tests/test_nodes.py

"""
test_nodes.py — Unit tests for all DSA Mentor agent node functions.

Uses MagicMock to patch the LLM and avoid real API calls.
Run with: pytest tests/ -v
"""

import pytest
from unittest.mock import MagicMock, patch


# ── Helpers ───────────────────────────────────────────────────────────────────

def _base_state(**overrides) -> dict:
    """Returns a minimal valid AgentState for testing."""
    state = {
        "problem":       "Given an array of integers, find two numbers that add to a target.",
        "user_thought":  "I'll use a nested loop to check every pair.",
        "code":          "",
        "strictness":    "Moderate",
        "request_mode":  "analyze",
        "session_id":    "test-session-001",
        "problem_topic": "Two Sum",
        "identified_gap": "Not using a hashmap",
        "gap_magnitude": 6,
        "current_hint_level": 1,
        "turn_count":    0,
        "messages":      [],
        "final_response": None,
        "test_pass_rate": None,
        "mistake":       None,
        "why_wrong":     None,
        "correct_thinking": None,
    }
    state.update(overrides)
    return state


# ── Classify Node ─────────────────────────────────────────────────────────────

def test_classify_problem_returns_topic():
    """classify_problem should update problem_topic from LLM response."""
    mock_response = MagicMock()
    mock_response.content = "Two Sum"

    with patch("agent.nodes.classify_node._llm") as mock_llm:
        mock_chain = MagicMock()
        mock_chain.invoke.return_value = mock_response
        mock_llm.__or__ = MagicMock(return_value=mock_chain)

        from agent.nodes.classify_node import classify_problem
        result = classify_problem(_base_state())

    assert "problem_topic" in result
    assert isinstance(result["problem_topic"], str)


# ── Gate Node ─────────────────────────────────────────────────────────────────

def test_gate_allows_solution_when_gap_critical():
    """gate_solution should allow solution when gap_magnitude > 7."""
    from agent.nodes.gate_node import gate_solution
    state = _base_state(request_mode="solution", gap_magnitude=9, turn_count=0)
    result = gate_solution(state)
    assert result.get("request_mode") == "solution"


def test_gate_blocks_solution_too_early():
    """gate_solution should block solution when turn_count < 2 and gap <= 7."""
    from agent.nodes.gate_node import gate_solution
    state = _base_state(request_mode="solution", gap_magnitude=5, turn_count=0)
    result = gate_solution(state)
    assert result.get("request_mode") == "hint_forced"


def test_gate_passthrough_non_solution_mode():
    """gate_solution should return empty dict for non-solution modes."""
    from agent.nodes.gate_node import gate_solution
    state = _base_state(request_mode="analyze")
    result = gate_solution(state)
    assert result == {}


# ── Validate Node ─────────────────────────────────────────────────────────────

def test_validate_solution_returns_100():
    """validate_solution should always return score=100."""
    with patch("agent.nodes.validate_node.load_profile") as mock_load, \
         patch("agent.nodes.validate_node.persist_profile"):
        mock_load.return_value = MagicMock(weak_topics={}, solved_problems=0, total_turns=0, avg_gap=0.0)
        from agent.nodes.validate_node import validate_solution
        result = validate_solution(_base_state())

    assert result["final_response"]["score"] == 100
    assert result["final_response"]["type"] == "Validation"


def test_validate_solution_contains_hint_text():
    """validate_solution final_response should have a 'hint' key."""
    with patch("agent.nodes.validate_node.load_profile") as mock_load, \
         patch("agent.nodes.validate_node.persist_profile"):
        mock_load.return_value = MagicMock(weak_topics={}, solved_problems=0, total_turns=0, avg_gap=0.0)
        from agent.nodes.validate_node import validate_solution
        result = validate_solution(_base_state())

    assert "hint" in result["final_response"]


# ── Hint Node ─────────────────────────────────────────────────────────────────

def test_generate_hint_increments_hint_level():
    """generate_hint should increment current_hint_level by 1."""
    mock_hint = MagicMock()
    mock_hint.hint = "Think about what data structure gives O(1) lookup."
    mock_hint.type = "Data Structure"

    with patch("agent.nodes.hint_node._structured_llm") as mock_llm, \
         patch("agent.nodes.hint_node.load_profile") as mock_load:
        mock_llm.invoke.return_value = mock_hint
        mock_load.return_value = MagicMock(weak_topics={}, avg_gap=5.0)

        from agent.nodes.hint_node import generate_hint
        result = generate_hint(_base_state(current_hint_level=1))

    assert result["current_hint_level"] == 2


def test_generate_hint_increments_turn_count():
    """generate_hint should increment turn_count."""
    mock_hint = MagicMock()
    mock_hint.hint = "Consider a different data structure."
    mock_hint.type = "Conceptual"

    with patch("agent.nodes.hint_node._structured_llm") as mock_llm, \
         patch("agent.nodes.hint_node.load_profile") as mock_load:
        mock_llm.invoke.return_value = mock_hint
        mock_load.return_value = MagicMock(weak_topics={}, avg_gap=5.0)

        from agent.nodes.hint_node import generate_hint
        result = generate_hint(_base_state(turn_count=1))

    assert result["turn_count"] == 2


def test_generate_hint_score_formula():
    """Score should be 100 - gap_magnitude * 10."""
    mock_hint = MagicMock()
    mock_hint.hint = "Hint text"
    mock_hint.type = "Conceptual"

    with patch("agent.nodes.hint_node._structured_llm") as mock_llm, \
         patch("agent.nodes.hint_node.load_profile") as mock_load:
        mock_llm.invoke.return_value = mock_hint
        mock_load.return_value = MagicMock(weak_topics={}, avg_gap=5.0)

        from agent.nodes.hint_node import generate_hint
        result = generate_hint(_base_state(gap_magnitude=4))

    assert result["final_response"]["score"] == 60


# ── Solution Node ─────────────────────────────────────────────────────────────

def test_reveal_solution_structure():
    """reveal_solution should return solution, explanation, and complexity."""
    mock_sol = MagicMock()
    mock_sol.solution_code = "def two_sum(nums, target): ..."
    mock_sol.explanation   = "Use a hashmap to store complements."
    mock_sol.complexity_analysis = "Time: O(N), Space: O(N)"

    with patch("agent.nodes.solution_node._structured_llm") as mock_llm:
        mock_llm.invoke.return_value = mock_sol

        from agent.nodes.solution_node import reveal_solution
        result = reveal_solution(_base_state())

    resp = result["final_response"]
    assert "solution" in resp
    assert "explanation" in resp
    assert "complexity" in resp
    assert resp["score"] == 0
    assert resp["type"] == "Solution"