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

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

# Load emotion detection model
emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")

def generate_response(user_input):
    # Generate chatbot response
    input_ids = tokenizer.encode(user_input + 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(user_input)
    emotion = emotion_result[0]["label"]
    
    return response, emotion

iface = gr.Interface(
    fn=generate_response,
    inputs=gr.Textbox(label="Enter your message"),
    outputs=[gr.Textbox(label="Chatbot Response"), gr.Textbox(label="Emotion Detected")],
    live=True
)

iface.launch()