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app.py

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+import torch
+from torchvision import transforms
+from PIL import Image
+import gradio as gr
+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 = torch.load("efficientnet_b7_best.pth", map_location=device)
+model.eval()  # Mode évaluation
+
+# Définir la taille de l'image
+image_size = (224, 224)
+
+# Transformation pour l'image
+class GrayscaleToRGB:
+    def __call__(self, img):
+        return img.convert("RGB")
+
+valid_test_transforms = transforms.Compose([
+    transforms.Grayscale(num_output_channels=1),
+    transforms.Resize(image_size),
+    GrayscaleToRGB(),  # Conversion en RGB
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+# Fonction de prédiction
+def predict_image(image):
+    image_tensor = valid_test_transforms(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        outputs = model(image_tensor)
+        _, predicted_class = torch.max(outputs, 1)
+    predicted_label = class_names[predicted_class.item()]
+    return predicted_label
+
+# Interface Gradio
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Prédiction d'images avec PyTorch",
+    description="Chargez une image pour obtenir une prédiction de classe."
+)
+
+if __name__ == "__main__":
+    interface.launch()

class_names.json

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+[
+    "disgust",
+    "happy",
+    "sad"
+]

efficientnet_b7_bestv1.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8c67597a1fddfac92cf0b7dc0c1b18bc6e18b56cc01733dd1c360a3b5355958e
+size 262038038

requirements.txt

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+torch
+torchvision
+gradio
+Pillow