app.py

import os
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\n📚 Sources: {', '.join(sources)}"
    history.append((message, answer))
    return history, ""

with gr.Blocks() as demo:
    gr.Markdown("## 📚 Course Assistant — Chat with your course files")
    chatbot = gr.Chatbot(elem_id="chatbot")
    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)))