Update app.py

app.py

@@ -32,7 +32,7 @@ import gradio as gr
 # Your code using these packages can follow here
 
 
-def remove_background_deeplab(image_path):
+def remove_background_deeplab(image):
     # Load the TensorFlow Lite model
     interpreter = tf.lite.Interpreter(model_path="2.tflite")
     interpreter.allocate_tensors()
@@ -40,13 +40,12 @@ def remove_background_deeplab(image_path):
     input_details = interpreter.get_input_details()
     output_details = interpreter.get_output_details()
 
-    # Load and preprocess the image
-    image = cv2.imread(image_path)
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    # Convert the Gradio Image object to a NumPy array
+    image_np = np.array(image)
 
     # Resize the image to match the expected input size of the model
     input_size = (257, 257)
-    image_resized = cv2.resize(image_rgb, input_size)
+    image_resized = cv2.resize(image_np, input_size)
 
     # Normalize the input image
     input_tensor = (image_resized / 127.5 - 1.0).astype(np.float32)
@@ -62,15 +61,16 @@ def remove_background_deeplab(image_path):
     mask = np.argmax(predictions, axis=-1)[0]
 
     # Resize the binary mask to match the shape of the image
-    binary_mask = cv2.resize(np.where(mask == 15, 1, 0).astype(np.uint8), (image.shape[1], image.shape[0]))
+    binary_mask = cv2.resize(np.where(mask == 15, 1, 0).astype(np.uint8), (image_np.shape[1], image_np.shape[0]))
 
     # Multiply the image with the binary mask to get the result
-    result = image * binary_mask[:, :, np.newaxis]
+    result = image_np * binary_mask[:, :, np.newaxis]
+
+    # Convert the result back to Gradio Image object
+    result_image = gr.Image(result)
+
+    return result_image
 
-    # Display the result or save it
-    cv2.imshow('Result', result)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
 
 # Example usage
 # remove_background_deeplab('images (2).jpeg')