Update app.py

app.py

@@ -2,44 +2,22 @@ import os
 from pathlib import Path
 import subprocess
 import gradio as gr
-from PIL import Image, ImageSequence
+from PIL import Image
 import io
 
-# ⚙️ CPU optimization tips
-os.environ["OMP_NUM_THREADS"] = "1"
-os.environ["MKL_NUM_THREADS"] = "1"
+# Paths to pre-existing demo images
+FRAME1_PATH = Path("demo/frame1.png")
+FRAME2_PATH = Path("demo/frame2.png")
+OUTPUT_GIF = Path("/tmp/output.gif")  # Use /tmp for fast access
 
-# 🗂 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 generate_demo_gif(exp=2, progress=gr.Progress(track_tqdm=True)):
+    progress(0.05, desc="Starting...")
 
-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 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")
-
-    # 🖼 Resize input images
-    _save_resized(img1, FRAME1_PATH)
-    _save_resized(img2, FRAME2_PATH)
-
-    # 🔁 Clean previous output
+    # Clean previous output if any
     if OUTPUT_GIF.exists():
         OUTPUT_GIF.unlink(missing_ok=True)
 
-    # 🚀 Run inference
+    # Build command using demo images directly
     cmd = [
         "python", "inference_img.py",
         "--img", str(FRAME1_PATH), str(FRAME2_PATH),
@@ -47,37 +25,36 @@ def generate_demo_gif(img1, img2, exp=2, progress=gr.Progress(track_tqdm=True)):
         "--model", "train_log/"
     ]
     print("Running:", " ".join(cmd))
-    progress(0.25, desc="Running inference")
-    result = subprocess.run(cmd, capture_output=True, text=True)
+    progress(0.25, desc="Running model")
 
+    result = subprocess.run(cmd, capture_output=True, text=True)
     print("STDOUT:", result.stdout)
     print("STDERR:", result.stderr)
 
-    progress(0.85, desc="Rendering result")
-
+    # Check result
     if result.returncode == 0 and OUTPUT_GIF.exists():
-        gif_image = load_gif_as_pil(OUTPUT_GIF)
+        progress(0.85, desc="Loading GIF")
+        with open(OUTPUT_GIF, "rb") as f:
+            gif_bytes = f.read()
+        image = Image.open(io.BytesIO(gif_bytes))
         progress(1.0, desc="Done")
-        return gif_image, "✅ GIF generated successfully!"
+        return image, "✅ GIF generated successfully!"
     else:
-        return None, "❌ GIF generation failed. Try smaller images or check your model path."
+        return None, "❌ GIF generation failed. Check model and paths."
 
-# 🎨 Hugging Face UI
+# Build Gradio UI (no uploads)
 with gr.Blocks() as demo_ui:
-    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")
+    gr.Markdown("## 🌀 Img_InterpolationCutoff — Demo GIF from Preloaded Frames")
 
     with gr.Row():
-        exp = gr.Slider(1, 4, value=2, step=1, label="Interpolation Exponent (lower = faster)")
+        frame_preview_1 = gr.Image(value=str(FRAME1_PATH), interactive=False, label="Frame 1")
+        frame_preview_2 = gr.Image(value=str(FRAME2_PATH), interactive=False, label="Frame 2")
 
-    run = gr.Button("Generate GIF")
+    exp = gr.Slider(1, 4, value=2, step=1, label="Interpolation Exponent")
+    run_btn = 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])
+    run_btn.click(fn=generate_demo_gif, inputs=[exp], outputs=[out_gif, status])
 
-demo_ui.queue()
 demo_ui.launch()