Create app.py

app.py

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+from fastapi import FastAPI, UploadFile, File
+from pydantic import BaseModel
+from transformers import AutoProcessor, CLIPModel
+from PIL import Image
+import torch
+
+# Charger le modèle CLIP et le processeur
+model = CLIPModel.from_pretrained("patrickjohncyh/fashion-clip")
+processor = AutoProcessor.from_pretrained("patrickjohncyh/fashion-clip")
+
+# Définir une classe de demande Pydantic
+class ImageTextRequest(BaseModel):
+    text: str
+    image: bytes
+
+# Créer une instance FastAPI
+app = FastAPI()
+
+# Définir la fonction de classification
+def classify_image_with_text(request: ImageTextRequest):
+    # Convertir l'image en format PIL
+    image_pil = Image.open(request.image)
+    
+    # Prétraiter l'image
+    image_tensor = processor(
+        text=request.text, images=image_pil, return_tensors="pt", padding=True
+    )
+    
+    # Effectuer la classification
+    outputs = model(**image_tensor)
+    logits_per_image = outputs.logits_per_image
+    probs = logits_per_image.softmax(dim=1)
+    predicted_class_index = probs.argmax(dim=1).item()
+    predicted_label = request.text.split(',')[predicted_class_index]
+    return predicted_label
+
+# Définir une route d'API
+@app.post("/classify")
+async def classify(request: ImageTextRequest):
+    return {"predicted_label": classify_image_with_text(request)}