app.py

import streamlit as st
import os
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

# ----------------- Improved Metadata Extraction -----------------
def extract_metadata(pdf_path):
    """Extracts title, author, emails, and affiliations from PDF."""
    with pdfplumber.open(pdf_path) as pdf:
        metadata = pdf.metadata or {}

        # Extract title
        title = metadata.get("Title", "").strip()
        if not title and pdf.pages:
            text = pdf.pages[0].extract_text()
            title_match = re.search(r"(?i)title[:\-]?\s*(.*)", text or "")
            title = title_match.group(1) if title_match else text.split("\n")[0] if text else "Untitled Document"

        # Extract author
        author = metadata.get("Author", "").strip()
        if not author and pdf.pages:
            author_match = re.search(r"(?i)by\s+([A-Za-z\s,]+)", pdf.pages[0].extract_text() or "")
            author = author_match.group(1).strip() if author_match else "Unknown Author"

        # Extract emails
        emails = re.findall(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", pdf.pages[0].extract_text() or "")
        email_str = ", ".join(emails) if emails else "No emails found"

        # Extract affiliations
        affiliations = re.findall(r"(?:Department|Faculty|Institute|University|College|School)\s+[\w\s]+", pdf.pages[0].extract_text() or "")
        affiliation_str = ", ".join(affiliations) if affiliations else "No affiliations found"

    return title, author, email_str, affiliation_str

# ----------------- 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
        title, author, email_str, affiliation_str = extract_metadata(st.session_state.pdf_path)

        # Display extracted metadata
        st.subheader("📄 Extracted Document Metadata")
        st.write(f"**Title:** {title}")
        st.write(f"**Author:** {author}")
        st.write(f"**Emails:** {email_str}")
        st.write(f"**Affiliations:** {affiliation_str}")

        # 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_text = f"Title: {title}\nAuthor: {author}\nEmails: {email_str}\nAffiliations: {affiliation_str}"
        metadata_doc = {"page_content": metadata_text, "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.markdown("### RAG Final Response")
    st.write(final_response["final_response"])

    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"])