Update lab/metadata_issue_debugging_statements.py

lab/metadata_issue_debugging_statements.py

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-Update app.py
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-import streamlit as st
-import os
-import json
-import requests
-import pdfplumber
-import chromadb
-import re
-from langchain.document_loaders import PDFPlumberLoader
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_experimental.text_splitter import SemanticChunker
-from langchain_chroma import Chroma
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain_groq import ChatGroq
-from prompts import rag_prompt, relevancy_prompt, relevant_context_picker_prompt, response_synth
-
-# ----------------- Streamlit UI Setup -----------------
-st.set_page_config(page_title="Blah-1", layout="centered")
-
-# ----------------- API Keys -----------------
-os.environ["GROQ_API_KEY"] = st.secrets.get("GROQ_API_KEY", "")
-
-# Load LLM models
-llm_judge = ChatGroq(model="deepseek-r1-distill-llama-70b")
-rag_llm = ChatGroq(model="mixtral-8x7b-32768")
-
-llm_judge.verbose = True
-rag_llm.verbose = True
-
-# Clear ChromaDB cache to fix tenant issue
-chromadb.api.client.SharedSystemClient.clear_system_cache()
-
-# st.title("Blah")
-
-# ----------------- ChromaDB Persistent Directory -----------------
-CHROMA_DB_DIR = "/mnt/data/chroma_db" 
-os.makedirs(CHROMA_DB_DIR, exist_ok=True)
-
-# ----------------- Initialize Session State -----------------
-if "pdf_loaded" not in st.session_state:
-    st.session_state.pdf_loaded = False
-if "chunked" not in st.session_state:
-    st.session_state.chunked = False
-if "vector_created" not in st.session_state:
-    st.session_state.vector_created = False
-if "processed_chunks" not in st.session_state:
-    st.session_state.processed_chunks = None
-if "vector_store" not in st.session_state:
-    st.session_state.vector_store = None
-
-# ----------------- Metadata Extraction -----------------
-# ----------------- Metadata Extraction -----------------
-def extract_metadata_llm(pdf_path):
-    """Extracts metadata using LLM instead of regex and logs progress in Streamlit UI."""
-    
-    with pdfplumber.open(pdf_path) as pdf:
-        first_page_text = pdf.pages[0].extract_text() or "No text found." if pdf.pages else "No text found."
-
-    # Streamlit Debugging: Show extracted text
-    st.subheader("📄 Extracted First Page Text for Metadata")
-    st.text_area("First Page Text:", first_page_text, height=200)
-
-    # Define metadata prompt
-    metadata_prompt = PromptTemplate(
-        input_variables=["text"],
-        template="""
-        Given the following first page of a research paper, extract metadata **strictly in JSON format**.
-        - If no data is found for a field, return `"Unknown"` instead.
-        - Ensure the output is valid JSON (do not include markdown syntax).
-        
-        Example output:
-        {
-            "Title": "Example Paper Title",
-            "Author": "John Doe, Jane Smith",
-            "Emails": "[email protected], [email protected]",
-            "Affiliations": "School of AI, University of Example"
-        }
-        
-        Now, extract the metadata from this document:
-        {text}
-        """
-    )
-
-    # Run LLM Metadata Extraction
-    metadata_chain = LLMChain(llm=llm_judge, prompt=metadata_prompt, output_key="metadata")
-
-    # Debugging: Log the LLM input
-    st.subheader("🔍 LLM Input for Metadata Extraction")
-    st.json({"text": first_page_text})
-
-    try:
-        metadata_response = metadata_chain.invoke({"text": first_page_text})
-        
-        # Debugging: Log raw LLM response
-        st.subheader("🔍 Raw LLM Response")
-        st.json(metadata_response)
-
-        # Handle JSON extraction from LLM response
-        try:
-            metadata_dict = json.loads(metadata_response["metadata"])
-        except json.JSONDecodeError:
-            try:
-                # Attempt to clean up JSON if needed
-                metadata_dict = json.loads(metadata_response["metadata"].strip("```json\n").strip("\n```"))
-            except json.JSONDecodeError:
-                metadata_dict = {
-                    "Title": "Unknown",
-                    "Author": "Unknown",
-                    "Emails": "No emails found",
-                    "Affiliations": "No affiliations found"
-                }
-        
-    except Exception as e:
-        st.error(f"❌ LLM Metadata Extraction Failed: {e}")
-        metadata_dict = {
-            "Title": "Unknown",
-            "Author": "Unknown",
-            "Emails": "No emails found",
-            "Affiliations": "No affiliations found"
-        }
-
-    # Ensure all required fields exist
-    required_fields = ["Title", "Author", "Emails", "Affiliations"]
-    for field in required_fields:
-        metadata_dict.setdefault(field, "Unknown")
-
-    # Streamlit Debugging: Display Final Extracted Metadata
-    st.subheader("✅ Extracted Metadata")
-    st.json(metadata_dict)
-
-    return metadata_dict
-
-
-# ----------------- Step 1: Choose PDF Source -----------------
-pdf_source = st.radio("Upload or provide a link to a PDF:", ["Upload a PDF file", "Enter a PDF URL"], index=0, horizontal=True)
-
-if pdf_source == "Upload a PDF file":
-    uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])
-    if uploaded_file:
-        st.session_state.pdf_path = "/mnt/data/temp.pdf"
-        with open(st.session_state.pdf_path, "wb") as f:
-            f.write(uploaded_file.getbuffer())
-        st.session_state.pdf_loaded = False
-        st.session_state.chunked = False
-        st.session_state.vector_created = False
-
-elif pdf_source == "Enter a PDF URL":
-    pdf_url = st.text_input("Enter PDF URL:")
-    if pdf_url and not st.session_state.pdf_loaded:
-        with st.spinner("🔄 Downloading PDF..."):
-            try:
-                response = requests.get(pdf_url)
-                if response.status_code == 200:
-                    st.session_state.pdf_path = "/mnt/data/temp.pdf"
-                    with open(st.session_state.pdf_path, "wb") as f:
-                        f.write(response.content)
-                    st.session_state.pdf_loaded = False
-                    st.session_state.chunked = False
-                    st.session_state.vector_created = False
-                    st.success("✅ PDF Downloaded Successfully!")
