Update app.py

app.py

@@ -25,10 +25,10 @@ def preprocess_image(image):
     img = np.expand_dims(img, axis=0)
     return img
 
-# Define the function to make predictions on an image
 
+# Define the function to make predictions on an image
 
-def predict(image):
+def predict(image_path_or_pil_image):
     # Load the TFLite model
     interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
     interpreter.allocate_tensors()
@@ -38,7 +38,12 @@ def predict(image):
     output_details = interpreter.get_output_details()
 
     # Preprocess image
-    img = preprocess_image(image)
+    if isinstance(image_path_or_pil_image, str):
+        img = cv2.imread(image_path_or_pil_image)
+        img = preprocess_image(img, input_details[0]['shape'][1:3])
+    else:
+        img = np.array(image_path_or_pil_image)
+        img = preprocess_image(img)
 
     # Set input tensor
     interpreter.set_tensor(input_details[0]['index'], img)
@@ -65,12 +70,10 @@ def predict(image):
 
 
 # Define the Gradio interface
-inputs = gr.inputs.ImageUploader(
-    label="Select an image or capture using camera")
+inputs = gr.inputs.Image()
 outputs = gr.outputs.Label(num_top_classes=5)
 interface = gr.Interface(fn=predict, inputs=inputs,
                          outputs=outputs, capture_session=True)
 
-
 # Run the interface
 interface.launch()