Update app.py

app.py

@@ -10,32 +10,12 @@ def predict(img):
     pred,pred_idx,probs = learn.predict(img)
     prediction = str(pred)
     
-    # Provide explanation based on the prediction
-    if pred == "Cancer_positive":
-        explanation = "The model predicts that the image shows signs of cancer."
-    elif pred == "Cancer_negative":
-        explanation = "The model predicts that the image does not show signs of cancer."
-    elif pred == "implant_cancer_positive":
-        explanation = "The model predicts that the image shows signs of implant-related cancer."
-    elif pred == "implant_cancer_negative":
-        explanation = "The model predicts that the image does not show signs of implant-related cancer."
-    else:
-        explanation = "Unknown prediction."
-    
-    return prediction, explanation
-
-# Create the Gradio interface
-inputs = gr.inputs.Image(label="Upload an image")
-outputs = [
-    gr.outputs.Textbox(label="Prediction"),
-    gr.outputs.Textbox(label="Explanation")
-]
-    
 
 title = "Breast cancer detection with AI(Deep Transfer Learning)"
 description = "<p style='text-align: center'><b>As a radiologist or oncologist, it is crucial to know what is wrong with a breast x-ray image.<b><br><b>Upload the breast X-ray image to know what is wrong with a patients breast with or without inplant<b><p>"
 article="<p style='text-align: center'>Web app is built and managed by Addai Fosberg<b></p>"
 examples = ['img1.jpeg', 'img2.jpeg']
 enable_queue=True
+#interpretation='default'
 
 gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,enable_queue=enable_queue).launch()