Update app.py

app.py

@@ -2,7 +2,8 @@ import os
 from pathlib import Path
 import subprocess
 import gradio as gr
-from PIL import Image
+from PIL import Image, ImageSequence
+import io
 
 # ⚙️ CPU optimization tips
 os.environ["OMP_NUM_THREADS"] = "1"
@@ -22,67 +23,61 @@ def _save_resized(img: Image.Image, path: Path, max_w=640):
         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 load_gif_as_pil(gif_path: Path):
+    with open(gif_path, "rb") as f:
+        gif_bytes = f.read()
+    return Image.open(io.BytesIO(gif_bytes))
 
 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
+    # 🔁 Clean previous output
+    if OUTPUT_GIF.exists():
+        OUTPUT_GIF.unlink(missing_ok=True)
+
+    # 🚀 Run inference
     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
+        "--model", "train_log/"
     ]
-    print("Running:", " ".join(map(str, cmd)))
-
-    progress(0.20, desc="Running inference")
+    print("Running:", " ".join(cmd))
+    progress(0.25, 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")
+    progress(0.85, desc="Rendering result")
 
     if result.returncode == 0 and OUTPUT_GIF.exists():
+        gif_image = load_gif_as_pil(OUTPUT_GIF)
         progress(1.0, desc="Done")
-        return str(OUTPUT_GIF), "✅ GIF generated successfully!"
+        return gif_image, "✅ GIF generated successfully!"
     else:
         return None, "❌ GIF generation failed. Try smaller images or check your model path."
 
-# 🖥 Gradio UI
+# 🎨 Hugging Face UI
 with gr.Blocks() as demo_ui:
-    gr.Markdown("### 🖼 Generate a GIF animation from two frames (CPU-optimized)")
+    gr.Markdown("## 🌀 Img_InterpolationCutoff — Frame-to-GIF Generator")
+
     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()