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import streamlit as st
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
from PIL import Image
# Charger le modèle pré-entraîné
model = load_model('plant_diseases.h5')
# Classes de labels (remplacez par vos propres classes)
class_labels = ['Piment: Bacterial_spot',
'Piment: healthy',
'Pomme de terre: Early_blight',
'Pomme de terre: Late_blight',
'Pomme de terre: Healthy', 'Tomate: Bacterial Spot', 'Tomate: Early Blight', 'Tomate: Late Blight', 'Tomate: Leaf mold', 'Tomate: Septoria leaf spot', 'Tomate: Siper mites', 'Tomate: Spot', "Tomate: Yellow Leaf Curl", 'Tomate: Virus Mosaïque', 'Tomate: Healthy']
def preprocess_image(image, image_size=(224, 224)):
# Convertir l'image en niveaux de gris
image = np.array(image.convert('L'))
# Redimensionner l'image
image = cv2.resize(image, image_size)
# Redimensionner pour le modèle
image = img_to_array(image)
image /= 255.0
image = np.expand_dims(image, axis=0)
return image
st.title("Classification des Maladies des Plantes")
st.write("Téléchargez une image de plante pour la classification")
uploaded_file = st.file_uploader("Choisissez une image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Afficher l'image téléchargée
image = Image.open(uploaded_file)
st.image(image, caption='Image téléchargée', use_column_width=True)
st.write("Classification en cours...")
# Prétraiter l'image
processed_image = preprocess_image(image)
# Faire la prédiction
predictions = model.predict(processed_image)
probabilities = predictions[0]
# Afficher les probabilités de chaque classe
for i, label in enumerate(class_labels):
if probabilities[i] > 0:
st.write(f"{label}: {probabilities[i]:.2f}")
# Afficher le résultat de la classe prédite
predicted_class = class_labels[np.argmax(probabilities)]
st.write(f"Classe prédite: {predicted_class}")
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