import gradio as gr
import re
import subprocess
import time
from tqdm import tqdm
from huggingface_hub import snapshot_download
import torch
import os

# Force the device to CPU
device = torch.device("cpu")

# Download model
snapshot_download(
    repo_id="Wan-AI/Wan2.1-T2V-1.3B",
    local_dir="./Wan2.1-T2V-1.3B"
)
print("✅ Model downloaded successfully.")

def infer(prompt, progress=gr.Progress(track_tqdm=True)):
    total_process_steps = 11
    irrelevant_steps = 4
    relevant_steps = total_process_steps - irrelevant_steps

    overall_bar = tqdm(total=relevant_steps, desc="Overall Process", position=1,
                       ncols=120, dynamic_ncols=False, leave=True)
    processed_steps = 0

    progress_pattern = re.compile(r"(\d+)%\|.*\| (\d+)/(\d+)")
    video_progress_bar = None

    sub_bar = None
    sub_ticks = 0
    sub_tick_total = 1500
    video_phase = False

    # ✅ Use generate.py directly
    command = [
        "python", "-u", "generate.py",  # <- Make sure generate.py is in the same folder
        "--task", "t2v-1.3B",
        "--size", "480*480",
        "--ckpt_dir", "./Wan2.1-T2V-1.3B",
        "--sample_shift", "8",
        "--sample_guide_scale", "6",
        "--prompt", prompt,
        "--t5_cpu",
        "--offload_model", "True",
        "--save_file", "generated_video.mp4"
    ]

    print("🚀 Starting video generation process...")
    process = subprocess.Popen(
        command,
        stdout=subprocess.PIPE,
        stderr=subprocess.STDOUT,
        text=True,
        bufsize=1
    )

    stdout = process.stdout

    for line in iter(stdout.readline, ''):
        stripped_line = line.strip()
        print(f"[SUBPROCESS]: {stripped_line}")  # Debug print

        if not stripped_line:
            continue

        # Match video generation progress (like tqdm)
        progress_match = progress_pattern.search(stripped_line)
        if progress_match:
            if sub_bar is not None and sub_ticks < sub_tick_total:
                sub_bar.update(sub_tick_total - sub_ticks)
                sub_bar.close()
                overall_bar.update(1)
                sub_bar = None
                sub_ticks = 0
            video_phase = True
            current = int(progress_match.group(2))
            total = int(progress_match.group(3))
            if video_progress_bar is None:
                video_progress_bar = tqdm(total=total, desc="Video Generation", position=0,
                                          ncols=120, dynamic_ncols=True, leave=True)
            video_progress_bar.update(current - video_progress_bar.n)
            if video_progress_bar.n >= video_progress_bar.total:
                video_phase = False
                overall_bar.update(1)
                video_progress_bar.close()
                video_progress_bar = None
            continue

        if "INFO:" in stripped_line:
            parts = stripped_line.split("INFO:", 1)
            msg = parts[1].strip() if len(parts) > 1 else ""
            print(f"[INFO]: {msg}")
            if processed_steps < irrelevant_steps:
                processed_steps += 1
                continue
            else:
                if sub_bar is not None and sub_ticks < sub_tick_total:
                    sub_bar.update(sub_tick_total - sub_ticks)
                    sub_bar.close()
                    overall_bar.update(1)
                    sub_bar = None
                    sub_ticks = 0
                sub_bar = tqdm(total=sub_tick_total, desc=msg, position=2,
                               ncols=120, dynamic_ncols=False, leave=True)
                sub_ticks = 0
            continue

    process.wait()

    # Final cleanup
    if video_progress_bar is not None:
        video_progress_bar.close()
    if sub_bar is not None:
        sub_bar.close()
    overall_bar.close()

    # ✅ Output validation
    if process.returncode == 0:
        if os.path.exists("generated_video.mp4"):
            print("✅ Video generation completed successfully.")
            return "generated_video.mp4"
        else:
            print("❌ Video generation finished but output file is missing.")
            raise gr.Error("Output video not found after generation.")
    else:
        print("❌ Subprocess failed.")
        raise gr.Error("Video generation failed. Check logs above.")

# ✅ Gradio UI
with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown("# Wan 2.1 1.3B - Text to Video")
        gr.Markdown("Generate short videos from prompts. Duplicate this space to avoid queue limits.")
        prompt = gr.Textbox(label="Enter your 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, ssr_mode=False)