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

# Determine device
device = "cuda" if torch.cuda.is_available() else "cpu"

model_id = "thrishala/mental_health_chatbot"

try:
    # Load model with appropriate device_map and settings
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map="auto",  # Use "auto" for device_map instead of device name
        torch_dtype=torch.float16,
        low_cpu_mem_usage=True,
        max_memory={0: "15GiB"} if torch.cuda.is_available() else None,
        offload_folder="offload",
    ).eval()  # Set model to evaluation mode

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.pad_token = tokenizer.eos_token  # Set padding token if missing

    # Perform a dummy generation to initialize model (if needed)
    dummy_input = tokenizer("This is a test.", return_tensors="pt").to(model.device)
    model.generate(
        input_ids=dummy_input.input_ids,
        max_new_tokens=1,
        return_dict_in_generate=True  # Correct parameter name
    )

except Exception as e:
    print(f"Error loading model: {e}")
    exit()

def generate_text(prompt, max_new_tokens=128):
    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        output = model.generate(
            input_ids=input_ids,
            max_new_tokens=max_new_tokens,
            do_sample=False,
            eos_token_id=tokenizer.eos_token_id,
            return_dict_in_generate=True  # Correct parameter name
        )

    generated_text = tokenizer.decode(output.sequences[0], skip_special_tokens=True)
    return generated_text

def generate_text_streaming(prompt, max_new_tokens=128):
    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        for _ in range(max_new_tokens):
            output = model.generate(
                input_ids=input_ids,
                max_new_tokens=1,
                do_sample=False,
                eos_token_id=tokenizer.eos_token_id,
                return_dict_in_generate=True  # Correct parameter name
            )

            # Get the last generated token
            generated_token_id = output.sequences[0, -1]
            generated_token = tokenizer.decode([generated_token_id], skip_special_tokens=True)
            yield generated_token

            # Append new token to input_ids
            input_ids = torch.cat([input_ids, output.sequences[:, -1:]], dim=-1)

            if generated_token_id == tokenizer.eos_token_id:
                break

def respond(message, history, system_message, max_tokens):
    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:
        for token in generate_text_streaming(prompt, max_tokens):
            yield token
    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=128, value=32, step=1, label="Max new tokens"),
    ],
)

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