import gradio as gr
from huggingface_hub import hf_hub_download, snapshot_download
import subprocess
import tempfile
import shutil
import os
import spaces
import importlib
from transformers import T5ForConditionalGeneration, T5Tokenizer
import os

def download_t5_model(model_id, save_directory):
    # Modelin tokenizer'ını ve modeli indir
    if not os.path.exists(save_directory):
        os.makedirs(save_directory)
    snapshot_download(repo_id="DeepFloyd/t5-v1_1-xxl",local_dir=save_directory, local_dir_use_symlinks=False)

# Model ID ve kaydedilecek dizin
model_id = "DeepFloyd/t5-v1_1-xxl"
save_directory = "pretrained_models/t5_ckpts/t5-v1_1-xxl"

# Modeli indir
download_t5_model(model_id, save_directory)

def download_model(repo_id, model_name):
    model_path = hf_hub_download(repo_id=repo_id, filename=model_name)
    return model_path

import glob

subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
repo_id = "hpcai-tech/Open-Sora"

# Map model names to their respective configuration files
model_name = "OpenSora-v1-HQ-16x512x512.pth"
config_mapping = {
    "OpenSora-v1-16x256x256.pth": "configs/opensora/inference/16x256x256.py",
    "OpenSora-v1-HQ-16x256x256.pth": "configs/opensora/inference/16x512x512.py",
    "OpenSora-v1-HQ-16x512x512.pth": "configs/opensora/inference/64x512x512.py"
}

config_path = config_mapping[model_name]
ckpt_path = download_model(repo_id, model_name)

@spaces.GPU(duration=200)
def run_inference(prompt_text):

    # Save prompt_text to a temporary text file
    prompt_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode='w')
    prompt_file.write(prompt_text)
    prompt_file.close()

    with open(config_path, 'r') as file:
        config_content = file.read()
    config_content = config_content.replace('prompt_path = "./assets/texts/t2v_samples.txt"', f'prompt_path = "{prompt_file.name}"')
    
    with tempfile.NamedTemporaryFile('w', delete=False, suffix='.py') as temp_file:
        temp_file.write(config_content)
        temp_config_path = temp_file.name

    cmd = [
        "torchrun", "--standalone", "--nproc_per_node", "1",
        "scripts/inference.py", temp_config_path,
        "--ckpt-path", ckpt_path
    ]
    subprocess.run(cmd)

    save_dir = "./outputs/samples/"  # Örneğin, inference.py tarafından kullanılan kayıt dizini
    list_of_files = glob.glob(f'{save_dir}/*')
    if list_of_files:
        latest_file = max(list_of_files, key=os.path.getctime)
        return latest_file
    else:
        print("No files found in the output directory.")
        return None

    # Clean up the temporary files
    os.remove(temp_file.name)
    os.remove(prompt_file.name)

def main():
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                gr.HTML(
                """
                <h1 style='text-align: center'>
               Open-Sora: Democratizing Efficient Video Production for All
                </h1>
                """
            )
                gr.HTML(
                    """
                    <h3 style='text-align: center'>
                    Follow me for more! 
                    <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>
                    </h3>
                    """
            )

        with gr.Row():
            with gr.Column():
                prompt_text = gr.Textbox(show_label=False, placeholder="Enter prompt text here", lines=4)
                submit_button = gr.Button("Run Inference")

            with gr.Column():
                output_video = gr.Video()

        submit_button.click(
            fn=run_inference, 
            inputs=[prompt_text], 
            outputs=output_video
        )
        gr.Examples(
            examples=[
                [
                    "A serene underwater scene featuring a sea turtle swimming through a coral reef. The turtle, with its greenish-brown shell, is the main focus of the video, swimming gracefully towards the right side of the frame. The coral reef, teeming with life, is visible in the background, providing a vibrant and colorful backdrop to the turtle's journey. Several small fish, darting around the turtle, add a sense of movement and dynamism to the scene. The video is shot from a slightly elevated angle, providing a comprehensive view of the turtle's surroundings. The overall style of the video is calm and peaceful, capturing the beauty and tranquility of the underwater world.",
                ],       
            ],
            fn=run_inference,
            inputs=[prompt_text,],
            outputs=[output_video],
            cache_examples=True,
        )

    demo.launch(debug=True)

if __name__ == "__main__":
    main()