Update README.md

README.md

@@ -78,8 +78,8 @@ from configuration_mirai import MiraiConfig
 from preprocessor import MiraiPreprocessor
 
 # Load model and configuration
-config = MiraiConfig.from_pretrained("Lab-Rasool/Mirai")
-model = MiraiModel.from_pretrained("Lab-Rasool/Mirai", config=config)
+config = MiraiConfig.from_pretrained(model_dir)
+model = MiraiModel.from_pretrained(model_dir, config=config)
 model.eval()
 
 # Initialize preprocessor
@@ -116,15 +116,16 @@ risk_factors = {
 
 risk_factors_tensor = preprocessor.prepare_risk_factors(risk_factors)
 
-# Prepare batch
+# Prepare batch - transpose to [views, channels, height, width]
+exam_tensor = exam_tensor.permute(1, 0, 2, 3)  # From [C, V, H, W] to [V, C, H, W]
 batch_images = exam_tensor.unsqueeze(0)
 batch_risk_factors = risk_factors_tensor.unsqueeze(0)
 
 # Create metadata for the batch
 batch_metadata = {
     'time_seq': torch.zeros(1, 4).long(),
-    'view_seq': torch.tensor([[0, 1, 2, 3]]),
-    'side_seq': torch.tensor([[0, 0, 1, 1]]),
+    'view_seq': torch.tensor([[0, 1, 0, 1]]),  # CC, MLO, CC, MLO
+    'side_seq': torch.tensor([[0, 0, 1, 1]]),  # L, L, R, R
 }
 
 # Run inference
@@ -147,9 +148,15 @@ with torch.no_grad():
         risk_pct = probabilities[year] * 100
         print(f"Year {year + 1} risk: {risk_pct:.2f}%")
 
-    # 5-year risk assessment
-    five_year_risk = probabilities[4] * 100
-    print(f"\n5-Year Cumulative Risk: {five_year_risk:.2f}%")
+    # Risk assessment for last available year
+    if len(probabilities) >= 5:
+        five_year_risk = probabilities[4] * 100
+        print(f"\n5-Year Cumulative Risk: {five_year_risk:.2f}%")
+    elif len(probabilities) > 0:
+        last_year = len(probabilities)
+        last_risk = probabilities[-1] * 100
+        print(f"\n{last_year}-Year Cumulative Risk: {last_risk:.2f}%")
+
 ```
 
 ### Method 2: Creating Sample Test Data