app.py

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

# Load chatbot model
print("Loading DialoGPT model...")
chatbot_model = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
model = AutoModelForCausalLM.from_pretrained(chatbot_model)

# Load emotion detection model
print("Loading emotion detection model...")
emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")

# Store chat histories
chat_histories = {}

def chatbot_response(message, history=None, session_id="default"):
    """Generate a chatbot response and detect emotion from user message"""
    # Initialize session if it doesn't exist
    if session_id not in chat_histories:
        chat_histories[session_id] = []
    
    # Generate chatbot 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 in chat history
    chat_histories[session_id].append((message, response))
    
    return response, emotion, score, chat_histories[session_id]

def api_chatbot_response(message, session_id="default"):
    """API endpoint version that returns a structured response"""
    response, emotion, score, _ = chatbot_response(message, None, session_id)
    
    return {
        "bot_response": response, 
        "emotion": emotion, 
        "emotion_score": score,
        "session_id": session_id
    }

def get_chat_history(session_id="default"):
    """Get chat history for a specific session"""
    if session_id in chat_histories:
        return chat_histories[session_id]
    return []

def clear_history(session_id="default"):
    """Clear chat history for a specific session"""
    if session_id in chat_histories:
        chat_histories[session_id] = []
        return f"History cleared for session {session_id}"
    return f"Session {session_id} not found"

# Define UI interface
with gr.Blocks(title="Mental Health Chatbot") as ui_interface:
    gr.Markdown("# 🧠 Mental Health Chatbot")
    
    with gr.Row():
        with gr.Column(scale=3):
            chatbot = gr.Chatbot(height=400, label="Conversation")
            
            with gr.Row():
                message = gr.Textbox(placeholder="Type your message here...", label="You", show_label=False)
                submit_btn = gr.Button("Send")
            
            with gr.Row():
                session_id = gr.Textbox(value="default", label="Session ID")
                clear_btn = gr.Button("Clear Chat")
        
        with gr.Column(scale=1):
            emotion_label = gr.Textbox(label="Emotion Detected")
            emotion_score = gr.Number(label="Confidence Score")
    
    # Set up event handlers
    def respond(message, chat_history, session_id):
        response, emotion, score, _ = chatbot_response(message, chat_history, session_id)
        chat_history.append((message, response))
        return "", chat_history, emotion, score
    
    submit_btn.click(
        respond, 
        [message, chatbot, session_id], 
        [message, chatbot, emotion_label, emotion_score]
    )
    
    message.submit(
        respond, 
        [message, chatbot, session_id], 
        [message, chatbot, emotion_label, emotion_score]
    )
    
    clear_btn.click(
        lambda s: ([], clear_history(s), "", 0), 
        [session_id], 
        [chatbot, emotion_label, emotion_score]
    )

# Define API interface
api_interface = gr.Interface(
    fn=api_chatbot_response,
    inputs=[
        gr.Textbox(label="Message"),
        gr.Textbox(label="Session ID", value="default")
    ],
    outputs=gr.JSON(label="Response"),
    title="Mental Health Chatbot API",
    description="Send a message to get chatbot response with emotion analysis",
    examples=[
        ["I'm feeling sad today", "user1"],
        ["I'm so excited about my new job!", "user2"],
        ["I'm worried about my exam tomorrow", "user3"]
    ]
)

history_api = gr.Interface(
    fn=get_chat_history,
    inputs=gr.Textbox(label="Session ID", value="default"),
    outputs=gr.JSON(label="Chat History"),
    title="Chat History API",
    description="Get chat history for a specific session"
)

clear_api = gr.Interface(
    fn=clear_history,
    inputs=gr.Textbox(label="Session ID", value="default"),
    outputs=gr.Textbox(label="Result"),
    title="Clear History API",
    description="Clear chat history for a specific session"
)

# Combine all interfaces
demo = gr.TabbedInterface(
    [ui_interface, api_interface, history_api, clear_api], 
    ["Chat UI", "Chat API", "History API", "Clear API"]
)

# Launch the app
if __name__ == "__main__":
    demo.launch()