predict fxn

app.py

@@ -48,27 +48,27 @@ class_name = ['Apple___Apple_scab',
     'Tomato___Tomato_mosaic_virus',
     'Tomato___healthy']
 
+
+
 def predict_disease(image):
     """
-    Predict plant disease from uploaded image
+    Predict plant disease from uploaded image using same preprocessing as your working cv2 method
     """
     try:
-        # Preprocess the image
-        image = image.resize((128, 128))
+        # Convert PIL image to array like load_img method
+        image = image.convert("RGB")  # Ensure 3 channels
+        image = image.resize((128, 128))  # Resize
         input_arr = tf.keras.preprocessing.image.img_to_array(image)
-        input_arr = np.array([input_arr])  # Convert single image to a batch
-        input_arr = input_arr / 255.0  # Normalize if your model expects it
-        
-        # Make prediction
+        input_arr = np.array([input_arr])  # Convert single image to batch
+
+        # Predict
         prediction = model.predict(input_arr)
         result_index = np.argmax(prediction)
         confidence = prediction[0][result_index]
-        
-        # Get disease name
         disease_name = class_name[result_index]
-        
+
         return f"Disease: {disease_name}\nConfidence: {confidence:.2%}"
-    
+
     except Exception as e:
         return f"Error: {str(e)}"