updated the csv

pages/Model_Evaluation.py

@@ -16,7 +16,9 @@ import streamlit as st
 import matplotlib.pyplot as plt
 from fpdf import FPDF
 from datasets import load_dataset
-from huggingface_hub import hf_hub_download  # ✅ NEW
+from huggingface_hub import hf_hub_download 
+import requests
+from io import BytesIO
 
 # ---- Streamlit State Initialization ----
 if 'stop_eval' not in st.session_state:
@@ -69,23 +71,27 @@ class DDRDataset(Dataset):
         return len(self.image_paths)
 
     def __getitem__(self, idx):
-        img_path = self.image_paths[idx]
+        img_url = self.image_paths[idx]
         label = int(self.labels[idx])
 
-        image = cv2.imread(img_path)
-        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        # Load image from URL
+        response = requests.get(img_url)
+        image = Image.open(BytesIO(response.content)).convert('RGB')
+        image = np.array(image)  # Convert PIL image to NumPy array for OpenCV functions
 
+        # Apply OpenCV preprocessing
         image = apply_median_filter(image)
         image = apply_clahe(image)
         image = apply_gamma_correction(image)
         image = apply_gaussian_filter(image)
 
-        image = Image.fromarray(image)
+        image = Image.fromarray(image)  # Back to PIL for transforms
+
         if self.transform:
             image = self.transform(image)
 
         return image, torch.tensor(label, dtype=torch.long)
-
+    
 # ---- Image Transforms ----
 val_transform = transforms.Compose([
     transforms.Resize((224, 224)),
@@ -98,20 +104,12 @@ val_transform = transforms.Compose([
 def load_test_data_from_huggingface():
     dataset = load_dataset(
         "Ci-Dave/DDR_dataset_train_test",
-        data_files={"test": "splits/test_labels.csv"},
+        data_files={"test": "splits/test_labels_updated.csv"},
         split="test"
     )
     df = dataset.to_pandas()
-
-    # 🔍 Check actual column names if needed:
-    # st.write(df.columns)
-
-    # 🔧 Use correct column name for image paths
-    df["new_path"] = "test/" + df["id_code"].astype(str)
-
     csv_path = "test_labels_temp.csv"
     df.to_csv(csv_path, index=False)
-
     dataset = DDRDataset(csv_path=csv_path, transform=val_transform)
     return DataLoader(dataset, batch_size=32, shuffle=False)