Sandbox run src/api.py

src/api.py

@@ -1,22 +1,23 @@
 import torch
 from fastapi import FastAPI, File, UploadFile
-from main import Net # Importing Net class from main.py
-from main import Trainer # Importing Trainer class from main.py
 from PIL import Image
 from torchvision import transforms
 
+from main import Net # Importing Net class from main.py
+from main import Trainer # Importing Trainer class from main.py
+
 # Load the model
 trainer = Trainer()
-trainer.load_model("mnist_model.pth") # Assuming load_model method exists
+trainer.load_model("mnist_model.pth")  # Assuming load_model method exists
 
 # Transform used for preprocessing the image
-transform = transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+ [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
 app = FastAPI()
 
+
 @app.post("/predict/")
 async def predict(file: UploadFile = File(...)):
  image = Image.open(file.file).convert("L")