app.py

import streamlit as st
import os
import requests
import chromadb
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, SequentialChain
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", layout="centered")
st.title("Blah-1")

# ----------------- API Keys -----------------
os.environ["GROQ_API_KEY"] = st.secrets.get("GROQ_API_KEY", "")

# ----------------- Clear ChromaDB Cache -----------------
chromadb.api.client.SharedSystemClient.clear_system_cache()

# ----------------- 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

# ----------------- Load Models -----------------
llm_judge = ChatGroq(model="deepseek-r1-distill-llama-70b")
rag_llm = ChatGroq(model="mixtral-8x7b-32768")

# Enable verbose logging for debugging
llm_judge.verbose = True
rag_llm.verbose = True

# ----------------- PDF Selection -----------------
#st.subheader("PDF Selection")
pdf_source = st.radio("Choose a PDF source:", ["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 = "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 = "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)

        # 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})

        # Prevent unnecessary re-chunking
        if not st.session_state.chunked:
            text_splitter = SemanticChunker(embedding_model)
            document_chunks = text_splitter.split_documents(docs)
            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(
            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.text("\n-------- 🟥 context_relevancy_evaluation_chain Statement 🟥 --------\n")
    st.json(final_response["relevancy_response"])

    st.text("\n-------- 🟦 pick_relevant_context_chain Statement 🟦 --------\n")
    st.json(final_response["context_number"])

    st.text("\n-------- 🟥 relevant_contexts_chain Statement 🟥 --------\n")
    st.json(final_response["relevant_contexts"])

    st.text("\n-------- 🟥 Rag Response Statement 🟥 --------\n")
    st.json(final_response["final_response"])