Update app.py

app.py

@@ -1,4 +1,3 @@
-import streamlit as st
 import torch
 from torchvision import transforms
 from efficientnet_pytorch import EfficientNet
@@ -7,17 +6,17 @@ from PIL import Image
 
 # Authenticate and download the custom model from Hugging Face Spaces
 fs = HfFileSystem()
-model_path = 'dhhd255/efficient_net_parkinsons/best_model.pth'
+model_path = 'dhhd255/efficientnet_b3/efficientnet_b3.pt'
 with fs.open(model_path, 'rb') as f:
     model_content = f.read()
 
 # Save the model file to disk
-with open('best_model.pth', 'wb') as f:
+with open('efficientnet_b3.pt', 'wb') as f:
     f.write(model_content)
 
 # Load your custom model onto the CPU
 model = EfficientNet.from_pretrained('efficientnet-b3')
-model.load_state_dict(torch.load('best_model.pth', map_location=torch.device('cpu')))
+model.load_state_dict(torch.load('efficientnet_b3.pt', map_location=torch.device('cpu')))
 model.eval()
 
 # Define a function that takes an image as input and uses the model for inference