Huggingface dataset and model

pages/Model_Evaluation.py

@@ -15,14 +15,14 @@ from sklearn.preprocessing import label_binarize
 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
 
 # ---- Streamlit State Initialization ----
 if 'stop_eval' not in st.session_state:
     st.session_state.stop_eval = False
-
 if 'evaluation_done' not in st.session_state:
     st.session_state.evaluation_done = False
-
 if 'trigger_eval' not in st.session_state:
     st.session_state.trigger_eval = False
 
@@ -33,7 +33,7 @@ st.markdown("<h2 style='color: #2E86C1;'>📈 Model Evaluation</h2>", unsafe_all
 class_names = ['No_DR', 'Mild', 'Moderate', 'Severe', 'Proliferative_DR']
 label_map = {label: idx for idx, label in enumerate(class_names)}
 
-# ---- Text Cleaning Function for PDF ----
+# ---- Text Cleaning Function ----
 def clean_text(text):
     return text.encode('utf-8', 'ignore').decode('utf-8')
 
@@ -75,7 +75,6 @@ class DDRDataset(Dataset):
         image = cv2.imread(img_path)
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-        # Apply preprocessing
         image = apply_median_filter(image)
         image = apply_clahe(image)
         image = apply_gamma_correction(image)
@@ -94,34 +93,36 @@ val_transform = transforms.Compose([
     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 ])
 
-# ---- Load Data (with caching) ----
+# ---- Load Data from Hugging Face (cached) ----
 @st.cache_resource
-def load_test_data(csv_path):
+def load_test_data_from_huggingface():
+    dataset = load_dataset("Ci-Dave/DDR_dataset_train_test/splits", data_files="test_labels.csv", split="test")
+    df = dataset.to_pandas()
+    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)
 
-# ---- Load Model (with caching) ----
+# ---- Load Model from Hugging Face (cached) ----
 @st.cache_resource
 def load_model():
+    model_path = hf_hub_download(repo_id="Ci-Dave/Densenet121", filename="Pretrained_Densenet-121.pth")
     model = models.densenet121(pretrained=False)
     model.classifier = nn.Linear(model.classifier.in_features, len(class_names))
-    model.load_state_dict(torch.load(r"D:\\DR_Classification\\training\\Pretrained_Densenet-121.pth", map_location=torch.device('cpu')))
+    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
     model.eval()
     return model
 
-# ---- Main UI Buttons ----
-csv_path = r"D:\\DR_Classification\\splits\\test_labels.csv"
+# ---- UI Buttons ----
 model = load_model()
-test_loader = load_test_data(csv_path)
+test_loader = load_test_data_from_huggingface()
 
 col1, col2 = st.columns([1, 1])
-
 with col1:
     if st.button("🚀 Start Evaluation"):
         st.session_state.stop_eval = False
         st.session_state.evaluation_done = False
         st.session_state.trigger_eval = True
-
 with col2:
     if st.button("🚩 Stop Evaluation"):
         st.session_state.stop_eval = True