Update app.py

app.py

@@ -21,33 +21,36 @@ models = {
 
 modality_keys = ['Ultrasound', 'OCT', 'Fundus', 'Fluorescence']
 
-def classify_images(img_ultrasound=None, img_oct=None, img_fundus=None, img_fluorescence=None):
+def classify_images(img_ultrasound, img_oct, img_fundus, img_fluorescence):
     imgs = [img_ultrasound, img_oct, img_fundus, img_fluorescence]
     predictions = []
     detailed_predictions = []  # To store detailed predictions for each modality
+
+    provided_imgs = [img for img in imgs if img is not None]
     
-    if not any(imgs):  # Check if no images were provided
+    if not provided_imgs:  # Check if no images were provided
         return "Please upload at least one image for prediction."
     
-    # Convert images to PILImage and predict with each model, if provided
+    # Convert provided images to PILImage and predict with each model
     for img, key in zip(imgs, modality_keys):
-        if img is not None:  # Only proceed if an image was uploaded for this modality
+        if img is not None:
             pil_img = PILImage.create(img)
             pred, _, probs = models[key].predict(pil_img)
-            predictions.append(pred)
             prob_pred = probs.max()  # Get the highest probability score
-            detailed_predictions.append(f"{key}: {pred} ({prob_pred:.2f}%)")
+            predictions.append(pred)
+            detailed_predictions.append(f"{key}: {pred} ({prob_pred.item()*100:.2f}%)")
         else:
             detailed_predictions.append(f"{key}: No image provided")
-    # Handle final decision logic here as needed
+
+    # Final decision logic here, if applicable
     
     return "\n".join(detailed_predictions)
 
-# Define the Gradio interface inputs and outputs
-inputs = [gr.Image(label=f"{modality} Image", optional=True) for modality in modality_keys]
+# Define the Gradio interface inputs without using 'optional=True'
+inputs = [gr.Image(label=f"{modality} Image") for modality in modality_keys]
 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 (as available) and receive individual predictions with percentages and a binary prediction for Optic Disk Drusen presence. At least one image is required.")
+                    description="Upload images for each modality (as available). It's not required to upload an image for every input field. At least one image is required for a prediction.")
 intf.launch(share=True)