| import gradio as gr | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing import image | |
| import numpy as np | |
| from PIL import Image | |
| model = load_model("Model_1.keras") | |
| def predict_image(img): | |
| img = img.resize((256, 256)) | |
| img_array = np.array(img) / 255.0 | |
| img_array = np.expand_dims(img_array, axis=0) | |
| prediction = model.predict(img_array) | |
| class_names = ["Fake","Real"] | |
| predicted_class = np.argmax(prediction[0]) | |
| confidence = prediction[0][predicted_class] | |
| return f"Prediction: {class_names[predicted_class]} (Confidence: {confidence:.2f})" | |
| interface = gr.Interface( | |
| fn=predict_image, | |
| inputs=gr.Image(type="pil"), | |
| outputs="text", | |
| title="Détection d'images générées par l'IA" | |
| ) | |
| interface.launch("share=True") | |