Describe what you changed

model.py

@@ -1,23 +1,18 @@
-from tensorflow.keras.applications import MobileNetV2
-from tensorflow.keras.layers import GlobalAveragePooling2D, Dense
-from tensorflow.keras.models import Model
-
-import tensorflow as tf
+from tensorflow.keras.models import load_model
+from PIL import Image
 import numpy as np
-from PIL import Image  # <-- Add this import
-
-
-def build_model(num_classes):
-    base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-    base_model.trainable = False
-
-    x = base_model.output
-    x = GlobalAveragePooling2D()(x)
-    x = Dense(128, activation='relu')(x)
-    output = Dense(num_classes, activation='softmax')(x)
 
-    model = Model(inputs=base_model.input, outputs=output)
-    return model
+# ✅ Define your class names in the exact training order
+class_names = [
+    "Pepper__bell___Bacterial_spot",
+    "Pepper__bell___healthy",
+    "Potato___healthy",
+    "Potato___Early_blight"
+]
+
+# ✅ Load your trained model
+# Make sure this file is in the same folder as model.py
+model = load_model("final_plantvillage_model.keras")
 
 def predict(image: Image.Image):
     """