Create app.py

app.py

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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
+
+# Load the pre-trained model
+model = load_model('plant_diseases.h5')
+
+# Class labels (replace with your own 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: Spider mites', 
+    'Tomate: Spot', 
+    'Tomate: Yellow Leaf Curl', 
+    'Tomate: Virus Mosaïque', 
+    'Tomate: Healthy'
+]
+
+def preprocess_image(image, image_size=(224, 224)):
+    # Convert image to grayscale
+    image = np.array(image.convert('L'))
+    # Resize image
+    image = cv2.resize(image, image_size)
+    # Prepare image for the model
+    image = img_to_array(image)
+    image /= 255.0
+    image = np.expand_dims(image, axis=0)
+    return image
+
+# Streamlit app setup
+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:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Image téléchargée', use_column_width=True)
+
+    st.write("Classification en cours...")
+
+    # Preprocess the image
+    processed_image = preprocess_image(image)
+
+    # Make predictions
+    predictions = model.predict(processed_image)
+    probabilities = predictions[0]
+
+    # Display probabilities for each class
+    for i, label in enumerate(class_labels):
+        if probabilities[i] > 0:
+            st.write(f"{label}: {probabilities[i]:.2f}")
+
+    # Show predicted class
+    predicted_class = class_labels[np.argmax(probabilities)]
+    st.write(f"Classe prédite: {predicted_class}")