fix issues

main.py

@@ -14,7 +14,7 @@ import torch
 import logging
 from typing import List
 import httpx
-
+from concurrent.futures import ProcessPoolExecutor
 
 app = FastAPI()
 
@@ -61,6 +61,23 @@ model_pipelines = {}
 base_model = ResNet("resnet152", num_output_neurons=2).to(device)
 
 
+def process_single_image(image_url, model):
+    try:
+        response = requests.get(image_url)
+        image = Image.open(BytesIO(response.content))
+        processed_image = process_image(image, size=image_size)
+        image_tensor = transforms.ToTensor()(processed_image).unsqueeze(0)
+
+        with torch.no_grad():
+            outputs = model(image_tensor)
+            probabilities = torch.nn.functional.softmax(outputs, dim=1)
+            predicted_probabilities = probabilities.numpy().tolist()
+            confidence = round(predicted_probabilities[0][1], 2)
+        return {"imageUrl": image_url, "confidence": confidence}
+    except Exception as e:
+        return {"imageUrl": image_url, "error": str(e)}
+
+
 @app.on_event("startup")
 async def load_models():
     # Charger les modèles au démarrage
@@ -165,25 +182,6 @@ class BatchPredictRequest(BaseModel):
 #     # Return the results as JSON
 #     return JSONResponse(content={"results": results})
 
-from concurrent.futures import ProcessPoolExecutor
-
-
-def process_single_image(image_url, model):
-    try:
-        response = requests.get(image_url)
-        image = Image.open(BytesIO(response.content))
-        processed_image = process_image(image, size=image_size)
-        image_tensor = transforms.ToTensor()(processed_image).unsqueeze(0)
-
-        with torch.no_grad():
-            outputs = model(image_tensor)
-            probabilities = torch.nn.functional.softmax(outputs, dim=1)
-            predicted_probabilities = probabilities.numpy().tolist()
-            confidence = round(predicted_probabilities[0][1], 2)
-        return {"imageUrl": image_url, "confidence": confidence}
-    except Exception as e:
-        return {"imageUrl": image_url, "error": str(e)}
-
 
 @app.post("/batch_predict")
 async def batch_predict(request: BatchPredictRequest):