initial

.gitignore

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+.env

app.py

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+import os
+import streamlit as st
+from openai import OpenAI
+from PyPDF2 import PdfReader
+import requests
+from youtube_transcript_api import YouTubeTranscriptApi
+from urllib.parse import urlparse, parse_qs
+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-ada-002")
+    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_web_link(url):
+    response = requests.get(url)
+    return response.text
+
+def process_youtube_link(url):
+    video_id = extract_video_id(url)
+    transcript = YouTubeTranscriptApi.get_transcript(video_id)
+    return " ".join([entry['text'] for entry in transcript])
+
+def extract_video_id(url):
+    parsed_url = urlparse(url)
+    if parsed_url.hostname == 'youtu.be':
+        return parsed_url.path[1:]
+    if parsed_url.hostname in ('www.youtube.com', 'youtube.com'):
+        if parsed_url.path == '/watch':
+            return parse_qs(parsed_url.query)['v'][0]
+        if parsed_url.path[:7] == '/embed/':
+            return parsed_url.path.split('/')[2]
+        if parsed_url.path[:3] == '/v/':
+            return parsed_url.path.split('/')[2]
+    return None
+
+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"
+    elif upload_type == "Web Link":
+        content = process_web_link(file_or_link)
+        doc_name = file_or_link
+    elif upload_type == "YouTube Link":
+        content = process_youtube_link(file_or_link)
+        doc_name = f"YouTube: {file_or_link}"
+    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)
+    truncated_embedding = embedding[:200]
+    return (f"{doc_id}_{i}", truncated_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)
+    truncated_query_embedding = query_embedding[:200]
+    
+    search_results = index.query(vector=truncated_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, Web Links, and YouTube Videos")
+
+# 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")
+    
+    # Web Link input
+    web_link = st.text_input("Enter a Web Link")
+    
+    # YouTube Link input
+    youtube_link = st.text_input("Enter a YouTube Link")
+    
+    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 web_link:
+                web_result = process_upload("Web Link", web_link)
+                results.append(web_result)
+            if youtube_link:
+                youtube_result = process_upload("YouTube Link", youtube_link)
+                results.append(youtube_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.")

requirements.txt

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+gradio
+openai
+PyPDF2
+requests
+youtube_transcript_api
+pinecone-client
+python-dotenv