Update app.py

app.py

@@ -1,21 +1,17 @@
 import torch
-from torchvision import transforms, models
+from torchvision import transforms
 from PIL import Image
-import json
 import streamlit as st
+import json
 
 # Charger les noms des classes
 with open("class_names.json", "r") as f:
     class_names = json.load(f)
 
-
 # Charger le modèle
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = models.efficientnet_b7(pretrained=False)  # Initialize the model architecture
-model.load_state_dict(torch.load("efficientnet_b7_best.pth", map_location=device))  # Load the saved weights
-model = model.to(device)  # Move model to the appropriate device (GPU/CPU)
-model.eval()  # Set the model to evaluation mode
-
+model = torch.load("efficientnet_b7_best.pth", map_location=device)
+model.eval()  # Mode évaluation
 
 # Définir la taille de l'image
 image_size = (224, 224)