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| import gradio as gr | |
| from tensorflow.keras.models import load_model | |
| import pandas as pd | |
| import numpy as np | |
| import cv2 | |
| dataCsv = pd.read_csv('birds.csv') | |
| def display_image_and_prediction(bird): | |
| model = load_model('mon_modele/mon_modele') | |
| generator = np.load('test_data.npy') | |
| bird_array_resized = cv2.resize(bird,(224, 224), 3) | |
| bird_array = np.expand_dims(bird_array_resized.astype('float32') / 255.0, axis=0) | |
| prediction = model.predict(bird_array) | |
| predicted_class = np.argmax(prediction) | |
| predicted_class_name = generator[predicted_class] | |
| scientificName = dataCsv.loc[dataCsv["labels"]==predicted_class_name,"scientific name"].iloc[0]#récupération du nom scientifique de cette oiseau | |
| return predicted_class_name, scientificName | |
| iface = gr.Interface(fn=display_image_and_prediction, inputs="image", outputs=["text","text"]) | |
| iface.launch(share=True) |