Rename Classify_product.py to app.py

Classify_product.py → app.py

@@ -13,7 +13,7 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 main_model = models.resnet18(pretrained=False)
 num_ftrs = main_model.fc.in_features
 main_model.fc = nn.Linear(num_ftrs, 3)  # 3 classes: Soda drinks, Clothing, Mobile Phones
-main_model.load_state_dict(torch.load('Saved Model\Main_Classifier_best_model.pth', map_location=device))
+main_model.load_state_dict(torch.load('Main_Classifier_best_model.pth', map_location=device))
 main_model = main_model.to(device)
 main_model.eval()
 
@@ -25,7 +25,7 @@ def load_soda_drinks_model():
     model = models.resnet18(pretrained=False)
     num_ftrs = model.fc.in_features
     model.fc = nn.Linear(num_ftrs, 3)  # 3 classes: Miranda, Pepsi, Seven Up
-    model.load_state_dict(torch.load('Saved Model\Soda_drinks_best_model.pth', map_location=device))
+    model.load_state_dict(torch.load('Soda_drinks_best_model.pth', map_location=device))
     model = model.to(device)
     model.eval()
     return model
@@ -34,7 +34,7 @@ def load_clothing_model():
     model = models.resnet18(pretrained=False)
     num_ftrs = model.fc.in_features
     model.fc = nn.Linear(num_ftrs, 2)  # 2 classes: Pants, T-Shirt
-    model.load_state_dict(torch.load('Saved Model\Clothes_best_model.pth', map_location=device))
+    model.load_state_dict(torch.load('Clothes_best_model.pth', map_location=device))
     model = model.to(device)
     model.eval()
     return model
@@ -43,7 +43,7 @@ def load_mobile_phones_model():
     model = models.resnet18(pretrained=False)
     num_ftrs = model.fc.in_features
     model.fc = nn.Linear(num_ftrs, 2)  # 2 classes: Apple, Samsung
-    model.load_state_dict(torch.load('Saved Model\Phone_best_model.pth', map_location=device))
+    model.load_state_dict(torch.load('Phone_best_model.pth', map_location=device))
     model = model.to(device)
     model.eval()
     return model