Upload 2 files

app.py

@@ -0,0 +1,60 @@
+import streamlit as st
+import torch
+from torchvision import transforms, models
+from PIL import Image
+from datasets import load_dataset
+import os
+
+# Vérification du GPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Charger le modèle MobileNetV2
+model = models.mobilenet_v2(weights=models.MobileNet_V2_Weights.DEFAULT)
+model.classifier = torch.nn.Sequential(
+    torch.nn.Dropout(0.2),
+    torch.nn.Linear(1280, 113)  # 113 classes pour Fruits-360
+)
+
+# Charger le modèle avec un chemin correct
+model_path = "model_fruits360.pth"
+model.load_state_dict(torch.load(model_path, map_location=device), strict=False)
+model.to(device)
+model.eval()
+
+# Charger le dataset Fruits-360 pour les labels
+dataset = load_dataset("PedroSampaio/fruits-360")
+labels = dataset['train'].features['label'].names
+
+# Transformations pour MobileNetV2
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Fonction pour prétraiter l'image
+def preprocess_image(image):
+    image = transform(image).unsqueeze(0).to(device)
+    return image
+
+# Interface Streamlit
+st.title("Prédiction des Fruits 🍎🍌🍊")
+
+# Upload d'image
+uploaded_file = st.file_uploader("Choisir une image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    # Affichage de l'image
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Image sélectionnée", use_column_width=True)
+
+    # Prédiction
+    image_tensor = preprocess_image(image)
+    with torch.no_grad():
+        outputs = model(image_tensor)
+        _, predicted_class = torch.max(outputs, 1)
+
+    fruit_name = labels[int(predicted_class.item())]
+
+    # Affichage du résultat
+    st.success(f"✅ Classe prédite : **{fruit_name}**")

requirements.txt

@@ -0,0 +1,5 @@
+torch
+torchvision
+streamlit
+datasets
+Pillow