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import gradio as gr
import tensorflow as tf
import cv2
import numpy as np
def classify_image(image):
# Read and resize the image
image = cv2.resize(image, (100, 100))
# Normalize the image
image = image / 255.0
# Expand dimensions to match the input shape of the model
image = np.expand_dims(image, axis=0)
# Perform prediction using the trained model
prediction = model.predict(image)
# Get the predicted label
label = classes[np.argmax(prediction[0])]
return label
# Load the pre-trained model
model = tf.keras.models.load_model('./my_model.h5')
# Define the class labels
classes = {
0: 'Bacterial_spot',
1: 'Early_blight',
2: 'Late_blight',
3: 'Leaf_Mold',
4: 'Septoria_leaf_spot',
5: 'Spider_mites',
6: 'Target_Spot',
7: 'Tomato_Yellow_Leaf_Curl_Virus',
8: 'Tomato_mosaic_virus',
9: 'healthy'
}
# Define the input and output interfaces for Gradio v3.x
input_interface = gr.Image() # Removed 'shape' argument
output_interface = gr.Textbox()
# Create the Gradio interface
gr.Interface(fn=classify_image, inputs=input_interface, outputs=output_interface).launch()
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