Update app.py

app.py

@@ -11,6 +11,7 @@ import torch.nn as nn
 import torchvision.models as models
 
 from fastapi import FastAPI, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
 
 # =========================================
@@ -38,6 +39,17 @@ app = FastAPI(
     version="1.0"
 )
 
+# =========================================
+# CORS
+# =========================================
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 # =========================================
 # LOAD NIFTI
 # =========================================
@@ -346,6 +358,7 @@ model3d = load_model(
 
 # =========================================
 # SINGLE PREDICTION
+# NOTE: All confidence and probability values are 0.0–1.0 (NOT percentages)
 # =========================================
 def predict_model(model, tensor):
 
@@ -372,12 +385,13 @@ def predict_model(model, tensor):
         "class_id":
             pred_idx,
 
+        # Confidence as 0.0–1.0 decimal (NOT percentage)
         "confidence":
             round(
                 float(
                     probs[pred_idx]
-                ) * 100,
-                2
+                ),
+                4
             ),
 
         "probabilities": {
@@ -386,8 +400,8 @@ def predict_model(model, tensor):
                 round(
                     float(
                         probs[i]
-                    ) * 100,
-                    2
+                    ),
+                    4
                 )
 
             for i in range(NUM_CLASSES)
@@ -396,6 +410,25 @@ def predict_model(model, tensor):
 
     return result, probs
 
+# =========================================
+# HEALTH ENDPOINT
+# =========================================
+@app.get("/health")
+def health():
+
+    return {
+        "status": "running",
+        "service": "Parkinson DaTscan Ensemble API",
+        "models_loaded": {
+            "densenet121": True,
+            "densenet169": True,
+            "densenet201": True,
+            "cnn3d": True,
+        },
+        "device": str(DEVICE),
+        "classes": LABELS,
+    }
+
 # =========================================
 # ROOT
 # =========================================
@@ -408,7 +441,16 @@ def home():
             "Parkinson DATSCAN Ensemble API Running",
 
         "classes":
-            LABELS
+            LABELS,
+
+        "endpoints": [
+            "/health",
+            "/predict/densenet121",
+            "/predict/densenet169",
+            "/predict/densenet201",
+            "/predict/cnn3d",
+            "/predict/ensemble",
+        ]
     }
 
 # =========================================
@@ -505,6 +547,7 @@ async def predict_cnn3d(
 
 # =========================================
 # ENSEMBLE ENDPOINT
+# Returns ensemble confidence as 0.0-1.0 with individual model breakdown
 # =========================================
 @app.post("/predict/ensemble")
 async def predict_ensemble(
@@ -552,27 +595,29 @@ async def predict_ensemble(
         avg_probs
     ).item()
 
+    # All probability values as 0.0–1.0
     final_result = {
 
-        "ensemble_prediction":
+        "prediction":
             LABELS[pred_idx],
 
-        "ensemble_confidence":
+        # Ensemble confidence as 0.0–1.0 (NOT percentage)
+        "confidence":
             round(
                 float(
                     avg_probs[pred_idx]
-                ) * 100,
-                2
+                ),
+                4
             ),
 
-        "ensemble_probabilities": {
+        "probabilities": {
 
             LABELS[i]:
                 round(
                     float(
                         avg_probs[i]
-                    ) * 100,
-                    2
+                    ),
+                    4
                 )
 
             for i in range(NUM_CLASSES)