import gradio as gr
import re 
import subprocess
import select
from huggingface_hub import snapshot_download

# Download model (for demonstration, adjust based on actual model needs)
snapshot_download(
    repo_id="Wan-AI/Wan2.1-T2V-1.3B",
    local_dir="./Wan2.1-T2V-1.3B"
)

# Function to generate video
def infer(prompt, progress=gr.Progress(track_tqdm=True)):
    # Reduced progress output and simplified structure
    command = [
        "python", "-u", "-m", "generate",  # Using unbuffered output
        "--task", "t2v-1.3B",
        "--size", "832*480",  # You can try reducing resolution further for CPU
        "--ckpt_dir", "./Wan2.1-T2V-1.3B",
        "--sample_shift", "8",
        "--sample_guide_scale", "6",
        "--prompt", prompt,
        "--save_file", "generated_video.mp4"
    ]

    # Run the model inference in a subprocess
    process = subprocess.Popen(command, 
                               stdout=subprocess.PIPE, 
                               stderr=subprocess.PIPE, 
                               text=True, 
                               bufsize=1)

    # Monitor progress with a minimal progress bar
    progress_pattern = re.compile(r"(\d+)%\|.*\| (\d+)/(\d+)")
    video_progress_bar = None
    overall_steps = 0

    while True:
        rlist, _, _ = select.select([process.stdout], [], [], 0.04)
        if rlist:
            line = process.stdout.readline()
            if not line:
                break
            stripped_line = line.strip()
            if not stripped_line:
                continue

            # Check for video generation progress
            progress_match = progress_pattern.search(stripped_line)
            if progress_match:
                current = int(progress_match.group(2))
                total = int(progress_match.group(3))
                if video_progress_bar is None:
                    video_progress_bar = gr.Progress()
                    video_progress_bar.update(current / total)
                video_progress_bar.update(current / total)
                continue

            # Process info messages (simplified)
            if "INFO:" in stripped_line:
                overall_steps += 1
                continue
            else:
                print(stripped_line)

        if process.poll() is not None:
            break

    # Clean up and finalize the progress bar
    process.wait()
    if video_progress_bar:
        video_progress_bar.close()

    # Return the video file path if successful
    if process.returncode == 0:
        return "generated_video.mp4"
    else:
        raise Exception("Error executing command")

# Gradio UI
with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown("# Wan 2.1 1.3B Video Generation")
        prompt = gr.Textbox(label="Prompt")
        submit_btn = gr.Button("Generate Video")
        video_res = gr.Video(label="Generated Video")

    submit_btn.click(
        fn=infer,
        inputs=[prompt],
        outputs=[video_res]
    )

demo.queue().launch(show_error=True, show_api=False)