Update lab/metadata_issue_debugging_statements.py

lab/metadata_issue_debugging_statements.py

@@ -0,0 +1,260 @@
+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()
+
+
+# ----------------- 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 -----------------
+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"])