src/predict.py

from features.build_features import TextPreprocessor
from features.build_features import ImagePreprocessor
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array, load_img
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
import json
from tensorflow import keras
import pandas as pd
import argparse
from keras import backend as K
from tools import f1_m, load_model, check_and_download
import time

# ... (omitted lines)

def main():
    parser = argparse.ArgumentParser(description= "Input data")
    
    parser.add_argument("--dataset_path", default = "data/predict/X_test_update.csv", type=str,help="File path for the input CSV file.")
    parser.add_argument("--images_path", default = "data/predict/image_test", type=str,  help="Base path for the images.")
    parser.add_argument("--prediction_path", default = "data/predict/predictions.csv",  type=str,  help="Path for the prediction results.")
    args = parser.parse_args()

    # Charger les configurations et modèles
    check_and_download("models", "tokenizer_config.json")
    with open("models/tokenizer_config.json", "r", encoding="utf-8") as json_file:
        tokenizer_config = json_file.read()
    tokenizer = keras.preprocessing.text.tokenizer_from_json(tokenizer_config)

    rnn = load_model("models","best_rnn_model.h5")
    vgg16 = load_model("models","best_vgg16_model.h5")

    check_and_download("models", "best_weights.json")
    with open("models/best_weights.json", "r") as json_file:
        best_weights = json.load(json_file)

    check_and_download("models", "mapper.json")
    with open("models/mapper.json", "r") as json_file:
        mapper = json.load(json_file)
        
    
    predictor = Predict(
        tokenizer=tokenizer,
        rnn=rnn,
        vgg16=vgg16,
        best_weights=best_weights,
        mapper=mapper,
        filepath= args.dataset_path,
        imagepath = args.images_path,
    )

    # Création de l'instance Predict et exécution de la prédiction
    t_debut = time.time()
    predictions = predictor.predict()
    
    # Sauvegarde des prédictions
    # with open("data/preprocessed/predictions.json", "w", encoding="utf-8") as json_file:
    #     json.dump(predictions, json_file, indent=2)
    predictions.to_csv(args.prediction_path, index=False)
    
    t_fin = time.time()
    print("Durée de la prédiction : {:.2f}".format(t_fin - t_debut))

if __name__ == "__main__":
    main()