feat: Updated src/api.py

src/api.py

@@ -1,13 +1,13 @@
-from fastapi import FastAPI, UploadFile, File
-from PIL import Image
 import torch
-from torchvision import transforms
+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
 
 # Load the model
-model = Net()
-model.load_state_dict(torch.load("mnist_model.pth"))
-model.eval()
+trainer = Trainer()
+trainer.load_model("mnist_model.pth") # Assuming load_model method exists
 
 # Transform used for preprocessing the image
 transform = transforms.Compose([
@@ -23,6 +23,6 @@ async def predict(file: UploadFile = File(...)):
     image = transform(image)
     image = image.unsqueeze(0)  # Add batch dimension
     with torch.no_grad():
-        output = model(image)
+        output = trainer.get_model()(image)  # Assuming get_model method exists
         _, predicted = torch.max(output.data, 1)
     return {"prediction": int(predicted[0])}