Create app1.py

app1.py

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+import os
+import requests
+import streamlit as st
+import pickle
+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, relevancy_prompt, relevant_context_picker_prompt, response_synth
+
+# Set 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
+
+VECTOR_DB_PATH = "/tmp/chroma_db"  
+CHUNKS_FILE = "/tmp/chunks.pkl"  
+
+# Session State Initialization
+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
+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
+
+st.title("Blah-2")
+
+# 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 = "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_path:
+        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}")
+
+# Step 2: Load & Process PDF (Only Once)
+if st.session_state.pdf_path and not st.session_state.pdf_loaded:
+    with st.spinner("Loading PDF..."):
+        try:
+            loader = PDFPlumberLoader(st.session_state.pdf_path)
+            docs = loader.load()
+            st.session_state.documents = docs
+            st.session_state.pdf_loaded = True
+            st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
+        except Exception as e:
+            st.error(f"❌ Error processing PDF: {e}")
+
+# Load Cached Chunks if Available
+def load_chunks():
+    if os.path.exists(CHUNKS_FILE):
+        with open(CHUNKS_FILE, "rb") as f:
+            return pickle.load(f)
+    return None
+
+if not st.session_state.chunked:  # Ensure chunking only happens once
+    cached_chunks = load_chunks()
+    if cached_chunks:
+        st.session_state.documents = cached_chunks
+        st.session_state.chunked = True
+
+# Step 3: Chunking (Only Happens Once)
+if st.session_state.pdf_loaded and not st.session_state.chunked:
+    with st.spinner("Chunking the document..."):
+        try:
+            model_name = "nomic-ai/modernbert-embed-base"
+            embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
+            text_splitter = SemanticChunker(embedding_model)
+            
+            if st.session_state.documents:
+                documents = text_splitter.split_documents(st.session_state.documents)
+                st.session_state.documents = documents
+                st.session_state.chunked = True
+
+                # Save chunks for persistence
+                with open(CHUNKS_FILE, "wb") as f:
+                    pickle.dump(documents, f)
+
+                st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
+        except Exception as e:
+            st.error(f"❌ Error chunking document: {e}")
+
+# Step 4: Setup Vectorstore 
+def load_vector_store():
+    return Chroma(persist_directory=VECTOR_DB_PATH, collection_name="deepseek_collection", embedding_function=HuggingFaceEmbeddings(model_name="nomic-ai/modernbert-embed-base"))
+
+if st.session_state.chunked and not st.session_state.vector_created:
+    with st.spinner("Creating vector store..."):
+        try:
+            if st.session_state.vector_store is None:  # Prevent unnecessary reloading
+                st.session_state.vector_store = load_vector_store()
+
+            if len(st.session_state.vector_store.get()["documents"]) == 0:  # Prevent duplicate insertions
+                st.session_state.vector_store.add_documents(st.session_state.documents)
+
+            num_documents = len(st.session_state.vector_store.get()["documents"])
+            st.session_state.vector_created = True
+            st.success(f"✅ **Vector Store Created!** Total documents stored: {num_documents}")
+        except Exception as e:
+            st.error(f"❌ Error creating vector store: {e}")
+
+# Debugging Logs
+st.write("📄 **PDF Loaded:**", st.session_state.pdf_loaded)
+st.write("🔹 **Chunked:**", st.session_state.chunked)
+st.write("📂 **Vector Store Created:**", st.session_state.vector_created)
+
+
+# ----------------- 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})
+        contexts = retriever.invoke(query)
+        # Debugging: Check what was retrieved
+        st.write("Retrieved Contexts:", contexts)
+        st.write("Number of Contexts:", len(contexts))
+        
+        context = [d.page_content for d in contexts]
+        # Debugging: Check extracted context
+        st.write("Extracted Context (page_content):", context)
+        st.write("Number of Extracted Contexts:", len(context))
+
+
+        ------