init

app.py

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+import gradio as gr
+from tensorflow.keras import models, layers
+
+# Define model architecture
+input_shape = (None, image_size, image_size, channels)
+n_classes = 3
+
+model = models.Sequential([
+    resize_and_rescale,
+    data_augmentation,
+    layers.Conv2D(64, kernel_size=3, activation='relu', input_shape=input_shape),
+    layers.MaxPooling2D((2, 2)),
+    layers.Conv2D(64, kernel_size=(3, 3), activation='relu'),
+    layers.MaxPooling2D((2, 2)),
+    layers.Conv2D(64, kernel_size=(3, 3), activation='relu'),
+    layers.MaxPooling2D((2, 2)),
+    layers.Conv2D(64, kernel_size=(3, 3), activation='relu'),
+    layers.MaxPooling2D((2, 2)),
+    layers.Conv2D(64, kernel_size=(3, 3), activation='relu'),
+    layers.MaxPooling2D((2, 2)),
+    layers.Flatten(),
+    layers.Dense(64, activation='relu'),
+    layers.Dense(n_classes, activation='softmax')
+])
+
+# Load pre-trained weights
+model.load_weights('model911.h5')
+
+# Function to make predictions
+def classify_image(image):
+    # Preprocess image if necessary
+    # Make prediction
+    prediction = model.predict(image)
+    classes = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']
+    return {classes[i]: float(prediction[0][i]) for i in range(len(classes))}
+
+
+# Input component
+inputs = gr.inputs.Image(shape=(image_size, image_size))
+
+# Output component
+outputs = gr.outputs.Label(num_top_classes=3)
+
+# Create Gradio interface
+gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title='Potato Plant Diseases Classifier').launch()