added frontend cors

app/main.py

@@ -14,7 +14,7 @@ app = FastAPI()
 # Add CORS middleware to allow requests from your frontend
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["http://localhost:5173"],  # Set your frontend URL here
+    allow_origins=["http://localhost:5173","https://eye-disease-detection-frontend.vercel.app/"],  # Set your frontend URL here
     allow_credentials=True,
     allow_methods=["*"],  # Allow all HTTP methods (GET, POST, etc.)
     allow_headers=["*"],  # Allow all headers

model/eye_disease_model.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:27a2e05d406289e1ac51649c2605d30d32cf917a3c1a9044e03aa8bcbb35f42a
-size 45845690

utils/image_utils.py

@@ -1,53 +0,0 @@
-import torch
-from torchvision import transforms, models
-from PIL import Image
-import io
-import os
-import torch.nn as nn
-
-# Model path
-MODEL_PATH = r"D:\College Works\ML_project\Web\api\app\model\eye_disease_model.pth"
-
-# Load model
-def load_model(model_path=MODEL_PATH):
-    if not os.path.exists(model_path):  # Check if model path exists
-        raise FileNotFoundError(f"Model file not found at: {model_path}")
-    
-    # Define the model (with custom layers as per your training)
-    model = models.resnet18(pretrained=False)
-    
-    # Freeze the layers and modify the final layers
-    for param in model.parameters():
-        param.requires_grad = False
-        
-    num_ftrs = model.fc.in_features
-    model.fc = nn.Sequential(
-        nn.Linear(num_ftrs, 512),
-        nn.ReLU(),
-        nn.Dropout(0.5),
-        nn.Linear(512, 4)  # Assuming 4 output classes for your eye disease model
-    )
-
-    # Load the trained weights into the model
-    model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
-    
-    # Set the model to evaluation mode
-    model.eval()
-    
-    return model
-
-# Define image transformation (should match test-time transforms)
-def get_transform():
-    return transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                             std=[0.229, 0.224, 0.225])
-    ])
-
-# Process the incoming image
-def process_image(contents):
-    image = Image.open(io.BytesIO(contents)).convert("RGB")
-    transform = get_transform()
-    image = transform(image).unsqueeze(0)  # Add batch dimension
-    return image