Update app.py

app.py

@@ -1,17 +1,6 @@
 from fastai.vision.all import *
 import gradio as gr
 
-from pathlib import Path
-import pandas as pd
-
-def get_x(row):
-    # All files are assumed to be '.jpg', so we directly return the path with '.jpg' extension
-    return path_image_combined / f"{row['file_name']}.jpg"
-
-def get_y(row):
-    return row['Buried ODD']
-
-
 # Load the models into a dictionary
 models = {
     'Ultrasound': load_learner('ODDUltrasound.pkl'),
@@ -22,33 +11,39 @@ models = {
 
 modality_keys = ['Ultrasound', 'OCT', 'Fundus', 'Fluorescence']
 
-def classify_images(img_ultrasound, img_oct, img_fundus, img_fluorescence):
+def classify_images(img_ultrasound=None, img_oct=None, img_fundus=None, img_fluorescence=None):
     imgs = [img_ultrasound, img_oct, img_fundus, img_fluorescence]
     predictions = []
     detailed_predictions = []  # To store detailed predictions for each modality
     
-    # Convert images to PILImage and predict with each model
-    for img, key in zip(imgs, modality_keys):
-        pil_img = PILImage.create(img)
-        pred, _, probs = models[key].predict(pil_img)
-        predictions.append(pred)
-        
-        # 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
+    if not any(imgs):  # Check if no images were provided
+        return "Please upload at least one image for prediction."
     
-    # Majority vote for final decision
-    final_decision = max(set(predictions), key=predictions.count)
+    # Convert images to PILImage and predict with each model, if provided
+    for img, key in zip(imgs, modality_keys):
+        if img is not None:  # Only proceed if an image was uploaded for this modality
+            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}%)")
+        else:
+            detailed_predictions.append(f"{key}: No image provided")
     
-    detailed_predictions.append(f"Final Decision: {final_decision}")  # Add the final decision
+    # Calculate the final decision based on provided predictions, if any
+    if predictions:
+        final_decision = max(set(predictions), key=predictions.count)
+        detailed_predictions.append(f"Final Decision: {final_decision}")
+    else:
+        detailed_predictions.append("No final decision (Insufficient data)")
     
-    return "\n".join(detailed_predictions)  # Return detailed predictions as a single string
+    return "\n".join(detailed_predictions)
 
 # Define the Gradio interface inputs and outputs
-inputs = [gr.Image(label=f"{modality} Image") for modality in modality_keys]
+inputs = [gr.Image(label=f"{modality} Image", optional=True) 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 and receive individual predictions with percentages and a binary prediction for Optic Disk Drusen presence.")
-intf.launch(share=True)
+                    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.")
+intf.launch(share=True)