| import gradio as gr | |
| import tensorflow as tf | |
| from PIL import Image | |
| import numpy as np | |
| # Cargue su modelo (aquí el archivo Keras .h5 como ejemplo) | |
| # Load your model (here the Keras .h5 file as an example) | |
| # Carica il tuo modello (qui il file Keras .h5 come esempio) | |
| # Cargue su modelo (aquí el archivo Keras .h5 como ejemplo) | |
| # 加载你的模型（这里以 Keras .h5 文件为例） | |
| # Lade dein Modell (hier als Beispiel die Keras .h5 Datei) | |
| model = tf.keras.models.load_model('pokemon_model.keras') | |
| # Class names should match your dataset | |
| # I nomi delle classi devono corrispondere al tuo set di dati | |
| # Los nombres de las clases deben coincidir con su conjunto de datos. | |
| # Klassennamen, sollten deinem Dataset entsprechen | |
| # 类名应该与你的数据集匹配 | |
| class_names = ['Jolteon', 'Kakuna', 'Mr. Mime'] | |
| def classify_image(image): | |
| image = Image.fromarray(image.astype('uint8'), 'RGB') | |
| img = image.resize((150, 150)) | |
| img_array = tf.keras.preprocessing.image.img_to_array(img) | |
| img_array = tf.expand_dims(img_array, 0) # 创建一个批次, # Crea un batch, # Erstelle einen Batch, # Create a batch, # Crear un lote, | |
| predictions = model.predict(img_array) | |
| predicted_class = class_names[np.argmax(predictions[0])] | |
| confidence = np.max(predictions[0]) | |
| return {predicted_class: float(confidence)} | |
| image_input = gr.Image() # Rimuove il parametro `shape`, # Entferne den `shape` Parameter, # 删除`shape`参数， # Eliminar el parámetro `forma`, # Remove the `shape` parameter, | |
| label = gr.Label(num_top_classes=3) | |
| iface = gr.Interface( | |
| fn=classify_image, | |
| inputs=image_input, | |
| outputs=label, | |
| title='Pokémon Classifier', | |
| description='Upload an image of Jolteon, Kakuna, or Mr. Mime and the classifier will tell you which one it is and the confidence level of the prediction.').launch() | |
| #iface.launch() |