import gradio as gr
from transformers import pipeline, Conversation

# --- Load model ---
# Use a lighter model for HF Spaces CPU (replace with a custom HF model if available)
chatbot_model_id = "microsoft/DialoGPT-small"
chatbot = pipeline("conversational", model=chatbot_model_id)

# --- Define response function ---
def respond(message, history=[]):
    conv = Conversation(message)
    response = chatbot(conv)
    reply = response.generated_responses[-1]  # Last generated reply
    history.append((message, reply))
    return history, history

# --- Build Gradio interface ---
with gr.Blocks() as demo:
    chat_history = gr.State([])  # Maintains conversation

    with gr.Row():
        user_input = gr.Textbox(label="You", placeholder="Type here...")
        send_button = gr.Button("Send")

    chat_display = gr.Chatbot(label="Mental Health Bot")

    send_button.click(
        fn=respond,
        inputs=[user_input, chat_history],
        outputs=[chat_display, chat_history]
    )

# Launch app
demo.launch()