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import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
from PIL import Image
# Load your trained Xception model
model = tf.keras.models.load_model("your_xception_model.h5")
# Define the labels for your classification (example: if you have 3 classes)
class_labels = ['class1', 'class2', 'class3'] # Replace with your actual class names
def classify_image(img):
# Preprocess the image to fit the model input shape
img = img.resize((299, 299)) # Xception takes 299x299 input size
img = np.array(img) / 255.0 # Normalize the image
img = np.expand_dims(img, axis=0)
# Make prediction
predictions = model.predict(img)
predicted_class = np.argmax(predictions, axis=1)[0]
confidence = np.max(predictions)
return {class_labels[i]: float(predictions[0][i]) for i in range(len(class_labels))}, confidence
# Gradio interface
demo = gr.Interface(
fn=classify_image,
inputs=gr.inputs.Image(type="pil"),
outputs=[gr.outputs.Label(num_top_classes=len(class_labels)), "number"],
live=True
)
if __name__ == "__main__":
demo.launch()
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