Update app.py

app.py

@@ -1,3 +1,78 @@
+'''
+import os
+from gradio_client import Client, handle_file
+from PIL import Image
+
+# Initialize Gradio client
+client = Client("http://localhost:7860")
+
+# Path configuration
+input_dir = "Aesthetics_X_Phone_720p_Images_Rec_Captioned_16_9"
+output_dir = "processed_output_v2_single_thread"
+os.makedirs(output_dir, exist_ok=True)
+
+def process_single_image(png_path):
+    """Process a single image and save results"""
+    try:
+        # Get base filename without extension
+        base_name = os.path.splitext(os.path.basename(png_path))[0]
+        
+        # Corresponding text file path
+        txt_path = os.path.join(input_dir, f"{base_name}.txt")
+        
+        print(f"Processing: {png_path}...", end=" ", flush=True)
+        
+        # Process image through API (returns WEBP path)
+        webp_result = client.predict(
+            img=handle_file(png_path),
+            model_selection="v2",
+            api_name="/predict"
+        )
+        
+        # Output paths
+        output_image_path = os.path.join(output_dir, f"{base_name}.png")
+        output_text_path = os.path.join(output_dir, f"{base_name}.txt")
+        
+        # Convert WEBP to PNG
+        with Image.open(webp_result) as img:
+            img.save(output_image_path, "PNG")
+        
+        # Copy corresponding text file if exists
+        if os.path.exists(txt_path):
+            with open(txt_path, 'r', encoding='utf-8') as src, \
+                 open(output_text_path, 'w', encoding='utf-8') as dst:
+                dst.write(src.read())
+        
+        print("Done")
+        return True
+    
+    except Exception as e:
+        print(f"Failed: {str(e)}")
+        return False
+
+def main():
+    # Get all PNG files in input directory
+    png_files = sorted([
+        os.path.join(input_dir, f) 
+        for f in os.listdir(input_dir) 
+        if f.lower().endswith('.png')
+    ])
+    
+    print(f"Found {len(png_files)} PNG files to process")
+    
+    # Process files one by one
+    success_count = 0
+    for i, png_path in enumerate(png_files, 1):
+        print(f"\n[{i}/{len(png_files)}] ", end="")
+        if process_single_image(png_path):
+            success_count += 1
+    
+    print(f"\nProcessing complete! Success: {success_count}/{len(png_files)}")
+
+if __name__ == "__main__":
+    main()
+'''
+
 from aura_sr import AuraSR
 import gradio as gr
 import spaces