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

model_id = "thrishala/mental_health_chatbot"

try:
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        load_in_8bit=True,
        device_map="auto",
        torch_dtype=torch.float16
    )
    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
except Exception as e:
    print(f"Error loading model: {e}")
    exit()

def respond(
    message,
    history,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Construct the prompt with clear separation
    prompt = f"{system_message}\n"
    for user_msg, bot_msg in history:
        prompt += f"User: {user_msg}\nAssistant: {bot_msg}\n"
    prompt += f"User: {message}\nAssistant:"
    
    try:
        response = pipe(
            prompt,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            eos_token_id=tokenizer.eos_token_id,  # Use EOS token to stop generation
        )[0]["generated_text"]
        
        # Extract only the new assistant response after the last Assistant: in the prompt
        bot_response = response[len(prompt):].split("User:")[0].strip()  # Take text after prompt and before next User
        yield bot_response
    except Exception as e:
        print(f"Error during generation: {e}")
        yield "An error occurred."

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(
            value="You are a friendly and helpful mental health chatbot.",
            label="System message",
        ),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

if __name__ == "__main__":
    demo.launch()