-                else:
-                    st.error("❌ Failed to download PDF. Check the URL.")
-            except Exception as e:
-                st.error(f"Error downloading PDF: {e}")
-
-
-# ----------------- Process PDF -----------------
-if not st.session_state.pdf_loaded and "pdf_path" in st.session_state:
-    with st.spinner("🔄 Processing document... Please wait."):
-        loader = PDFPlumberLoader(st.session_state.pdf_path)
-        docs = loader.load()
-        st.json(docs[0].metadata)
-
-        # Extract metadata
-        metadata = extract_metadata_llm(st.session_state.pdf_path)
-
-        # Display extracted-metadata
-        if isinstance(metadata, dict):
-            st.subheader("📄 Extracted Document Metadata")
-            st.write(f"**Title:** {metadata.get('Title', 'Unknown')}")
-            st.write(f"**Author:** {metadata.get('Author', 'Unknown')}")
-            st.write(f"**Emails:** {metadata.get('Emails', 'No emails found')}")
-            st.write(f"**Affiliations:** {metadata.get('Affiliations', 'No affiliations found')}")
-        else:
-            st.error("Metadata extraction failed. Check the LLM response format.")
-
-        # Embedding Model
-        model_name = "nomic-ai/modernbert-embed-base"
-        embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={"device": "cpu"}, encode_kwargs={'normalize_embeddings': False})
-
-        # Convert metadata into a retrievable chunk
-        metadata_doc = {"page_content": metadata, "metadata": {"source": "metadata"}}
-
-
-        # Prevent unnecessary re-chunking
-        if not st.session_state.chunked:
-            text_splitter = SemanticChunker(embedding_model)
-            document_chunks = text_splitter.split_documents(docs)
-            document_chunks.insert(0, metadata_doc)  # Insert metadata as a retrievable document
-            st.session_state.processed_chunks = document_chunks
-            st.session_state.chunked = True
-
-        st.session_state.pdf_loaded = True
-        st.success("✅ Document processed and chunked successfully!")
-
-# ----------------- Setup Vector Store -----------------
-if not st.session_state.vector_created and st.session_state.processed_chunks:
-    with st.spinner("🔄 Initializing Vector Store..."):
-        st.session_state.vector_store = Chroma(
-            persist_directory=CHROMA_DB_DIR,  # <-- Ensures persistence
-            collection_name="deepseek_collection",
-            collection_metadata={"hnsw:space": "cosine"},
-            embedding_function=embedding_model
-        )
-        st.session_state.vector_store.add_documents(st.session_state.processed_chunks)
-        st.session_state.vector_created = True
-        st.success("✅ Vector store initialized successfully!")
-
-
-# ----------------- Query Input -----------------
-query = st.text_input("🔍 Ask a question about the document:")
-
-if query:
-    with st.spinner("🔄 Retrieving relevant context..."):
-        retriever = st.session_state.vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
-        retrieved_docs = retriever.invoke(query)
-        context = [d.page_content for d in retrieved_docs]
-        st.success("✅ Context retrieved successfully!")
-
-    # ----------------- Run Individual Chains Explicitly -----------------
-    context_relevancy_chain = LLMChain(llm=llm_judge, prompt=PromptTemplate(input_variables=["retriever_query", "context"], template=relevancy_prompt), output_key="relevancy_response")
-    relevant_context_chain = LLMChain(llm=llm_judge, prompt=PromptTemplate(input_variables=["relevancy_response"], template=relevant_context_picker_prompt), output_key="context_number")
-    relevant_contexts_chain = LLMChain(llm=llm_judge, prompt=PromptTemplate(input_variables=["context_number", "context"], template=response_synth), output_key="relevant_contexts")
-    response_chain = LLMChain(llm=rag_llm, prompt=PromptTemplate(input_variables=["query", "context"], template=rag_prompt), output_key="final_response")
-
-    response_crisis = context_relevancy_chain.invoke({"context": context, "retriever_query": query})
-    relevant_response = relevant_context_chain.invoke({"relevancy_response": response_crisis["relevancy_response"]})
-    contexts = relevant_contexts_chain.invoke({"context_number": relevant_response["context_number"], "context": context})
-    final_response = response_chain.invoke({"query": query, "context": contexts["relevant_contexts"]})
-
-    # ----------------- Display All Outputs -----------------
-    st.markdown("### Context Relevancy Evaluation")
-    st.json(response_crisis["relevancy_response"])
-
-    st.markdown("### Picked Relevant Contexts")
-    st.json(relevant_response["context_number"])
-
-    st.markdown("### Extracted Relevant Contexts")
-    st.json(contexts["relevant_contexts"])
-
-    st.subheader("context_relevancy_evaluation_chain Statement")
-    st.json(final_response["relevancy_response"])
-
-    st.subheader("pick_relevant_context_chain Statement")
-    st.json(final_response["context_number"])
-
-    st.subheader("relevant_contexts_chain Statement")
-    st.json(final_response["relevant_contexts"])
-
-    st.subheader("RAG Response Statement")
-    st.json(final_response["final_response"])
-