Update app.py

app.py

@@ -6,20 +6,23 @@ import joblib
 # Load the trained model
 model = joblib.load("model.pkl")
 
-# Define class labels 
 class_names = ["Monkeypox", "Not Monkeypox"]
 
 def predict(img):
     # Resize image to match model input size
-    img = img.resize((224,224))
+    img = img.resize((224, 224))
     img = np.array(img) / 255.0  # normalize
     img = np.expand_dims(img, axis=0)
 
-    # Predict
     preds = model.predict(img)
 
-    # Convert predictions to dictionary
-    return {class_names[i]: float(preds[0][i]) for i in range(len(class_names))}
+    probs = preds[0]
+    result = {class_names[i]: float(probs[i]) for i in range(len(class_names))}
+
+    pred_idx = np.argmax(probs)
+    pred_label = class_names[pred_idx]
+
+    return result
 
 demo = gr.Interface(
     fn=predict,