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 import image
+import tensorflow as tf
+
+# Load the saved model
+model = load_model('acres-ppdc-01.keras')
+
+# Define the classes the model was trained on
+class_labels = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']
+
+def classify_potato_plant(img):
+    # Preprocess the image for the model
+    img = img.resize((256, 256))  # Resize to the same size the model was trained on
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = img / 255.0  # Normalize the image
+
+    # Make the prediction
+    predictions = model.predict(img)
+    predicted_class = np.argmax(predictions[0])
+    confidence = predictions[0][predicted_class]
+
+    # Get the predicted class and confidence score
+    return class_labels[predicted_class], confidence
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=classify_potato_plant,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=[gr.outputs.Label(num_top_classes=1), gr.outputs.Textbox(label="Confidence Score")]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()