Empty appointments bug fix

CHANGELOG.md

@@ -6,6 +6,15 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## [Unreleased]
+## [1.5.1] - 2024-12-23
+
+### Added
+- Error warmings incase no appointments are provided
+- Added ssl-certificate fix in deploy script
+- Added expetion tests
+
+### Changed
+- Saving of predictions is now located in helper function
 
 ## [1.5.0] - 2024-12-10
 

deploy.sh

@@ -6,6 +6,9 @@ do
     fi
 done
 
+# necessary for certifi to find the right certificate if run without conda forge's ca-certificates
+export SSL_CERT_FILE=/etc/ssl/certs/ca-bundle.crt
+
 read -p "What do you want to deploy? Options: 'admin-dash'/1 ; 'calling-dash'/2 ; 'calling-dash-test'/3 ; 'api'/4 ; 'api-test'/5 " APPLICATION
 APPLICATION=${APPLICATION:-N}
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "noshow"
-version = "1.5.0"
+version = "1.5.1"
 authors = [
   { name="Ruben Peters", email="[email protected]" },
   { name="Eric Wolters", email="[email protected]" }

src/noshow/api/app.py

@@ -17,14 +17,12 @@
     fix_outdated_appointments,
     load_model,
     remove_sensitive_info,
+    store_predictions,
 )
 from noshow.api.pydantic_models import Appointment
 from noshow.config import CLINIC_CONFIG, KEEP_SENSITIVE_DATA
 from noshow.database.models import (
-    ApiPatient,
-    ApiPrediction,
     ApiRequest,
-    ApiSensitiveInfo,
     Base,
 )
 from noshow.model.predict import create_prediction
@@ -119,10 +117,18 @@ async def predict(
     project_path = Path(__file__).parents[3]
     start_time = datetime.now()
 
+    if len(input) == 0:
+        raise HTTPException(status_code=400, detail="Input cannot be empty.")
+
     input_df = load_appointment_pydantic(input)
     appointments_df = process_appointments(input_df, CLINIC_CONFIG, start_date)
     all_postalcodes = process_postal_codes(project_path / "data" / "raw" / "NL.txt")
 
+    if appointments_df.empty:
+        raise HTTPException(
+            status_code=400, detail="No appointments for the start date and filters"
+        )
+
     model = load_model()
     prediction_df = create_prediction(
         model,
@@ -160,71 +166,7 @@ async def predict(
     )
     db.add(apirequest)
 
-    for _, row in prediction_df.iterrows():
-        apisensitive = db.get(ApiSensitiveInfo, row["pseudo_id"])
-
-        if not apisensitive:
-            if row["name_text"] is None:
-                row["name_text"] = ""
-                logger.warning(
-                    f"Patient {row['pseudo_id']} has no name_text, "
-                    "replacing with empty string"
-                )
-
-            apisensitive = ApiSensitiveInfo(
-                patient_id=row["pseudo_id"],
-                hix_number=row["patient_id"],
-                full_name=row["name_text"],
-                first_name=row["name_given1_callMe"],
-                birth_date=row["birthDate"],
-                mobile_phone=row["telecom1_value"],
-                home_phone=row["telecom2_value"],
-                other_phone=row["telecom3_value"],
-            )
-        else:
-            # name and birthdate can't change, but phone number might
-            apisensitive.mobile_phone = row["telecom1_value"]
-            apisensitive.home_phone = row["telecom2_value"]
-            apisensitive.other_phone = row["telecom3_value"]
-
-        apipatient = db.get(ApiPatient, row["pseudo_id"])
-        if not apipatient:
-            apipatient = ApiPatient(
-                id=row["pseudo_id"],
-            )
-        apipatient.treatment_group = int(row["treatment_group"])
-        apiprediction = db.get(ApiPrediction, row["APP_ID"])
-        if not apiprediction:
-            apiprediction = ApiPrediction(
-                id=row["APP_ID"],
-                patient_id=row["pseudo_id"],
-                prediction=row["prediction"],
-                start_time=row["start"],
-                request_relation=apirequest,
-                patient_relation=apipatient,
-                clinic_name=row["hoofdagenda"],
-                clinic_reception=row["description"],
-                clinic_phone_number=CLINIC_CONFIG[row["clinic"]].phone_number,
-                clinic_teleq_unit=CLINIC_CONFIG[row["clinic"]].teleq_name,
-                active=True,
-            )
-        else:
-            # All values of a prediction can be updated except the ID and treatment
-            apiprediction.prediction = row["prediction"]
-            apiprediction.start_time = row["start"]
-            apiprediction.request_relation = apirequest
-            apiprediction.clinic_name = row["hoofdagenda"]
-            apiprediction.clinic_reception = row["description"]
-            apiprediction.clinic_phone_number = CLINIC_CONFIG[
-                row["clinic"]
-            ].phone_number
-            apiprediction.clinic_teleq_unit = CLINIC_CONFIG[row["clinic"]].teleq_name
-            apiprediction.active = True
-
-        db.merge(apisensitive)
-        db.merge(apiprediction)
-        db.merge(apipatient)
-        db.commit()
+    store_predictions(prediction_df, db, logger, apirequest, CLINIC_CONFIG)
 
     fix_outdated_appointments(db, prediction_df["APP_ID"], start_date)
 

