img1

app.py

@@ -67,10 +67,6 @@ def predict_image(input):
     # Return input image path, predicted class label, and confidence score
     return input, {predicted_class_label: confidence_score}
 
-def predict_image(input_img):
-    predictions = model1.predict(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions}
-
 image_gradio_app = gr.Interface(
     fn=predict_image,
     inputs=gr.Image(label="Select waste candidate", sources=['upload', 'webcam'], type="pil"),