Update app.py

app.py

@@ -4,7 +4,7 @@ from tensorflow.keras.preprocessing import image
 import numpy as np
 
 # Load your Keras model
-model = load_model('vege_classifier_model.h5')
+model = load_model('model.h5')
 
 # Define the updated classes
 classes = [
@@ -16,10 +16,10 @@ classes = [
 # Define a prediction function
 def predict_image(img):
     # Preprocess the image to fit the model's input requirements
-    img = img.resize((224, 224))  # Resize image to 224x224 (adjust if your model expects a different size)
+    img = img.resize((224, 224))  # Assume the model expects 224x224 images
     img_array = image.img_to_array(img)
     img_array = np.expand_dims(img_array, axis=0)  # Create a batch
-    img_array /= 255.0  # Model expects pixels to be in [0, 1]
+    img_array /= 255.0  # Normalize the image to [0, 1]
 
     # Predict
     predictions = model.predict(img_array)
@@ -28,12 +28,11 @@ def predict_image(img):
     # Return the class name
     return classes[predicted_class[0]]
 
-# Create a Gradio interface
+# Create a simplified Gradio interface
 iface = gr.Interface(fn=predict_image, 
-                     inputs=gr.inputs.Image(shape=(224, 224)), 
-                     outputs="text", 
+                     inputs="image", 
+                     outputs="label", 
                      title="Vegetable Image Classifier",
                      description="Classify images of various vegetables into 15 categories: Bean, Bitter Gourd, Bottle Gourd, Brinjal, Broccoli, Cabbage, Capsicum, Carrot, Cauliflower, Cucumber, Papaya, Potato, Pumpkin, Radish, Tomato.")
 
-if __name__ == "__main__":
-    iface.launch()
+if __name__ == "__main__"