added confidencesDDD

app.py

@@ -2,8 +2,7 @@ import gradio as gr
 from transformers import pipeline
 from PIL import Image
 
-# Load your model
-# You can add top_k=10 to get all class probabilities
+# Load your model and get all class results
 model = pipeline("image-classification", model="wellCh4n/tomato-leaf-disease-classification-vit", top_k=10)
 
 def classify(image):
@@ -13,23 +12,18 @@ def classify(image):
     # 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]
+    # --- THE FIX IS HERE ---
+    # Convert the list of dicts into a single dict that gr.Label expects:
+    # {'label_string': score_float, ...}
+    confidences = {p['label']: 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
-    }
+    return confidences
 
 # Define the interface
 app = gr.Interface(
     fn=classify,
     inputs=gr.Image(type="pil"),
-    # Use gr.Label() for rich output that can handle confidence scores
+    # gr.Label() can now correctly process the new output format
     outputs=gr.Label(), 
     title="Tomato Leaf Disease Analyzer",
     description="Upload a tomato leaf image to detect the disease."