Update app.py

app.py

@@ -30,9 +30,12 @@ def classify_images(img_ultrasound, img_oct, img_fundus, img_fluorescence):
     # Convert images to PILImage and predict with each model
     for img, key in zip(imgs, modality_keys):
         pil_img = PILImage.create(img)
-        pred, _, _ = models[key].predict(pil_img)
+        pred, _, probs = models[key].predict(pil_img)
         predictions.append(pred)
-        detailed_predictions.append(f"{key}: {pred}")  # Add the prediction with the modality name
+        
+        # Assuming binary classification, extract the probability for the predicted class
+        prob_pred = probs.max()  # Get the highest probability score
+        detailed_predictions.append(f"{key}: {pred} ({prob_pred:.2f}%)")  # Format prediction with percentage
     
     # Majority vote for final decision
     final_decision = max(set(predictions), key=predictions.count)
@@ -41,11 +44,11 @@ def classify_images(img_ultrasound, img_oct, img_fundus, img_fluorescence):
     
     return "\n".join(detailed_predictions)  # Return detailed predictions as a single string
 
-# Adjust the Gradio interface definition by removing 'shape' argument
+# Define the Gradio interface inputs and outputs
 inputs = [gr.Image(label=f"{modality} Image") for modality in modality_keys]
-output = gr.Text(label="Final Decision")
+output = gr.Text(label="Predictions")
 
 intf = gr.Interface(fn=classify_images, inputs=inputs, outputs=output,
                     title="ODD Detection from Multiple Imaging Modalities",
-                    description="Upload images for each modality and receive a binary prediction for Optic Disk Drusen presence.")
-intf.launch(share=True)
+                    description="Upload images for each modality and receive individual predictions with percentages and a binary prediction for Optic Disk Drusen presence.")
+intf.launch(share=True)