Update app_2.py

app_2.py

@@ -1,3 +1,5 @@
+from fastai.vision.all import *
+import gradio as gr
 from pathlib import Path
 import pandas as pd
 
@@ -6,4 +8,45 @@ def get_x(row):
     return path_image_combined / f"{row['file_name']}.jpg"
 
 def get_y(row):
-    return row['Buried ODD']
+    return row['Buried ODD']
+
+# Load the models into a dictionary
+models = {
+    'Ultrasound': load_learner('ODDUltrasound.pkl'),
+    'OCT': load_learner('ODDOCT.pkl'),
+    'Fundus': load_learner('ODDfundus.pkl'),
+    'Fluorescence': load_learner('ODDfluorescence.pkl')
+}
+
+modality_keys = ['Ultrasound', 'OCT', 'Fundus', 'Fluorescence']
+
+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
+    
+    # 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
+    
+    # Majority vote for final decision
+    final_decision = max(set(predictions), key=predictions.count)
+    
+    detailed_predictions.append(f"Final Decision: {final_decision}")  # Add the final decision
+    
+    return "\n".join(detailed_predictions)  # Return detailed predictions as a single string
+
+# Define the Gradio interface inputs and outputs
+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 and receive individual predictions with percentages and a binary prediction for Optic Disk Drusen presence.")
+intf.launch(share=True)