app.py

import gradio as gr
import time

# -------------------------
# Gradio function
# -------------------------
def generate_response_gradio(message, history):
    """
    Handles user query, preserves LangChain chat history,
    and tracks response time.
    """
    history = history or []  # Gradio passes None initially

    # Convert Gradio history to proper format for LangChain
    langchain_history = [(h[0], h[1].split("\n⏱")[0]) for h in history] if history else []

    start_time = time.time()

    # Get answer from ConversationalRetrievalChain
    out = qa_chain({"question": message, "chat_history": langchain_history})
    answer = out["answer"]

    end_time = time.time()
    elapsed_time = end_time - start_time

    # Append to history in proper tuple format
    history.append((message, f"{answer}\n⏱ Response Time: {elapsed_time:.2f} sec"))

    return history, history

# -------------------------
# Gradio UI
# -------------------------
with gr.Blocks() as demo:
    gr.Markdown("## 🤖 Chat with HR Policies")

    chatbot = gr.Chatbot(label="Chat Window", height=400)
    txt = gr.Textbox(
        placeholder="Type your message and press Enter", show_label=False, autofocus=True
    )

    txt.submit(generate_response_gradio, [txt, chatbot], [chatbot, chatbot])
    txt.submit(lambda: "", None, txt)

demo.launch(inline=True, share=True)