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| import streamlit as st | |
| import requests | |
| import uuid | |
| import json | |
| # 🌐 BACKEND CONFIGURATION | |
| BACKEND_URL = "http://localhost:8000" # Update this to your FastAPI backend URL if different | |
| st.set_page_config( | |
| page_title="Enterprise Multi-Tenant FAQ Bot", | |
| page_icon="🤖", | |
| layout="wide" | |
| ) | |
| # 🔑 SESSION STATE INITIALIZATION | |
| if "session_id" not in st.session_state: | |
| # Generate a unique session token for this browser tab | |
| st.session_state.session_id = str(uuid.uuid4()) | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| if "active_sources" not in st.session_state: | |
| st.session_state.active_sources = None | |
| # 🗂️ SIDEBAR: Workspace Management & File Uploads | |
| with st.sidebar: | |
| st.title("⚙️ Workspace Panel") | |
| # Display the current isolated session ID | |
| st.info(f"**Active Session Partition:**\n`{st.session_state.session_id}`") | |
| st.caption("All document uploads and chat history are securely sandboxed inside this unique session token.") | |
| st.markdown("---") | |
| # File Uploader Widget | |
| st.subheader("📥 Ingest Dynamic Context") | |
| uploaded_file = st.file_uploader( | |
| "Upload a .txt or .pdf file to train the bot for this session:", | |
| type=["txt", "pdf"] | |
| ) | |
| if uploaded_file is not None: | |
| if st.button("🚀 Process & Vectorize Document", use_container_width=True): | |
| with st.spinner("Streaming chunks to NVIDIA Embedding pipeline..."): | |
| try: | |
| # Prepare multipart form file payload | |
| files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)} | |
| headers = {"X-Session-ID": st.session_state.session_id} | |
| # Call FastAPI dynamic ingestion endpoint | |
| response = requests.post( | |
| f"{BACKEND_URL}/api/v1/upload", | |
| files=files, | |
| headers=headers | |
| ) | |
| if response.status_code == 200: | |
| st.success(f"✅ Context parsed! {uploaded_file.name} is now live in your workspace.") | |
| else: | |
| error_detail = response.json().get('detail', 'Unknown error') | |
| st.error(f"❌ Ingestion Failed: {error_detail}") | |
| except Exception as e: | |
| st.error(f"❌ Connection error: {str(e)}") | |
| st.markdown("---") | |
| # Reset/Clear Options | |
| st.subheader("🧹 Workspace Cleanup") | |
| if st.button("Clear Chat Window State", use_container_width=True): | |
| st.session_state.messages = [] | |
| st.session_state.active_sources = None | |
| st.rerun() | |
| if st.button("🗑️ Wipe Global Vector DB (Admin)", use_container_width=True, type="secondary"): | |
| with st.spinner("Dropping collection..."): | |
| try: | |
| res = requests.post(f"{BACKEND_URL}/api/v1/clear") | |
| if res.status_code == 200: | |
| st.warning("💥 Global Vector Store wiped entirely.") | |
| else: | |
| st.error("Failed to clear DB.") | |
| except Exception as e: | |
| st.error(f"Error: {e}") | |
| # 💬 MAIN INTERFACE: Streaming Chat Engine | |
| st.title("🤖 Enterprise Multi-Tenant FAQ Bot") | |
| st.markdown("Ask general questions, standard FAQs, or query details out of your uploaded dynamic documents.") | |
| # 📜 Render Existing Chat Log | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # ⚡ Live User Interaction Loop | |
| if prompt := st.chat_input("Type your message here..."): | |
| # Display user input inside the panel | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Store message in state history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # Build a clean conversion format payload for history matching | |
| formatted_history = [ | |
| {"role": msg["role"], "role": "assistant" if msg["role"] == "assistant" else "user", "content": msg["content"]} | |
| for msg in st.session_state.messages[:-1] | |
| ] | |
| # Setup the JSON request body | |
| chat_payload = { | |
| "question": prompt, | |
| "history": formatted_history, | |
| "session_id": st.session_state.session_id | |
| } | |
| # Stream the incoming chunks from FastAPI | |
| with st.chat_message("assistant"): | |
| response_placeholder = st.empty() | |
| full_response = "" | |
| sources_found = [] | |
| try: | |
| # Open persistent stream connection to FastAPI router | |
| with requests.post(f"{BACKEND_URL}/api/v1/chat", json=chat_payload, stream=True) as response: | |
| if response.status_code == 500: | |
| st.error("The streaming backend pipeline returned a fatal exception.") | |
| for line in response.iter_lines(): | |
| if line: | |
| # Decode raw byte lines from incoming NDJSON data stream | |
| decoded_line = line.decode('utf-8') | |
| chunk_data = json.loads(decoded_line) | |
| # 📦 Handle Retrieved Data Sources | |
| if chunk_data.get("type") == "sources": | |
| sources_found = chunk_data.get("content", []) | |
| # ⚡ Handle Live Text Tokens | |
| elif chunk_data.get("type") == "token": | |
| full_response += chunk_data.get("content", "") | |
| # Live redraw token progression | |
| response_placeholder.markdown(full_response + "▌") | |
| # Lock final markdown block state rendering without cursor character | |
| response_placeholder.markdown(full_response) | |
| # Render Source Documents in an Accordion if present | |
| if sources_found: | |
| with st.expander("📚 View Retrieved Reference Sources"): | |
| for idx, src in enumerate(sources_found): | |
| src_name = src.get("metadata", {}).get("source", "Global Base FAQ") | |
| st.markdown(f"**Source [{idx+1}]:** `{src_name}`") | |
| st.caption(src.get("content", "")) | |
| # Save assistant milestone string state safely | |
| st.session_state.messages.append({"role": "assistant", "content": full_response}) | |
| except Exception as conn_err: | |
| st.error(f"Streaming error or dropped server connection: {str(conn_err)}") |