run aeon/paladin

app.py

@@ -25,39 +25,28 @@ def analyze_slide(slide_path, site_type):
         max_num_holes=2
     )
 
-#    features, _ = get_features(coords, slide_path, attrs, model_type=ModelType.OPTIMUS, use_gpu=USE_GPU, batch_size=64, num_workers=NUM_WORKERS)
-#
-#    # Step 3: Run Aeon to predict histology
-#    aeon_results, _ = run_aeon(
-#    │   features=features,
-#    │   model_path="data/aeon_model.pkl",
-#    │   metastatic=(site_type == "Metastatic"),
-#    │   batch_size=8,
-#    │   num_workers=NM_WORKERS,
-#    │   use_cpu=not USE_GPU,
-#    )
-#
-#    # Step 4: Run Paladin to predict biomarkers
-#    paladin_results = run_paladin(
-#    │   features=features,
-#    │   model_map_path="data/paladin_model_map.csv",
-#    │   aeon_results=aeon_results,
-#    │   metastatic=(site_type == "Metastatic"),
-#    │   batch_size=8,
-#    │   num_workers=NUM_WORKERS,
-#    │   use_cpu=not USE_GPU,
-#    )
-    # Dummy results for demonstration purposes
-    # Replace these with actual model inference results
-    aeon_results = pd.DataFrame({
-        "Cancer Subtype": ["Subtype A", "Subtype B"],
-        "Confidence": [0.95, 0.85]
-    })
-    paladin_results = pd.DataFrame({
-        "Cancer Subtype": ["Subtype A", "Subtype A"],
-        "Biomarker": ["Biomarker 1", "Biomarker 2"],
-        "Score": [0.9, 0.8]
-    })
+    features, _ = get_features(coords, slide_path, attrs, model_type=ModelType.OPTIMUS, use_gpu=USE_GPU, batch_size=64, num_workers=NUM_WORKERS)
+
+    # Step 3: Run Aeon to predict histology
+    aeon_results, _ = run_aeon(
+    │   features=features,
+    │   model_path="data/aeon_model.pkl",
+    │   metastatic=(site_type == "Metastatic"),
+    │   batch_size=8,
+    │   num_workers=NM_WORKERS,
+    │   use_cpu=not USE_GPU,
+    )
+
+    # Step 4: Run Paladin to predict biomarkers
+    paladin_results = run_paladin(
+    │   features=features,
+    │   model_map_path="data/paladin_model_map.csv",
+    │   aeon_results=aeon_results,
+    │   metastatic=(site_type == "Metastatic"),
+    │   batch_size=8,
+    │   num_workers=NUM_WORKERS,
+    │   use_cpu=not USE_GPU,
+    )
 
     return aeon_results, paladin_results