creted pytests

Author: Shakiran-Nannyombi

.github/workflows/ci.yml

@@ -0,0 +1,49 @@
+name: CI
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  test:
+    name: Run backend tests
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.11]
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Cache pip
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('**/backend/requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r backend/requirements.txt
+
+      - name: Run pytest
+        run: |
+          mkdir -p reports
+          pytest backend/tests -q --junitxml=reports/junit.xml
+
+      - name: Upload test report
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: pytest-report
+          path: reports/junit.xml

backend/docker/Dockerfile

@@ -1,6 +1,5 @@
 # Multi-stage build for MzeeChakula Backend API
 
-
 # Builder stage
 FROM python:3.12-slim AS builder
 

backend/tests/test_loader_and_api.py

@@ -0,0 +1,122 @@
+import os
+import pickle
+import json
+import numpy as np
+import tempfile
+from pathlib import Path
+
+import pytest
+from fastapi.testclient import TestClient
+
+
+def make_dummy_model(pred_value=123.45, feature_names=None):
+    class DummyModel:
+        def __init__(self, v, cols=None):
+            self._v = v
+            if cols is not None:
+                # mimic scikit-learn attribute
+                self.feature_names_in_ = cols
+
+        def predict(self, X):
+            # return array-like
+            return [self._v] * len(X)
+
+    return DummyModel(pred_value, feature_names)
+
+
+@pytest.fixture
+def tmp_models_dir(tmp_path, monkeypatch):
+    d = tmp_path / "models"
+    d.mkdir()
+
+    # Create feature names pickle
+    feature_names = [
+        "Energy_kcal_per_serving",
+        "Protein_g_per_serving",
+        "Fat_g_per_serving",
+        "Carbohydrates_g_per_serving",
+        "Fiber_g_per_serving",
+        "Calcium_mg_per_serving",
+        "Iron_mg_per_serving",
+        "Zinc_mg_per_serving",
+        "VitaminA_ug_per_serving",
+        "VitaminC_mg_per_serving",
+        "Potassium_mg_per_serving",
+        "Magnesium_mg_per_serving",
+        "region_encoded",
+        "condition_encoded",
+        "age_group_encoded",
+        "season_encoded",
+        "portion_size_g",
+        "estimated_cost_ugx",
+    ]
+    with open(d / "xgboost_feature_names_20251103.pkl", "wb") as f:
+        pickle.dump(feature_names, f)
+
+    # Create a dummy xgboost pickle model
+    dummy = make_dummy_model(pred_value=250.0, feature_names=feature_names)
+    with open(d / "xgboost_nutrition_model_20251103.pkl", "wb") as f:
+        pickle.dump(dummy, f)
+
+    return d
+
+
+def test_local_loader_and_predict(tmp_models_dir, monkeypatch):
+    # Disable HF snapshot during this test to avoid network calls
+    import backend.api.models.loader as loader_mod
+    monkeypatch.setattr(loader_mod, "HF_AVAILABLE", False)
+
+    # Instantiate ModelLoader pointing to our tmp models dir
+    loader = loader_mod.ModelLoader(local_model_dir=tmp_models_dir)
+
+    assert loader.models.get('local_xgboost', {}).get('available') is True
+
+    # Prepare input matching feature names
+    input_dict = {k: 1.0 for k in loader.feature_names}
+    res = loader.predict(input_dict, model_preference='auto')
+    assert res['success'] is True
+    assert 'prediction' in res
+    assert float(res['prediction']['caloric_needs']) == pytest.approx(250.0)
+
+
+def test_predict_endpoint_and_recommend(tmp_models_dir, monkeypatch):
+    # Disable HF snapshot during this test
+    import backend.api.models.loader as loader_mod
+    monkeypatch.setattr(loader_mod, "HF_AVAILABLE", False)
+
+    # Create loader and attach an ensemble manually
+    loader = loader_mod.ModelLoader(local_model_dir=tmp_models_dir)
+
+    # create a tiny ensemble for recommend testing
+    emb = np.array([[1.0, 0.0], [0.0, 1.0]], dtype=float)
+    norms = np.linalg.norm(emb, axis=1, keepdims=True)
+    emb_norm = emb / norms
+    loader.models['ensemble'] = {
+        'embeddings': emb_norm,
+        'ids': ['food_a', 'food_b'],
+        'metadata': {'food_a': {'name': 'A'}, 'food_b': {'name': 'B'}},
+        'available': True
+    }
+
+    # Now wire this loader into the running app routers
+    from backend.api.main import app
+    from backend.api.routers import predict as predict_router
+
+    predict_router.set_model_loader(loader)
+
+    client = TestClient(app)
+
+    # Call predict endpoint
+    payload = {k: 1.0 for k in loader.feature_names}
+    r = client.post("/predict/", json=payload)
+    assert r.status_code == 200
+    body = r.json()
+    assert body['success'] is True
+    assert float(body['prediction']['caloric_needs']) == pytest.approx(250.0)
+
+    # Call recommend by id
+    r2 = client.get('/predict/recommend', params={'by_id': 'food_a', 'top_k': 2})
+    assert r2.status_code == 200
+    b2 = r2.json()
+    assert b2['success'] is True
+    assert len(b2['items']) == 2