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Update app.py
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app.py
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import shutil
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import tempfile
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import threading
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import subprocess
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from pathlib import Path
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import gradio as gr
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from PIL import Image
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RIFE_DIR = Path("rife") # Local bundled repo
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OUTPUT_DIR = RIFE_DIR / "output"
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LOCK = threading.Lock()
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def interpolate(a: Image.Image, b: Image.Image, fps: int = 14, exp: int = 2):
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with LOCK:
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# Normalize inputs
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a = a.convert("RGB")
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b = b.convert("RGB")
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if b.size != a.size:
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b = b.resize(a.size, Image.BICUBIC)
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work_dir = Path(tempfile.mkdtemp(prefix="rife_run_"))
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p1 = work_dir / "a.png"
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p2 = work_dir / "b.png"
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a.save(p1, "PNG")
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b.save(p2, "PNG")
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# Clean previous outputs
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if OUTPUT_DIR.exists():
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shutil.rmtree(OUTPUT_DIR)
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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# Run RIFE inference
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cmd = ["python3", str(RIFE_DIR / "inference_img.py"), "--img", str(p1), str(p2)]
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if isinstance(exp, int) and exp >= 1:
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cmd += ["--exp", str(exp)]
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subprocess.run(cmd, cwd=str(RIFE_DIR), check=True)
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# Collect interpolated frames
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frames = []
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i = 1
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while True:
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fp = OUTPUT_DIR / f"img{i}.png"
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if not fp.exists():
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break
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frames.append(fp)
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i += 1
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if not frames:
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raise RuntimeError("No frames generated.")
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# Build GIF
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images = [Image.open(p).convert("RGBA") for p in frames]
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duration_ms = max(1, int(1000 / max(1, fps)))
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gif_path = work_dir / "interpolation.gif"
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images[0].save(
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gif_path,
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save_all=True,
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append_images=images[1:],
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optimize=False,
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duration=duration_ms,
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loop=0,
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disposal=2,
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)
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# Optional cleanup
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try:
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shutil.rmtree(OUTPUT_DIR)
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except Exception:
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pass
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return str(gif_path)
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# Gradio UI
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TITLE = "🔥 RIFE Interpolation Demo (PyTorch, Local)"
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with gr.Blocks(title=TITLE, analytics_enabled=False) as demo:
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gr.Markdown(f"# {TITLE}")
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with gr.Row():
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with gr.Column():
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img_a = gr.Image(type="pil", label="Image A")
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img_b = gr.Image(type="pil", label="Image B")
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with gr.Column():
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fps = gr.Slider(6, 30, value=14, step=1, label="FPS")
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exp = gr.Slider(1, 4, value=2, step=1, label="Interpolation exponent")
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run = gr.Button("Interpolate", variant="primary")
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gif_out = gr.Image(type="filepath", label="Result GIF")
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run.click(interpolate, inputs=[img_a, img_b, fps, exp], outputs=[gif_out])
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demo.queue(concurrency_count=1, max_size=8)
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# app.py
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import subprocess
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from pathlib import Path
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import gradio as gr
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# --- Paths ---
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DEMO_DIR = Path("demo") # Folder with input frames
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FRAME1 = DEMO_DIR / "frame1.png" # First image
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FRAME2 = DEMO_DIR / "frame2.png" # Second image
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RETRO_GIF = DEMO_DIR / "demo.gif" # Final result
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# --- Interpolation function ---
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def interpolate(img_a_path, img_b_path):
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"""
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Run interpolation pipeline using existing shell scripts and commands.
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Expects two input image paths. Outputs interpolated GIF.
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"""
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try:
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# Step 1: Run your inference script to generate intermediate frames
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subprocess.run([
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"python3", "img_inference.py",
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"--img", str(img_a_path), str(img_b_path)
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], check=True)
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# Step 2: Generate optimized palette using ffmpeg
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subprocess.run([
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"ffmpeg", "-r", "14", "-f", "image2", "-i", "output/img%d.png",
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"-vf", "palettegen=stats_mode=single", "palette.png"
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], check=True)
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# Step 3: Apply palette to produce final gif
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subprocess.run([
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"ffmpeg", "-r", "14", "-f", "image2", "-i", "output/img%d.png",
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"-i", "palette.png", "-lavfi", "paletteuse", str(RETRO_GIF)
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], check=True)
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return str(RETRO_GIF)
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except subprocess.CalledProcessError:
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raise gr.Error("Interpolation failed. Please check script and inputs.")
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# --- Demo reset: returns static frames ---
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def reset_demo():
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return str(FRAME1), str(FRAME2)
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# --- Gradio UI ---
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with gr.Blocks(title="RIFE Image Interpolation") as demo:
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with gr.Tab("Demo"):
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gr.Markdown("### Interpolate between two images")
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img_a = gr.Image(type="filepath", value=str(FRAME1), label="Image A")
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img_b = gr.Image(type="filepath", value=str(FRAME2), label="Image B")
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run = gr.Button("Interpolate")
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reset = gr.Button("Reset")
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result = gr.Image(type="filepath", label="Interpolated GIF")
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run.click(interpolate, inputs=[img_a, img_b], outputs=result)
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reset.click(reset_demo, outputs=[img_a, img_b])
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demo.launch()
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