| import streamlit as st |
| import tensorflow as tf |
| from PIL import Image |
| import numpy as np |
|
|
| |
| with st.spinner("Chargement des modèles..."): |
| try: |
| model_infect = tf.keras.models.load_model("src/exo1.keras") |
| model_animals = tf.keras.models.load_model("src/exo2.keras") |
| st.success("Modèles chargés avec succès ") |
| except Exception as e: |
| st.error(f"Erreur lors du chargement des modèles : {e}") |
| st.stop() |
|
|
| |
| def predict(model, img_array, classes): |
| prediction = model.predict(img_array) |
| index = np.argmax(prediction) |
| return classes[index], float(prediction[0][index]) |
|
|
| |
| st.title("Classification d’images") |
|
|
| |
| option = st.selectbox("Choisissez le modèle :", ("Infecté / Non Infecté", "Chat / Chien")) |
|
|
| |
| uploaded_file = st.file_uploader("Uploader une image", type=["jpg", "jpeg", "png"]) |
|
|
| if uploaded_file: |
| try: |
| image = Image.open(uploaded_file).convert("RGB") |
| st.image(image, caption="Image chargée", use_container_width=True) |
|
|
| |
| if st.button("Prédire"): |
| with st.spinner("Prétraitement de l’image..."): |
| if option == "Infecté / Non Infecté": |
| image = image.resize((50, 50)) |
| model = model_infect |
| classes = ["Non Infecté", "Infecté"] |
| else: |
| image = image.resize((128, 128)) |
| model = model_animals |
| classes = ["Chat", "Chien"] |
|
|
| img_array = np.array(image) / 255.0 |
| img_array = np.expand_dims(img_array, axis=0) |
|
|
| with st.spinner("Prédiction en cours..."): |
| label, confidence = predict(model, img_array, classes) |
|
|
| st.success(f"✅ Classe prédite : **{label}** avec une confiance de **{confidence:.2f}**") |
|
|
| except Exception as e: |
| st.error(f"❌ Erreur lors du traitement : {e}") |
|
|
