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"""
Tests for FastAPI skill classification endpoints.
Tests cover request validation, response structure, error handling,
and batch processing capabilities.
"""
from http import HTTPStatus
import pytest
from fastapi.testclient import TestClient
from hopcroft_skill_classification_tool_competition.main import app
_client = None
def get_client():
"""Get or create TestClient with lifespan executed."""
global _client
if _client is None:
_client = TestClient(app)
_client.__enter__() # Force lifespan startup
return _client
client = get_client()
class TestRootEndpoint:
"""Tests for the root endpoint."""
def test_read_root(self):
"""Test root endpoint returns basic API information."""
response = client.get("/")
assert response.status_code == HTTPStatus.OK
assert response.request.method == "GET"
data = response.json()
assert "message" in data
assert "version" in data
assert data["message"] == "Skill Classification API"
assert data["version"] == "1.0.0"
class TestHealthEndpoint:
"""Tests for the health check endpoint."""
def test_health_check(self):
"""Test health endpoint returns service status."""
response = client.get("/health")
assert response.status_code == HTTPStatus.OK
assert response.request.method == "GET"
data = response.json()
assert "status" in data
assert "model_loaded" in data
assert "version" in data
assert data["status"] == "healthy"
assert isinstance(data["model_loaded"], bool)
class TestPredictionEndpoint:
"""Tests for the single prediction endpoint."""
def test_predict_with_minimal_data(self):
"""Test prediction with only required fields."""
issue_data = {
"issue_text": "Fix authentication bug in login module"
}
response = client.post("/predict", json=issue_data)
assert response.status_code == HTTPStatus.CREATED
assert response.request.method == "POST"
data = response.json()
assert "predictions" in data
assert "num_predictions" in data
assert "model_version" in data
assert "processing_time_ms" in data
# Verify predictions structure
assert data["num_predictions"] == len(data["predictions"])
# Check each prediction has required fields
for pred in data["predictions"]:
assert "skill_name" in pred
assert "confidence" in pred
assert 0.0 <= pred["confidence"] <= 1.0
def test_predict_with_full_data(self):
"""Test prediction with all optional fields."""
issue_data = {
"issue_text": "Add support for OAuth authentication",
"issue_description": "Implement OAuth 2.0 flow for third-party authentication providers",
"repo_name": "myorg/myproject",
"pr_number": 456,
"author_name": "developer123",
"created_at": "2024-01-15T10:30:00Z"
}
response = client.post("/predict", json=issue_data)
assert response.status_code == HTTPStatus.CREATED
data = response.json()
assert len(data["predictions"]) > 0
assert data["model_version"] == "1.0.0"
assert data["processing_time_ms"] > 0
def test_predict_missing_required_field(self):
"""Test prediction fails when required field is missing."""
issue_data = {
"issue_description": "This is missing the issue_text field"
}
response = client.post("/predict", json=issue_data)
# Should return validation error (422)
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_predict_invalid_pr_number(self):
"""Test prediction fails with invalid PR number."""
issue_data = {
"issue_text": "Fix bug",
"pr_number": -5
}
response = client.post("/predict", json=issue_data)
# Should return validation error
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_predict_empty_issue_text(self):
"""Test prediction with empty issue text."""
issue_data = {
"issue_text": ""
}
response = client.post("/predict", json=issue_data)
# Should return validation error (min_length=1)
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_predict_whitespace_only_text(self):
"""Test prediction with whitespace-only text."""
issue_data = {
"issue_text": " " # Only whitespace
}
response = client.post("/predict", json=issue_data)
# Should be cleaned by validator
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
class TestBatchPredictionEndpoint:
"""Tests for the batch prediction endpoint."""
def test_batch_predict_multiple_issues(self):
"""Test batch prediction with multiple issues."""
batch_data = {
"issues": [
{
"issue_text": "Transfer learning with transformers for text classification."
},
{
"issue_text": "Generative adversarial networks in both PyTorch and TensorFlow."
