added confidences

app.py

@@ -3,22 +3,37 @@ from transformers import pipeline
 from PIL import Image
 
 # Load your model
-model = pipeline("image-classification", model="wellCh4n/tomato-leaf-disease-classification-vit")
+# You can add top_k=10 to get all class probabilities
+model = pipeline("image-classification", model="wellCh4n/tomato-leaf-disease-classification-vit", top_k=10)
 
 def classify(image):
     if not isinstance(image, Image.Image):
         image = Image.fromarray(image)
-    result = model(image)
-    return result[0]['label']
+        
+    # The model returns a list of dictionaries, e.g., [{'label': '...', 'score': ...}]
+    predictions = model(image)
+    
+    # The gr.Label component and our Next.js client expect a 'confidences' key
+    # with a list of dicts that have a 'confidence' key (not 'score').
+    # We will reformat the output to match this.
+    confidences = [{'label': p['label'], 'confidence': p['score']} for p in predictions]
+    
+    # Return a dictionary containing the top label and the full list of confidences.
+    # This is the format our Next.js API is expecting.
+    return {
+        'label': predictions[0]['label'],
+        'confidences': confidences
+    }
 
 # Define the interface
 app = gr.Interface(
     fn=classify,
     inputs=gr.Image(type="pil"),
-    outputs="label",
+    # Use gr.Label() for rich output that can handle confidence scores
+    outputs=gr.Label(), 
     title="Tomato Leaf Disease Analyzer",
     description="Upload a tomato leaf image to detect the disease."
 )
 
-# ✅ Disable SSR to enable API correctly
-app.launch(show_api=True)
+# Launch the app
+app.launch(show_api=True)