Update app.py

app.py

@@ -1,15 +1,4 @@
-import os
-import chromadb
-import requests
-import streamlit as st
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain_groq import ChatGroq
-from langchain.document_loaders import PDFPlumberLoader
-from langchain_experimental.text_splitter import SemanticChunker
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_chroma import Chroma
-from prompts import rag_prompt
+
 
 # Set API Keys
 os.environ["GROQ_API_KEY"] = st.secrets.get("GROQ_API_KEY", "")
@@ -26,21 +15,7 @@ chromadb.api.client.SharedSystemClient.clear_system_cache()
 
 st.title("Blah - 1")
 
-# **Initialize session state variables**
-if "pdf_path" not in st.session_state:
-    st.session_state.pdf_path = None  
-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 "vector_store_path" not in st.session_state:
-    st.session_state.vector_store_path = "./chroma_langchain_db"
-if "vector_store" not in st.session_state:
-    st.session_state.vector_store = None
-if "documents" not in st.session_state:
-    st.session_state.documents = None
+
 
 # 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)
@@ -74,62 +49,3 @@ elif pdf_source == "Enter a PDF URL":
             except Exception as e:
                 st.error(f"Error downloading PDF: {e}")
 
-# Step 2: Process PDF 
-if st.session_state.pdf_path and not st.session_state.get("pdf_loaded", False):
-    with st.spinner("Loading and processing PDF..."):
-        loader = PDFPlumberLoader(st.session_state.pdf_path)
-        docs = loader.load()
-        st.session_state.documents = docs
-        st.session_state.pdf_loaded = True  # ✅ Prevent re-loading
-        st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
-
-# Step 3: Chunking 
-if st.session_state.get("pdf_loaded", False) and not st.session_state.get("chunked", False):
-    with st.spinner("Chunking the document..."):
-        model_name = "nomic-ai/modernbert-embed-base"
-        embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': False})
-        text_splitter = SemanticChunker(embedding_model)
-        documents = text_splitter.split_documents(st.session_state.documents)
-        st.session_state.documents = documents  # ✅ Store chunked docs
-        st.session_state.chunked = True  # ✅ Prevent re-chunking
-        st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
-
-# Step 4: Setup Vectorstore 
-if st.session_state.get("chunked", False) and not st.session_state.get("vector_created", False):
-    with st.spinner("Creating vector store..."):
-        embedding_model = HuggingFaceEmbeddings(model_name="nomic-ai/modernbert-embed-base", model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': False})
-        
-        vector_store = Chroma(
-            collection_name="deepseek_collection",
-            collection_metadata={"hnsw:space": "cosine"},
-            embedding_function=embedding_model,
-            persist_directory=st.session_state.vector_store_path
-        )
-        vector_store.add_documents(st.session_state.documents)
-        num_documents = len(vector_store.get()["documents"])
-        st.session_state.vector_store = vector_store
-        st.session_state.vector_created = True  # ✅ Prevent re-creating vector store
-        st.success(f"✅ **Vector Store Created!** Total documents stored: {num_documents}")
-
-# Step 5: Query Input 
-if st.session_state.get("vector_created", False) and st.session_state.get("vector_store", None):
-    query = st.text_input("🔍 Enter a Query:")
-
-    if query and st.session_state.get("vector_created", False):
-        with st.spinner("Retrieving relevant contexts..."):
-            retriever = st.session_state.vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
-            contexts = retriever.invoke(query)
-            context_texts = [doc.page_content for doc in contexts]
-
-        st.success(f"✅ **Retrieved {len(context_texts)} Contexts!**")
-        for i, text in enumerate(context_texts, 1):
-            st.write(f"**Context {i}:** {text[:500]}...")
-
-        # **Step 6: Generate Final Response**
-        with st.spinner("Generating the final answer..."):
-            final_prompt = PromptTemplate(input_variables=["query", "context"], template=rag_prompt)
-            response_chain = LLMChain(llm=rag_llm, prompt=final_prompt, output_key="final_response")
-            final_response = response_chain.invoke({"query": query, "context": context_texts})
-
-        st.subheader("🟥 RAG Final Response")
-        st.success(final_response['final_response'])