},
{
"issue_text": "Fix database connection pooling issue"
}
]
}
response = client.post("/predict/batch", json=batch_data)
assert response.status_code == HTTPStatus.OK
assert response.request.method == "POST"
data = response.json()
assert "results" in data
assert "total_issues" in data
assert "total_processing_time_ms" in data
# Verify correct number of results
assert len(data["results"]) == len(batch_data["issues"])
assert data["total_issues"] == 3
# Verify each result has predictions
for result in data["results"]:
assert "predictions" in result
assert "num_predictions" in result
assert len(result["predictions"]) > 0
def test_batch_predict_single_issue(self):
"""Test batch prediction with single issue."""
batch_data = {
"issues": [
{
"issue_text": "Add unit tests for authentication module"
}
]
}
response = client.post("/predict/batch", json=batch_data)
assert response.status_code == HTTPStatus.OK
data = response.json()
assert data["total_issues"] == 1
assert len(data["results"]) == 1
def test_batch_predict_empty_list(self):
"""Test batch prediction with empty issues list."""
batch_data = {
"issues": []
}
response = client.post("/predict/batch", json=batch_data)
# Should return validation error (min_length=1)
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_batch_predict_too_many_issues(self):
"""Test batch prediction exceeds maximum limit."""
batch_data = {
"issues": [
{"issue_text": f"Issue {i}"}
for i in range(101)
]
}
response = client.post("/predict/batch", json=batch_data)
# Should return validation error
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_batch_predict_with_mixed_data(self):
"""Test batch prediction with mix of minimal and full data."""
batch_data = {
"issues": [
{
"issue_text": "Simple issue"
},
{
"issue_text": "Detailed issue",
"issue_description": "With description and metadata",
"repo_name": "user/repo",
"pr_number": 123
}
]
}
response = client.post("/predict/batch", json=batch_data)
assert response.status_code == HTTPStatus.OK
data = response.json()
assert len(data["results"]) == 2
class TestErrorHandling:
"""Tests for error handling and responses."""
def test_missing_required_field(self):
"""Test validation error for missing required field."""
response = client.post("/predict", json={})
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_endpoint_not_found(self):
"""Test non-existent endpoint returns 404."""
response = client.get("/nonexistent")
assert response.status_code == HTTPStatus.NOT_FOUND
class TestGetPredictionEndpoint:
"""Tests for retrieving individual predictions by run_id."""
def test_get_prediction_success(self):
"""Test retrieving an existing prediction."""
issue_data = {"issue_text": "Test issue for retrieval"}
create_response = client.post("/predict", json=issue_data)
assert create_response.status_code == HTTPStatus.CREATED
run_id = create_response.json()["run_id"]
response = client.get(f"/predictions/{run_id}")
assert response.status_code == HTTPStatus.OK
data = response.json()
assert data["run_id"] == run_id
assert "predictions" in data
assert "timestamp" in data
def test_get_prediction_not_found(self):
"""Test retrieving a non-existent prediction returns 404."""
fake_run_id = "nonexistent_run_id_12345"
response = client.get(f"/predictions/{fake_run_id}")
assert response.status_code == HTTPStatus.NOT_FOUND
class TestListPredictionsEndpoint:
"""Tests for listing recent predictions."""
def test_list_predictions(self):
"""Test listing predictions works."""
response = client.get("/predictions")
assert response.status_code == HTTPStatus.OK
data = response.json()
assert isinstance(data, list)
def test_list_predictions_with_pagination(self):
"""Test listing predictions with pagination parameters."""
response = client.get("/predictions?skip=0&limit=5")
assert response.status_code == HTTPStatus.OK
data = response.json()
assert isinstance(data, list)
assert len(data) <= 5
class TestMLflowIntegration:
"""Tests for MLflow tracking integration."""
def test_prediction_creates_run_id(self):
"""Test that predictions create MLflow run_id."""
issue_data = {"issue_text": "MLflow tracking test"}
response = client.post("/predict", json=issue_data)
assert response.status_code == HTTPStatus.CREATED
data = response.json()
assert "run_id" in data
assert data["run_id"]
def test_retrieve_prediction_by_run_id(self):
"""Test retrieving prediction using run_id."""
response = client.post("/predict", json={"issue_text": "Test retrieval"})
run_id = response.json()["run_id"]
retrieve_response = client.get(f"/predictions/{run_id}")
assert retrieve_response.status_code == HTTPStatus.OK
assert retrieve_response.json()["run_id"] == run_id
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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