Update app.py

app.py

@@ -148,7 +148,7 @@ def predict(img_path):
 
     # Move the model to CPU and load its state dict from the specified path
     inf_model.to(torch.device('cpu'))
-    inf_model.load_state_dict(torch.load('/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/resnet18_tinyfood_classifier.pth', map_location='cpu'))
+    inf_model.load_state_dict(torch.load('./resnet18_tinyfood_classifier.pth', map_location='cpu'))
 
     # Perform inference on the image using the model
     with torch.no_grad():
@@ -166,7 +166,7 @@ def predict(img_path):
     confidence = probabilities[0, idx].item() * 100  # Get confidence for the predicted class
 
     # Read nutrition data from a CSV file and match the predicted class
-    nutrition_data_path = '/content/drive/MyDrive/Assignment-Citra-SkillacademyAI/food-data.csv'
+    nutrition_data_path = './food-data.csv'
     df = pd.read_csv(nutrition_data_path)
 
     if pred_class.capitalize() in df["Makanan"].values: