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| import streamlit as st | |
| import requests | |
| import uuid | |
| # 1. PAGE CONFIGURATION | |
| st.set_page_config(page_title="Enterprise RAG Assistant", page_icon="🤖", layout="centered") | |
| st.title("📚 Enterprise Document Assistant") | |
| st.markdown("Upload a PDF to the knowledge base and ask questions about it.") | |
| # 2. SESSION STATE INITIALIZATION (The Memory Bank) | |
| if "user_id" not in st.session_state: | |
| st.session_state.user_id = str(uuid.uuid4()) | |
| if "thread_id" not in st.session_state: | |
| st.session_state.thread_id = str(uuid.uuid4()) | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| # 3. SIDEBAR: PDF UPLOAD (The Handoff to FastAPI) | |
| with st.sidebar: | |
| st.header("Document Ingestion") | |
| uploaded_file = st.file_uploader("Upload a PDF", type="pdf") | |
| if st.button("Process Document"): | |
| if uploaded_file: | |
| with st.spinner("Transmitting to backend..."): | |
| # Package the file as multipart/form-data | |
| files = {"file": (uploaded_file.name, uploaded_file.getvalue(), "application/pdf")} | |
| payload_data = {"user_id": st.session_state.user_id} | |
| # Send the POST request to your local FastAPI server | |
| try: | |
| response = requests.post( | |
| "https://lightrt-pdf-rag.hf.space/upload", | |
| files=files, | |
| data=payload_data | |
| ) | |
| if response.status_code == 200: | |
| st.success("File uploaded! The AI is reading it in the background.") | |
| else: | |
| st.error(f"Upload failed: {response.text}") | |
| except requests.exceptions.ConnectionError: | |
| st.error("Cannot connect to backend. Is FastAPI running?") | |
| else: | |
| st.warning("Please select a file first.") | |
| # 4. CHAT HISTORY RENDERING | |
| for msg in st.session_state.messages: | |
| # This creates a chat bubble. role is either 'user' or 'assistant' | |
| with st.chat_message(msg["role"]): | |
| st.markdown(msg["content"]) | |
| # 5. CHAT INPUT & BACKEND COMMUNICATION | |
| if prompt := st.chat_input("Ask a question about your documents..."): | |
| # Immediately render the user's new message to the UI | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Show a loading indicator while we wait for FastAPI and LangGraph | |
| with st.chat_message("assistant"): | |
| message_placeholder = st.empty() | |
| message_placeholder.markdown("*(Thinking...)*") | |
| # Prepare the JSON payload for FastAPI | |
| payload = { | |
| "message": prompt, | |
| "user_id": st.session_state.user_id, | |
| "thread_id": st.session_state.thread_id | |
| } | |
| try: | |
| # Send the question to your LangGraph backend | |
| chat_response = requests.post("https://lightrt-pdf-rag.hf.space/chat", json=payload) | |
| if chat_response.status_code == 200: | |
| # Extract the answer from the JSON response | |
| answer = chat_response.json().get("response", "No response found.") | |
| # Update the UI placeholder with the actual answer | |
| message_placeholder.markdown(answer) | |
| # Save the AI's answer to the session state memory | |
| st.session_state.messages.append({"role": "assistant", "content": answer}) | |
| else: | |
| message_placeholder.error(f"Error: {chat_response.text}") | |
| except requests.exceptions.ConnectionError: | |
| message_placeholder.error("Cannot connect to backend. Is FastAPI running?") |