import os from pathlib import Path # Disable Chroma telemetry (optional) os.environ["CHROMA_TELEMETRY_ENABLED"] = "false" # Check if DB exists, else build DB_DIR = Path(__file__).parent / "db" if not DB_DIR.exists() or not any(DB_DIR.iterdir()): print("šŸ“¦ No DB found. Building vectorstore...") import scripts.load_documents import scripts.chunk_and_embed import scripts.setup_vectorstore else: print("āœ… DB found. Skipping build.") import gradio as gr from scripts.router_chain import build_router_chain OPENAI_KEY = os.getenv("OPENAI_API_KEY", None) MODEL_NAME = os.getenv("OPENAI_MODEL", "gpt-4o-mini") if not OPENAI_KEY: print("WARNING: OPENAI_API_KEY not set. The app may fail at runtime.") # Build the router once (keeps vectorstore & models in memory) router = build_router_chain(model_name=MODEL_NAME) def chat_fn(message, history): if not message: return history, "" # call router result = router.invoke({"input": message}) # RetrievalQA returns dict with 'result' key (and maybe 'source_documents') answer = result.get("result") if isinstance(result, dict) else str(result) # append sources if present sources = None if isinstance(result, dict) and "source_documents" in result and result["source_documents"]: try: sources = list({str(d.metadata.get("source", "unknown")) for d in result["source_documents"]}) except Exception: sources = None if sources: answer = f"{answer}\n\nšŸ“š Sources: {', '.join(sources)}" history.append((message, answer)) return history, "" with gr.Blocks() as demo: gr.Markdown("## šŸ“š Course Assistant — Chat with your course files") # chatbot = gr.Chatbot(elem_id="chatbot", type="messages") chatbot = gr.Chatbot(elem_id="chatbot", type="tuple") txt = gr.Textbox(show_label=False, placeholder="Ask about the course...") txt.submit(chat_fn, [txt, chatbot], [chatbot, txt]) txt.submit(lambda: None, None, txt) # clear input if __name__ == "__main__": demo.launch(server_port=int(os.getenv("PORT", 7860)))