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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="wide")
st.image("https://huggingface.co/front/assets/huggingface_logo-noborder.svg", width=150)
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 (Upload or URL) -----------------
st.sidebar.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.sidebar.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.sidebar.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()
# Embedding Model (HF on CPU)
model_name = "nomic-ai/modernbert-embed-base"
embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={"device": "cpu"})
# 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!")
# ----------------- Full SequentialChain Execution -----------------
with st.spinner("🔄 Running full pipeline..."):
final_output = SequentialChain(
chains=[
LLMChain(llm=llm_judge, prompt=PromptTemplate(input_variables=["retriever_query", "context"], template=relevancy_prompt), output_key="relevancy_response"),
LLMChain(llm=llm_judge, prompt=PromptTemplate(input_variables=["relevancy_response"], template=relevant_context_picker_prompt), output_key="context_number"),
LLMChain(llm=llm_judge, prompt=PromptTemplate(input_variables=["context_number", "context"], template=response_synth), output_key="relevant_contexts"),
LLMChain(llm=rag_llm, prompt=PromptTemplate(input_variables=["query", "context"], template=rag_prompt), output_key="final_response")
],
input_variables=["context", "retriever_query", "query"],
output_variables=["relevancy_response", "context_number", "relevant_contexts", "final_response"]
).invoke({"context": context, "retriever_query": query, "query": query})
# ----------------- Display All Outputs -----------------
st.subheader("🟥 Context Relevancy Evaluation")
st.json(final_output["relevancy_response"])
st.subheader("🟦 Picked Relevant Contexts")
st.json(final_output["context_number"])
st.subheader("🟥 Extracted Relevant Contexts")
st.json(final_output["relevant_contexts"])
st.subheader("🟥 RAG Final Response")
st.write(final_output["final_response"])
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