from threading import Thread  # Import the Thread class from the threading module

import torch  # Import the PyTorch library
import gradio as gr  # Import Gradio for creating a UI
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TextIteratorStreamer  # Import Hugging Face Transformers

# Define the Hugging Face model ID and check for available GPU (cuda)
model_id = "declare-lab/flan-alpaca-large"
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
print("Running on device:", torch_device)
print("CPU threads:", torch.get_num_threads())

# Load the pre-trained model based on the device
if torch_device == "cuda":
    model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_8bit=True, device_map="auto")
else:
    model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

tokenizer = AutoTokenizer.from_pretrained(model_id)

# Define a function to run model text generation
def run_generation(user_text, top_p, temperature, top_k, max_new_tokens):
    # Get the model and tokenizer, and tokenize the user text.
    model_inputs = tokenizer([user_text], return_tensors="pt").to(torch_device)

    # Start generation on a separate thread, so that we don't block the UI.
    # Adds timeout to the streamer to handle exceptions in the generation thread.
    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        temperature=float(temperature),
        top_k=top_k
    )
    
    # Create a new thread for model generation
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    model_output = ""
    for new_text in streamer:
        model_output += new_text
        yield model_output
    return model_output

# Define a function to reset the user input textbox
def reset_textbox():
    return gr.update(value='')

# Create a Gradio UI interface
with gr.Blocks() as demo:
    # Display a title
    gr.Markdown(
        "# Testing ALPACA Model \n"
    )

    with gr.Row():
        with gr.Column(scale=4):
            # Create a textbox for user input
            user_text = gr.Textbox(
                placeholder="Ask Me Anything ... ",
                label="User input"
            )
            # Create a textbox for model output
            model_output = gr.Textbox(label="Model output", lines=10, interactive=False)
            # Create a submit button
            button_submit = gr.Button(value="Submit")

        with gr.Column(scale=1):
            # Create sliders for adjusting generation parameters
            max_new_tokens = gr.Slider(
                minimum=1, maximum=1000, value=250, step=1, interactive=True, label="Max New Tokens",
            )
            top_p = gr.Slider(
                minimum=0.05, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)",
            )
            top_k = gr.Slider(
                minimum=1, maximum=50, value=50, step=1, interactive=True, label="Top-k",
            )
            temperature = gr.Slider(
                minimum=0.1, maximum=5.0, value=0.8, step=0.1, interactive=True, label="Temperature",
            )

    # Set up the submission of user input
    user_text.submit(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)
    button_submit.click(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)

    # Launch the Gradio interface
    demo.queue(max_size=32).launch(enable_queue=True)