import sys
import os
import pytest
from unittest.mock import patch

# Ensure src is in path
sys.path.append(os.getcwd())

from src.infer import predict_pass_prob, explain_prediction, load_model

# We need a fixture to create a valid model file for inference
@pytest.fixture(scope="module")
def train_dummy_model(tmp_path_factory):
    """Trains a quick dummy model and saves it to a temp dir."""
    models_dir = tmp_path_factory.mktemp("models")
    model_path = models_dir / "model.pkl"
    
    # We reuse the logic from src.train but point to our temp path
    # OR we can just manually create a pipeline and save it
    # Reusing src.train is better integration testing
    from src.train import get_pipeline, generate_data, preprocess_data
    import joblib
    
    df = generate_data(n_samples=20)
    df = preprocess_data(df)
    
    X = df.drop(columns=["is_pass"])
    y = df["is_pass"]
    
    pipeline = get_pipeline("rf")
    pipeline.fit(X, y)
    
    joblib.dump(pipeline, model_path)
    
    return str(model_path)

@patch("src.infer._MODEL", None) # Reset global cached model
def test_predict_pass_prob(train_dummy_model):
    """Test prediction using the dummy trained model."""
    
    with patch("src.infer.MODEL_PATH", train_dummy_model):
        proba = predict_pass_prob(
            study_hours=5.0,
            sleep_hours=7.0,
            attendance_rate=0.9,
            stress_level=3,
            study_type="Self"
        )
        assert 0.0 <= proba <= 1.0

@patch("src.infer._MODEL", None) # Reset global cached model
def test_explain_prediction(train_dummy_model):
    """Test explanation generation."""
    
    with patch("src.infer.MODEL_PATH", train_dummy_model):
        explanation = explain_prediction()
        assert isinstance(explanation, str)
        assert "模型特征重要性排名" in explanation

@patch("src.infer._MODEL", None)
def test_load_model_missing():
    """Test error handling when model is missing."""
    with patch("src.infer.MODEL_PATH", "non_existent_path/model.pkl"):
        # Should raise FileNotFoundError or be handled
        with pytest.raises(FileNotFoundError):
             predict_pass_prob(1,1,1,1,"Self") # This calls load_model internally