import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

# Load models
chatbot_model = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
model = AutoModelForCausalLM.from_pretrained(chatbot_model)
emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")

# Store chat histories
chat_histories = {}

def chatbot_response(message, session_id="default"):
    if session_id not in chat_histories:
        chat_histories[session_id] = []

    # Generate response
    input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors="pt")
    output = model.generate(input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)

    # Detect emotion
    emotion_result = emotion_pipeline(message)
    emotion = emotion_result[0]["label"]
    score = float(emotion_result[0]["score"])

    # Store history
    chat_histories[session_id].append((message, response))
    return response, emotion, score

# ------------------ Web Interface ------------------
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 Mental Health Chatbot")
    with gr.Row():
        with gr.Column():
            chatbot = gr.Chatbot()
            msg = gr.Textbox(label="Your Message")
            session_id = gr.Textbox(label="Session ID", value="default")
            btn = gr.Button("Send")
            clear_btn = gr.Button("Clear History")
        with gr.Column():
            emotion_out = gr.Textbox(label="Detected Emotion")
            score_out = gr.Number(label="Confidence Score")

    def respond(message, chat_history, session_id):
        response, emotion, score = chatbot_response(message, session_id)
        chat_history.append((message, response))
        return "", chat_history, emotion, score

    btn.click(respond, [msg, chatbot, session_id], [msg, chatbot, emotion_out, score_out])
    msg.submit(respond, [msg, chatbot, session_id], [msg, chatbot, emotion_out, score_out])
    clear_btn.click(lambda s_id: ([], "", 0.0) if s_id in chat_histories else ([], "", 0.0),
                    inputs=[session_id],
                    outputs=[chatbot, emotion_out, score_out])

# ------------------ API Endpoint ------------------
api_interface = gr.Interface(
    fn=chatbot_response,  # Exposing the chatbot function
    inputs=[gr.Textbox(label="Message"), gr.Textbox(label="Session ID", value="default")],
    outputs=[gr.Textbox(label="Chatbot Response"), gr.Textbox(label="Detected Emotion"), gr.Number(label="Confidence Score")]
)

# Launch Gradio interface and API
demo.launch(share=True, server_name="0.0.0.0", server_port=7860)
api_interface.launch(share=True, server_name="0.0.0.0", server_port=7861)