
            st.session_state.chat_progress.empty()
        else:
            print("Empty user input")
            st.warning("Please enter a question.")

with col3:
    st.header("Source Chunks")
    if 'sources' in st.session_state and st.session_state.sources:
        for i, source in enumerate(st.session_state.sources, 1):
            with st.expander(f"Source {i} - {source['type']} ({source['doc_name']})"):
                st.markdown(f"**Chunk Index:** {source['chunk_index']}")
                st.text(source['text'])
    else:
        st.info("Ask a question to see source chunks here.")