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import gradio as gr
import tensorflow as tf
import numpy as np
# Load the model
model = tf.keras.models.load_model('model.h5')
# Define the class names
class_names = {
0: 'Glioma',
1: 'Menin',
2: 'Tumor'
}
def classify_image(image):
# Preprocess the image
img_array = tf.image.resize(image, [200, 200])
img_array = tf.expand_dims(img_array, 0) / 255.0
# Make a prediction
prediction = model.predict(img_array)
predicted_class = tf.argmax(prediction[0], axis=-1)
confidence = np.max(prediction[0])
return class_names[predicted_class.numpy()], confidence
iface = gr.Interface(
fn=classify_image,
inputs="image",
outputs=["text", "number"],
examples=[
['examples/0.jpg'],
['examples/1.jpg'],
['examples/2.jpg'],
])
iface.launch()
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