Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the trained model
+model = tf.keras.models.load_model('/content/tato.h5')
+
+# Define class labels (update with your dataset's class names)
+class_labels = ['Late Blight', 'Early Blight', 'Healthy']
+
+# Define a prediction function
+def predict(image):
+    # Resize and preprocess the image
+    image = image.resize((224, 224))  # Resize to match model input size
+    image_array = np.array(image) / 255.0  # Normalize the image
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    
+    # Make predictions
+    predictions = model.predict(image_array)
+    predicted_class = class_labels[np.argmax(predictions)]  # Map prediction to class label
+    confidence = np.max(predictions)  # Get the highest confidence score
+    
+    return f"Predicted Class: {predicted_class}" #with confidence {confidence:.2f}"
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict,  # The prediction function
+    inputs=gr.Image(type="pil"),  # Input type (image as PIL object)
+    outputs="text",  # Output type (text)
+    title="Plant Disease Classifier",
+    description="Upload an image of a plant leaf to identify its condition."
+)
+
+# Launch the interface
+interface.launch()