change path

pages/Upload_and_Predict.py

@@ -6,6 +6,7 @@ import numpy as np
 import pandas as pd
 from collections import defaultdict
 import os
+from datasets import load_dataset
 
 # Title
 st.markdown("<h2 style='color: #2E86C1;'>📷 Upload & Predict</h2>", unsafe_allow_html=True)
@@ -55,20 +56,19 @@ The tool also shows **sample images from the test set** for each class. You can
 # DR class names
 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='./splits/test_labels.csv'):
-    df = pd.read_csv(csv_path)
+def load_sample_images_from_csv():
+    dataset = load_dataset("Ci-Dave/test_labels.csv", split="test") 
     samples = defaultdict(list)
 
     for i in range(5):
         class_name = class_names[i]
-        class_samples = df[df['label'] == i].head(5)
-        for _, row in class_samples.iterrows():
-            img_path = row['new_path']
-            if os.path.exists(img_path):
+        class_samples = dataset.filter(lambda example: example['label'] == i)[:5]
+        for example in class_samples:
+            img_path = example['new_path']  # Ensure this column exists or adjust accordingly
+            if os.path.exists(img_path):  # Optional: skip if not local
                 samples[class_name].append(img_path)
-    
+
     return samples
 
 # Load pretrained model

requirements.txt

@@ -9,3 +9,4 @@ torch
 torchvision
 scikit-learn
 fpdf
+datasets