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backend/tests/test_database.py

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+import pytest
+import pytest_asyncio
+import os
+import sys
+import aiosqlite
+from collections import namedtuple
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+import database
+
+# Mock settings to avoid real huggingface calls
+database.settings.hf_token = None
+database.DB_PATH = "test_patient_history.db"
+
+@pytest_asyncio.fixture(autouse=True)
+async def setup_teardown():
+    if os.path.exists(database.DB_PATH):
+        os.remove(database.DB_PATH)
+    await database.init_db()
+    yield
+    if os.path.exists(database.DB_PATH):
+        try:
+            os.remove(database.DB_PATH)
+        except PermissionError:
+            pass # Windows file lock fallback
+
+@pytest.mark.asyncio
+async def test_init_db():
+    assert os.path.exists(database.DB_PATH)
+    async with aiosqlite.connect(database.DB_PATH) as db:
+        cursor = await db.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='predictions'")
+        table = await cursor.fetchone()
+        assert table is not None
+
+@pytest.mark.asyncio
+async def test_log_and_get_history():
+    Data = namedtuple('Data', ['Heart_Rate', 'SpO2_Level', 'Systolic_BP', 'Diastolic_BP', 'Body_Temp'])
+    data = Data(80.0, 98.0, 120.0, 80.0, 37.0)
+    
+    await database.log_prediction(data, "Normal", 0.95)
+    
+    history = await database.get_history()
+    assert len(history) == 1
+    assert history[0]['heart_rate'] == 80.0
+    assert history[0]['prediction_label'] == "Normal"
+    assert history[0]['confidence'] == 0.95
+
+@pytest.mark.asyncio
+async def test_empty_history():
+    if os.path.exists(database.DB_PATH):
+        os.remove(database.DB_PATH)
+    history = await database.get_history()
+    assert history == []

backend/tests/test_ensemble.py

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+import pytest
+import numpy as np
+import os
+import sys
+from unittest.mock import patch, MagicMock
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from ensemble import EnsembleModel
+
+@patch('joblib.load')
+@patch('os.path.exists')
+def test_ensemble_initialization(mock_exists, mock_load):
+    mock_exists.return_value = True
+    
+    mock_model = MagicMock()
+    mock_load.return_value = mock_model
+    
+    ensemble = EnsembleModel()
+    
+    assert len(ensemble.models) == len(ensemble.model_names)
+    assert 'rf' in ensemble.models
+    assert 'logreg' in ensemble.models
+
+@patch('joblib.load')
+@patch('os.path.exists')
+def test_ensemble_prediction(mock_exists, mock_load):
+    mock_exists.return_value = True
+    
+    mock_model = MagicMock()
+    mock_model.predict_proba.return_value = np.array([[0.2, 0.8]])
+    
+    mock_label_encoder = MagicMock()
+    mock_label_encoder.inverse_transform.side_effect = lambda x: [f"Class_{i}" for i in x]
+    
+    def side_effect(path):
+        if 'label_encoder' in path:
+            return mock_label_encoder
+        return mock_model
+        
+    mock_load.side_effect = side_effect
+    
+    ensemble = EnsembleModel()
+    X_dummy = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 1.0]])
+    final_pred, confidence, individual_preds, class_probs, weights, individual_conf = ensemble.predict(X_dummy)
+    
+    assert confidence == 0.8
+    assert final_pred == "Class_1"
+    assert "Class_0" in class_probs
+    assert class_probs["Class_1"] == 0.8

backend/tests/test_preprocessing.py

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+import pytest
+import pandas as pd
+import numpy as np
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from preprocessing import preprocess_data, resolve_model_dir
+
+def test_resolve_model_dir():
+    md = resolve_model_dir()
+    assert md.endswith("model")
+
+def test_preprocess_data_training():
+    df = pd.DataFrame({
+        'Heart Rate (bpm)': [80, 90, np.nan],
+        'SpO2 Level (%)': [98, 97, 95],
+        'Systolic Blood Pressure (mmHg)': [120, 125, 130],
+        'Diastolic Blood Pressure (mmHg)': [80, 85, 90],
+        'Body Temperature (°C)': [37.0, 37.2, 37.5],
+        'Predicted Disease': ['Normal', 'Asthma', 'Normal']
+    })
+    
+    X, y = preprocess_data(df, is_training=True)
+    
+    assert not X.isnull().values.any()
+    assert len(y) == 3
+    assert 'Risk_Severity' in X.columns
+
+def test_preprocess_data_inference():
+    df = pd.DataFrame({
+        'Heart Rate (bpm)': [100],
+        'SpO2 Level (%)': [99],
+        'Systolic Blood Pressure (mmHg)': [120],
+        'Diastolic Blood Pressure (mmHg)': [80],
+        'Body Temperature (°C)': [37.0],
+    })
+    
+    X, y = preprocess_data(df, is_training=False)
+    
+    assert y is None
+    assert 'Risk_Severity' in X.columns

backend/tests/test_simulator.py

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+import pytest
+import asyncio
+import sys
+import os
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from simulator import PatientDataSimulator
+
+@pytest.mark.asyncio
+async def test_simulator_initialization():
+    sim = PatientDataSimulator(initial_hr=80.0)
+    assert sim.hr == 80.0
+    assert sim.spo2 == 98.0
+    assert sim.sys_bp == 120.0
+    assert sim.dia_bp == 80.0
+    assert sim.temp == 37.0
+
+@pytest.mark.asyncio
+async def test_simulator_shift_state():
+    sim = PatientDataSimulator()
+    import random
+    random.seed(42)
+    sim.target_hr = 150.0
+    sim._step_physics()
+    assert sim.hr > 75.0 
+
+@pytest.mark.asyncio
+async def test_simulator_run_yields_dict():
+    sim = PatientDataSimulator()
+    generator = sim.run()
+    data = await anext(generator)
+    assert isinstance(data, dict)
+    assert "Heart_Rate" in data
+    assert "SpO2_Level" in data
+    assert "Systolic_BP" in data
+    assert "Diastolic_BP" in data
+    assert "Body_Temp" in data