Upload app.py

app.py

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+import os
+import shutil
+import subprocess
+import spaces
+import gradio as gr
+from pathlib import Path
+
+# --- 1. BOOTSTRAP ENVIRONMENT ---
+def setup_repos():
+    if not os.path.exists("/app/SadTalker"):
+        print("📥 Cloning Repositories...")
+        subprocess.run(["git", "clone", "https://github.com/OpenTalker/SadTalker.git", "/app/SadTalker"])
+        subprocess.run(["git", "clone", "https://github.com/Rudrabha/Wav2Lip.git", "/app/Wav2Lip"])
+        # Fix BasicSR compatibility
+        subprocess.run(["find", "/usr/local/lib/python3.10/site-packages/basicsr", "-name", "degradations.py", "-exec", "sed", "-i", "s/functional_tensor/functional/g", "{}", "+"])
+
+setup_repos()
+
+# --- 2. THE GPU-ACCELERATED CORE ---
+@spaces.GPU(duration=120) # Grants H200 access for 2 minutes per click
+def generate(image, audio):
+    # Setup paths
+    workspace = Path("/tmp/visor_workspace")
+    workspace.mkdir(parents=True, exist_ok=True)
+    
+    img_path = workspace / "input.jpg"
+    aud_path = workspace / "input.mp3"
+    
+    # Gradio provides file paths directly
+    shutil.copy(image, img_path)
+    shutil.copy(audio, aud_path)
+
+    # Note: On HF Spaces, you should use their 'checkpoints' or 
+    # use 'gdown' to pull your weights into /app/SadTalker/checkpoints
+    # For testing, SadTalker will auto-download if folder is empty.
+
+    print("🎬 Running Animation...")
+    subprocess.run([
+        "python", "/app/SadTalker/inference.py",
+        "--driven_audio", str(aud_path),
+        "--source_image", str(img_path),
+        "--result_dir", "/tmp/results",
+        "--still", "--preprocess", "full"
+    ], env={**os.environ, "PYTHONPATH": "/app/SadTalker"})
+
+    # Return the first mp4 found
+    result_video = list(Path("/tmp/results").glob("**/*.mp4"))
+    return result_video[0] if result_video else None
+
+# --- 3. GRADIO INTERFACE (The Frontend) ---
+with gr.Blocks(title="VisorFlow Core") as demo:
+    gr.Markdown("# 🛡️ VisorFlow Core: ZeroGPU Edition")
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(type="filepath", label="Source Image")
+            input_aud = gr.Audio(type="filepath", label="Driven Audio")
+            run_btn = gr.Button("Execute Phase 3", variant="primary")
+        with gr.Column():
+            output_video = gr.Video(label="Generated Intelligence")
+
+    run_btn.click(fn=generate, inputs=[input_img, input_aud], outputs=[output_video])
+
+demo.launch()