Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+
+# Load the saved model
+model = load_model('fruit_veg_classifier.h5')
+
+# Create a class mapping (based on your dataset)
+class_names = list(train_generator.class_indices.keys())
+
+# Define the prediction function
+def classify_image(image):
+    # Preprocess the image
+    image = image.resize((150, 150))
+    image = img_to_array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+    
+    # Make prediction
+    predictions = model.predict(image)
+    predicted_class = np.argmax(predictions)
+    return class_names[predicted_class]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.inputs.Image(shape=(150, 150)),
+    outputs=gr.outputs.Label(),
+    title="Fruit & Vegetable Classifier",
+    description="Upload an image of a fruit or vegetable, and the model will predict what it is!"
+)
+
+# Launch the app
+interface.launch()