Spaces:
Configuration error
Configuration error
| import streamlit as st | |
| import os | |
| import tempfile | |
| from langchain_community.document_loaders import PyPDFLoader | |
| from groq_client import GroqClient | |
| from langchain_community.vectorstores import Chroma | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain_text_splitters import RecursiveCharacterTextSplitter | |
| from langchain.chains import create_retrieval_chain | |
| from langchain.chains.combine_documents import create_stuff_documents_chain | |
| from langchain_core.prompts import ChatPromptTemplate | |
| # --- PAGE CONFIGURATION --- | |
| st.set_page_config(page_title="GenAI Master Project", layout="wide") | |
| # --- SIDEBAR NAVIGATION --- | |
| st.sidebar.title("🤖 GenAI Project") | |
| st.sidebar.caption("Created by a Future GenAI Engineer") | |
| # Ab yahan 4 Options hain | |
| app_mode = st.sidebar.selectbox( | |
| "Choose a Feature:", | |
| ["🏠 Home", "💬 1. Simple Chatbot", "🔍 2. Document Analysis", "🧠 3. RAG System (Chat with PDF)"] | |
| ) | |
| # --- 1. HOME PAGE --- | |
| if app_mode == "🏠 Home": | |
| st.title("🚀 My Generative AI Project") | |
| st.markdown(""" | |
| ### Welcome to my Portfolio Project! | |
| This application demonstrates the complete lifecycle of a GenAI project. | |
| #### Modules Explained: | |
| - **💬 1. Simple Chatbot:** Basic AI Agent connecting to Gemma2:2b. | |
| - **🔍 2. Document Analysis:** Raw data extraction pipeline to read and understand PDFs. | |
| - **🧠 3. RAG System:** Advanced Retrieval-Augmented Generation to chat with documents. | |
| #### Tech Stack: | |
| - Python, Streamlit, LangChain, Ollama, ChromaDB. | |
| 👈 **Select a module from the Sidebar to start!** | |
| """) | |
| # --- 2. SIMPLE CHATBOT (Phase 1) --- | |
| elif app_mode == "💬 1. Simple Chatbot": | |
| st.header("💬 Simple Chatbot") | |
| st.caption("This model runs locally without internet.") | |
| if "chat_history" not in st.session_state: | |
| st.session_state.chat_history = [{"role": "assistant", "content": "Hi! I am Gemma. Ask me anything."}] | |
| for msg in st.session_state.chat_history: | |
| st.chat_message(msg["role"]).write(msg["content"]) | |
| if prompt := st.chat_input("Type your message..."): | |
| st.chat_message("user").write(prompt) | |
| st.session_state.chat_history.append({"role": "user", "content": prompt}) | |
| with st.chat_message("assistant"): | |
| with st.spinner("Thinking..."): | |
| try: | |
| llm = GroqClient(model="gemma2:2b") | |
| response = llm.invoke(prompt) | |
| st.write(response) | |
| st.session_state.chat_history.append({"role": "assistant", "content": response}) | |
| except Exception as e: | |
| st.error(f"Error: {e}. Make sure GROQ_API_KEY is set and endpoint reachable.") | |
| # --- 3. DOCUMENT ANALYSIS (Phase 2 - Wapas aa gaya!) --- | |
| elif app_mode == "🔍 2. Document Analysis": | |
| st.header("🔍 Document Analysis Engine") | |
| st.caption("This module extracts and analyzes raw text from PDFs (No Chat, Just Analysis).") | |
| uploaded_file = st.file_uploader("Upload a PDF for Analysis", type="pdf") | |
| if uploaded_file is not None: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file: | |
| temp_file.write(uploaded_file.read()) | |
| temp_file_path = temp_file.name | |
| if st.button("Analyze Document"): | |
| with st.spinner("Extracting Data..."): | |
| try: | |
| loader = PyPDFLoader(temp_file_path) | |
| docs = loader.load() | |
| st.success(f"Analysis Complete! Found {len(docs)} pages.") | |
| # Show Preview of Text | |
| st.subheader("📄 Extracted Content Preview:") | |
| for i, doc in enumerate(docs[:3]): # Sirf pehle 3 page dikhayega | |
| with st.expander(f"Page {i+1} Content"): | |
| st.write(doc.page_content) | |
| st.info("Note: This raw text is what sends to the LLM in the next stage (RAG).") | |
| except Exception as e: | |
| st.error(f"Error: {e}") | |
| # Cleanup | |
| try: | |
| os.remove(temp_file_path) | |
| except: | |
| pass | |
| # --- 4. RAG SYSTEM (Phase 3) --- | |
| elif app_mode == "🧠 3. RAG System (Chat with PDF)": | |
| st.header("🧠 RAG System (Chat with PDF)") | |
| st.caption("Upload a PDF and ask questions. The AI will answer ONLY from the document.") | |
| uploaded_file = st.file_uploader("Upload PDF for RAG", type="pdf") | |
| if "rag_history" not in st.session_state: | |
| st.session_state.rag_history = [] | |
| if uploaded_file is not None: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file: | |
| temp_file.write(uploaded_file.read()) | |
| temp_file_path = temp_file.name | |
| # Process PDF only once | |
| if "processed_file_rag" not in st.session_state or st.session_state.processed_file_rag != uploaded_file.name: | |
| with st.spinner("Creating Vector Embeddings..."): | |
| loader = PyPDFLoader(temp_file_path) | |
| docs = loader.load() | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) | |
| splits = text_splitter.split_documents(docs) | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| st.session_state.vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings) | |
| st.session_state.processed_file_rag = uploaded_file.name | |
| st.success("Database Ready! Ask your questions below.") | |
| # Chat Interface | |
| for msg in st.session_state.rag_history: | |
| st.chat_message(msg["role"]).write(msg["content"]) | |
| if user_question := st.chat_input("Ask about the PDF..."): | |
| st.chat_message("user").write(user_question) | |
| st.session_state.rag_history.append({"role": "user", "content": user_question}) | |
| with st.chat_message("assistant"): | |
| with st.spinner("Searching document..."): | |
| try: | |
| retriever = st.session_state.vectorstore.as_retriever() | |
| try: | |
| # Get relevant documents from retriever (best-effort) | |
| if hasattr(retriever, 'get_relevant_documents'): | |
| docs = retriever.get_relevant_documents(user_question) | |
| else: | |
| docs = retriever.retrieve(user_question) | |
| except Exception: | |
| # fallback: try similarity_search on the vectorstore | |
| try: | |
| docs = st.session_state.vectorstore.similarity_search(user_question, k=4) | |
| except Exception: | |
| docs = [] | |
| contexts = [] | |
| for d in docs: | |
| content = getattr(d, 'page_content', None) or getattr(d, 'content', None) or str(d) | |
| contexts.append(content) | |
| llm = GroqClient(model="gemma2:2b") | |
| answer = llm.generate_from_context(contexts, user_question) | |
| st.write(answer) | |
| st.session_state.rag_history.append({"role": "assistant", "content": answer}) | |
| except Exception as e: | |
| st.error(f"Error: {e}") | |
| try: | |
| os.remove(temp_file_path) | |
| except: | |
| pass |