app.py

import os
from pathlib import Path
import subprocess
import gradio as gr
from PIL import Image

# ⚙️ CPU optimization tips
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"

# 🗂 Paths
TMP = Path("/tmp")
TMP.mkdir(parents=True, exist_ok=True)
FRAME1_PATH = TMP / "frame1.jpg"
FRAME2_PATH = TMP / "frame2.jpg"
OUTPUT_GIF = TMP / "output.gif"

def _save_resized(img: Image.Image, path: Path, max_w=640):
    w, h = img.size
    if w > max_w:
        scale = max_w / w
        img = img.resize((int(w * scale), int(h * scale)), Image.BILINEAR)
    img.save(path, quality=90)

def write_gif(frames, out_path, duration_ms=60):
    frames[0].save(
        out_path,
        save_all=True,
        append_images=frames[1:],
        duration=duration_ms,
        loop=0,
        disposal=2,
        optimize=False,
        dither=0
    )

def generate_demo_gif(img1, img2, exp=2, progress=gr.Progress(track_tqdm=True)):
    progress(0.05, desc="Preparing frames")

    # 🧹 Clean previous output
    if OUTPUT_GIF.exists():
        OUTPUT_GIF.unlink(missing_ok=True)

    # 🖼 Resize input images
    _save_resized(img1, FRAME1_PATH)
    _save_resized(img2, FRAME2_PATH)

    # 🧠 Build and run inference command
    cmd = [
        "python", "inference_img.py",
        "--img", str(FRAME1_PATH), str(FRAME2_PATH),
        f"--exp={int(exp)}",
        "--model", "train_log/",
        "--out", str(OUTPUT_GIF)  # Use if your script supports explicit output path
    ]
    print("Running:", " ".join(map(str, cmd)))

    progress(0.20, desc="Running inference")
    result = subprocess.run(cmd, capture_output=True, text=True)

    print("STDOUT:", result.stdout)
    print("STDERR:", result.stderr)

    progress(0.85, desc="Checking result")

    if result.returncode == 0 and OUTPUT_GIF.exists():
        progress(1.0, desc="Done")
        return str(OUTPUT_GIF), "✅ GIF generated successfully!"
    else:
        return None, "❌ GIF generation failed. Try smaller images or check your model path."

# 🖥 Gradio UI
with gr.Blocks() as demo_ui:
    gr.Markdown("### 🖼 Generate a GIF animation from two frames (CPU-optimized)")
    with gr.Row():
        img1 = gr.Image(label="Frame 1", type="pil")
        img2 = gr.Image(label="Frame 2", type="pil")
    with gr.Row():
        exp = gr.Slider(1, 4, value=2, step=1, label="Interpolation Exponent (lower = faster)")
    run = gr.Button("Generate GIF")
    out_gif = gr.Image(label="Animated GIF")
    status = gr.Markdown()

    run.click(generate_demo_gif, [img1, img2, exp], [out_gif, status])

# 🧵 Enable queuing to prevent app cutoff
demo_ui.queue()
demo_ui.launch()