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backend/main.py

@@ -143,7 +143,7 @@ def force_sync():
 
 @app.post("/explain")
 async def explain(data: PatientData):
-    eng = ml_engine.get_ensemble()
+    eng = ml_engine.get_ensemble() if ml_engine else None
     if not eng or 'rf' not in eng.models:
         return {"error": "RF Model unavailable for explanation."}
         
@@ -172,7 +172,9 @@ async def explain(data: PatientData):
 
 @app.get("/export_tinyml")
 def export_tinyml(model_name: str = "rf", quantize: bool = False):
-    eng = ml_engine.get_ensemble()
+    eng = ml_engine.get_ensemble() if ml_engine else None
+    if not eng:
+        return {"error": "Models untrained or engine offline"}
     return generate_c_code(eng, model_name, quantize)
 
 def _run_mlops_ingest(csv_bytes: bytes):

tests/test_api.py

@@ -6,24 +6,27 @@ import os
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../backend')))
 from main import app
 
-client = TestClient(app)
+@pytest.fixture
+def client():
+    with TestClient(app) as c:
+        yield c
 
-def test_history_endpoint():
+def test_history_endpoint(client):
     response = client.get("/history")
     assert response.status_code == 200
     assert isinstance(response.json(), list)
 
-def test_dataset_endpoint():
+def test_dataset_endpoint(client):
     response = client.get("/dataset")
     assert response.status_code == 200
     assert isinstance(response.json(), list)
 
-def test_export_tinyml_random_forest():
+def test_export_tinyml_random_forest(client):
     response = client.get("/export_tinyml?model_name=rf")
     
     if response.status_code == 200:
         data = response.json()
         if "error" in data:
-            assert "not found" in data["error"].lower()
+            assert "not found" in data["error"].lower() or "models untrained" in data["error"].lower()
         else:
             assert "code" in data or "c_code" in data