src/noshow/api/app_helpers.py

@@ -186,3 +186,93 @@ def create_treatment_groups(
     predictions = predictions.drop(columns="score_bin")
     predictions["treatment_group"] = predictions["treatment_group"].astype(int)
     return predictions
+
+
+def store_predictions(
+    prediction_df: pd.DataFrame,
+    db: Session,
+    logger: Any,
+    apirequest: Any,
+    CLINIC_CONFIG: Dict[str, Any],
+) -> None:
+    """
+    Store predictions in the database.
+
+    Parameters
+    ----------
+    prediction_df : pd.DataFrame
+        DataFrame containing the predictions.
+    db : Session
+        Database session.
+    logger : Any
+        Logger for logging warnings and information.
+    apirequest : Any
+        API request object related to the predictions.
+    CLINIC_CONFIG : Dict[str, Any]
+        Configuration dictionary for clinic details.
+    """
+    for _, row in prediction_df.iterrows():
+        apisensitive = db.get(ApiSensitiveInfo, row["pseudo_id"])
+
+        if not apisensitive:
+            if row["name_text"] is None:
+                row["name_text"] = ""
+                logger.warning(
+                    f"Patient {row['pseudo_id']} has no name_text, "
+                    "replacing with empty string"
+                )
+
+            apisensitive = ApiSensitiveInfo(
+                patient_id=row["pseudo_id"],
+                hix_number=row["patient_id"],
+                full_name=row["name_text"],
+                first_name=row["name_given1_callMe"],
+                birth_date=row["birthDate"],
+                mobile_phone=row["telecom1_value"],
+                home_phone=row["telecom2_value"],
+                other_phone=row["telecom3_value"],
+            )
+        else:
+            # name and birthdate can't change, but phone number might
+            apisensitive.mobile_phone = row["telecom1_value"]
+            apisensitive.home_phone = row["telecom2_value"]
+            apisensitive.other_phone = row["telecom3_value"]
+
+        apipatient = db.get(ApiPatient, row["pseudo_id"])
+        if not apipatient:
+            apipatient = ApiPatient(
+                id=row["pseudo_id"],
+            )
+        apipatient.treatment_group = int(row["treatment_group"])
+        apiprediction = db.get(ApiPrediction, row["APP_ID"])
+        if not apiprediction:
+            apiprediction = ApiPrediction(
+                id=row["APP_ID"],
+                patient_id=row["pseudo_id"],
+                prediction=row["prediction"],
+                start_time=row["start"],
+                request_relation=apirequest,
+                patient_relation=apipatient,
+                clinic_name=row["hoofdagenda"],
+                clinic_reception=row["description"],
+                clinic_phone_number=CLINIC_CONFIG[row["clinic"]].phone_number,
+                clinic_teleq_unit=CLINIC_CONFIG[row["clinic"]].teleq_name,
+                active=True,
+            )
+        else:
+            # All values of a prediction can be updated except the ID and treatment
+            apiprediction.prediction = row["prediction"]
+            apiprediction.start_time = row["start"]
+            apiprediction.request_relation = apirequest
+            apiprediction.clinic_name = row["hoofdagenda"]
+            apiprediction.clinic_reception = row["description"]
+            apiprediction.clinic_phone_number = CLINIC_CONFIG[
+                row["clinic"]
+            ].phone_number
+            apiprediction.clinic_teleq_unit = CLINIC_CONFIG[row["clinic"]].teleq_name
+            apiprediction.active = True
+
+        db.merge(apisensitive)
+        db.merge(apiprediction)
+        db.merge(apipatient)
+        db.commit()

tests/test_api.py

@@ -66,3 +66,32 @@ async def test_predict_endpoint(monkeypatch):
     output = await predict(appointments_pydantic, "2024-07-16", FakeDB(), "test")
     output_df = pd.DataFrame(output)
     assert output_df.shape == (5, 17)
+
+
+# teste empty appointments
+@pytest.mark.asyncio
+async def test_predict_endpoint_empty_appointments(monkeypatch):
+    appointments_pydantic = fake_appointments()
+    monkeypatch.setattr(app, "get_bins", fake_bins)
+    monkeypatch.setattr(app, "process_postal_codes", fake_postal_codes)
+    monkeypatch.setattr(app, "load_model", fake_model)
+    monkeypatch.setattr(app_helpers, "delete", lambda x: FakeWhere())
+    # patch create treatment groups and add column to the dataframe
+    monkeypatch.setattr(
+        app, "create_treatment_groups", lambda x, y, z, q: x.assign(treatment_group=1)
+    )
+    monkeypatch.setattr(app, "CLINIC_CONFIG", create_unit_test_clinic_config())
+    monkeypatch.setenv("DB_USER", "")
+    monkeypatch.setenv("X_API_KEY", "test")
+
+    # empty appointments
+    with pytest.raises(Exception) as exc_info_empty:
+        __ = await predict([], "2024-07-16", FakeDB(), "test")
+    assert "400: Input cannot be empty." in str(exc_info_empty.value)
+
+    # no appointments for the start date
+    with pytest.raises(Exception) as exc_inf_wrong_date:
+        __ = await predict(appointments_pydantic, "2024-07-15", FakeDB(), "test")
+    assert "400: No appointments for the start date and filters" in str(
+        exc_inf_wrong_date.value
+    )