import tensorflow as tffrom tensorflow.keras.preprocessing import imageimport matplotlib.pyplot as plt# Charger un modèle pré-entraîné pour la reconnaissance d'imagemodel = tf.keras.applications.VGG16(weights='imagenet', include_top=True)# Fonction pour charger et prétraiter l'imagedef load_and_preprocess_image(img_path):img = image.load_img(img_path, target_size=(224, 224))img_array = image.img_to_array(img)img_array = tf.expand_dims(img_array, 0)return tf.keras.applications.vgg16.preprocess_input(img_array)# Fonction pour prédire les composants de l'imagedef predict_components(img_path):img = load_and_preprocess_image(img_path)predictions = model.predict(img)return tf.keras.applications.vgg16.decode_predictions(predictions, top=5)[0]# Fonction pour générer un schéma (simplifié)def generate_schema(predictions):plt.figure(figsize=(10, 6))plt.title("Generated Schema")for pred in predictions:plt.text(0.5, 0.5, pred[1], fontsize=12)plt.show()# Exemple d'utilisationimg_path = 'path_to_your_image.png'predictions = predict_components(img_path)generate_schema
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