Update README.md

README.md

@@ -44,30 +44,41 @@ pip install huggingface-hub torch torchvision pydicom
 ```python
 from huggingface_hub import snapshot_download
 import sys
+import os
 
 # Download model
 model_path = snapshot_download(repo_id="Lab-Rasool/sybil")
 sys.path.append(model_path)
 
 # Import model
-from modeling_sybil_wrapper import SybilHFWrapper
+from modeling_sybil_hf import SybilHFWrapper
 from configuration_sybil import SybilConfig
 
 # Initialize
 config = SybilConfig()
 model = SybilHFWrapper(config)
 
-# Prepare your DICOM files (CT scan slices)
-dicom_paths = ["scan1.dcm", "scan2.dcm", ...]  # Replace with actual paths
+dicom_dir = "path/to/volume"
+dicom_paths = [os.path.join(dicom_dir, f) for f in os.listdir(dicom_dir) if f.endswith('.dcm')]
+
+print(f"Found {len(dicom_paths)} DICOM files for prediction.")
 
 # Get predictions
 output = model(dicom_paths=dicom_paths)
 risk_scores = output.risk_scores.numpy()
 
 # Display results
-print("Lung Cancer Risk Predictions:")
+print("\nLung Cancer Risk Predictions:")
+print(f"Risk scores shape: {risk_scores.shape}")
+
+# Handle both single and batch predictions
+if risk_scores.ndim == 2:
+    # Batch predictions - take first sample
+    risk_scores = risk_scores[0]
+
 for i, score in enumerate(risk_scores):
-    print(f"Year {i+1}: {score*100:.1f}%")
+    print(f"Year {i+1}: {float(score)}")
+
 ```
 
 ## 📊 Example with Demo Data
@@ -121,17 +132,7 @@ output = model(dicom_paths=dicom_files)
 
 # Show results
 for i, score in enumerate(output.risk_scores.numpy()):
-    print(f"Year {i+1}: {score*100:.1f}% risk")
-```
-
-Expected output for demo data:
-```
-Year 1: 2.2% risk
-Year 2: 4.5% risk
-Year 3: 7.2% risk
-Year 4: 7.9% risk
-Year 5: 9.6% risk
-Year 6: 13.6% risk
+    print(f"Year {i+1}: {float(score)}")
 ```
 
 ## 📈 Performance Metrics