fixed path to work in huggingface

pages/Dataset.py

@@ -44,8 +44,8 @@ with tab2:
     st.markdown("### 📊 Training Data Class Distribution")
     
     # CSV path and image folder path (adjust as needed)
-    CSV_PATH = r"D:\DR_Classification\dataset\DR_grading.csv"
-    IMG_FOLDER = r"D:\DR_Classification\dataset\images"  # Folder where all images are stored
+    CSV_PATH = "./dataset/DR_grading.csv"
+    IMG_FOLDER = "./dataset/images"  # Folder where all images are stored
 
     # Load CSV
     df = pd.read_csv(CSV_PATH)

pages/Model_Evaluation.py

@@ -105,12 +105,12 @@ def load_test_data(csv_path):
 def load_model():
     model = models.densenet121(pretrained=False)
     model.classifier = nn.Linear(model.classifier.in_features, len(class_names))
-    model.load_state_dict(torch.load(r"Model/Pretrained_Densenet-121.pth", map_location=torch.device('cpu')))
+    model.load_state_dict(torch.load("./Model/Pretrained_Densenet-121.pth", map_location=torch.device('cpu')))
     model.eval()
     return model
 
 # ---- Main UI Buttons ----
-csv_path = r"D:\DR_Classification\splits\test_labels.csv"
+csv_path = "./splits/test_labels.csv"
 model = load_model()
 test_loader = load_test_data(csv_path)
 

pages/Upload_and_Predict.py

@@ -57,7 +57,7 @@ class_names = ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR']
 
 # Load sample images from CSV with proper label mapping
 @st.cache_data
-def load_sample_images_from_csv(csv_path=r'D:\DR_Classification\splits\test_labels.csv'):
+def load_sample_images_from_csv(csv_path='./splits/test_labels.csv'):
     df = pd.read_csv(csv_path)
     samples = defaultdict(list)
 
@@ -76,7 +76,7 @@ def load_sample_images_from_csv(csv_path=r'D:\DR_Classification\splits\test_labe
 def load_model():
     model = models.densenet121(pretrained=False)
     model.classifier = torch.nn.Linear(model.classifier.in_features, len(class_names))
-    model.load_state_dict(torch.load("Model/Pretrained_Densenet-121.pth", map_location='cpu'))
+    model.load_state_dict(torch.load("./Model/Pretrained_Densenet-121.pth", map_location='cpu'))
     model.eval()
     return model