Create app.py

app.py

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+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(concurrency_count=1, max_size=8)
+demo_ui.launch()