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.ipynb_checkpoints/main-checkpoint.py

@@ -5,8 +5,11 @@ pipeline = pipeline("image-classification", model="jhoppanne/SkinCancerClassifie
 
 def predict(input_img):
     pred = pipeline(input_img)
-    label = ['Benign','Indeterminate','Malignant']
-    answer = f'We predict that you have {label[pred['label']]} type of skin cancer,\n with confidence score of: {pred['score']*100:.2f}%'
+    labels = ['Benign','Indeterminate','Malignant']
+    label_id = int(pred[0]['label'])
+    label = labels[label_id]
+    score = pred[0]['score']
+    answer = f'We predict that you have {label} type of skin cancer,\nwith confidence score of : {score*100:.2f}%'
     return answer
 
 gradio_app = gr.Interface(

main.py

@@ -5,8 +5,11 @@ pipeline = pipeline("image-classification", model="jhoppanne/SkinCancerClassifie
 
 def predict(input_img):
     pred = pipeline(input_img)
-    label = ['Benign','Indeterminate','Malignant']
-    answer = f'We predict that you have {label[pred['label']]} type of skin cancer,\n with confidence score of: {pred['score']*100:.2f}%'
+    labels = ['Benign','Indeterminate','Malignant']
+    label_id = int(pred[0]['label'])
+    label = labels[label_id]
+    score = pred[0]['score']
+    answer = f'We predict that you have {label} type of skin cancer,\nwith confidence score of : {score*100:.2f}%'
     return answer
 
 gradio_app = gr.Interface(