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)