Update app.py

app.py

@@ -10,57 +10,87 @@ from realesrgan import RealESRGANer
 app = Flask(__name__)
 CORS(app)
 
-# Initialize model
-def init_upsampler():
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-    
-    return RealESRGANer(
-        scale=4,
-        model_path='weights/realesr-general-x4v3.pth',
-        model=model,
-        tile=400,
-        tile_pad=10,
-        pre_pad=0,
-        half=False,
-        device=device
-    )
+# Initialize Real-ESRGAN upsampler
+def initialize_enhancer():
+    try:
+        # Configuration for RealESRGAN x4v3
+        model = RRDBNet(
+            num_in_ch=3,
+            num_out_ch=3,
+            num_feat=64,
+            num_block=6,  # Critical parameter for x4v3 model
+            num_grow_ch=32,
+            scale=4
+        )
+        
+        # Force CPU usage for Hugging Face compatibility
+        device = torch.device('cpu')
+        
+        return RealESRGANer(
+            scale=4,
+            model_path='weights/realesr-general-x4v3.pth',
+            model=model,
+            tile=0,        # Set to 0 for small images, increase for large images
+            tile_pad=10,
+            pre_pad=0,
+            half=False,    # CPU doesn't support half precision
+            device=device
+        )
+    except Exception as e:
+        print(f"Initialization error: {str(e)}")
+        return None
 
-try:
-    upsampler = init_upsampler()
-    print("Model initialized successfully!")
-except Exception as e:
-    print(f"Model initialization failed: {str(e)}")
-    upsampler = None
+# Global upsampler instance
+upsampler = initialize_enhancer()
 
 @app.route('/enhance', methods=['POST'])
 def enhance_image():
     if not upsampler:
-        return {'error': 'Model not initialized'}, 500
+        return {'error': 'Model failed to initialize'}, 500
     
     if 'file' not in request.files:
         return {'error': 'No file uploaded'}, 400
     
     try:
+        # Read and validate image
         file = request.files['file']
+        if file.filename == '':
+            return {'error': 'Empty file submitted'}, 400
+            
         img = Image.open(file.stream).convert('RGB')
         img_array = np.array(img)
         
-        output, _ = upsampler.enhance(img_array, outscale=4)
+        # Enhance image
+        output, _ = upsampler.enhance(
+            img_array,
+            outscale=4,    # 4x super-resolution
+            alpha_upsampler='realesrgan'
+        )
         
+        # Convert to JPEG bytes
         img_byte_arr = io.BytesIO()
-        Image.fromarray(output).save(img_byte_arr, format='JPEG')
+        Image.fromarray(output).save(img_byte_arr, format='JPEG', quality=95)
         img_byte_arr.seek(0)
         
         return send_file(img_byte_arr, mimetype='image/jpeg')
     
     except Exception as e:
-        return {'error': str(e)}, 500
+        return {'error': f'Processing error: {str(e)}'}, 500
 
 @app.route('/health', methods=['GET'])
 def health_check():
     status = 'ready' if upsampler else 'unavailable'
     return {'status': status}, 200
 
+@app.route('/')
+def home():
+    return {
+        'message': 'Image Enhancement API',
+        'endpoints': {
+            'POST /enhance': 'Process images (4x upscale)',
+            'GET /health': 'Service status check'
+        }
+    }, 200
+
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=5000)