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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()
# ----------------- 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"])
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