# -*- coding: utf-8 -*-
"""
Three-Tier Source Attribution Test Script
==========================================
Tests that AI responses correctly attribute their source using the right
trailing emoji indicators:

  📚 Tier 1 — Verified plant database (plants_database.json)
  📖 Tier 2 — Verified RAG document (knowledge_chunks, similarity >=85%)
  ⚠️  Tier 3 — AI-generated estimate (Cerebras LLM fallback)

Usage:
    python tests/test_source_attribution.py
"""

import asyncio
import sys
from pathlib import Path

# Force UTF-8 output so emojis render correctly on all terminals
sys.stdout.reconfigure(encoding="utf-8")

# Add parent directory to path so imports work
sys.path.insert(0, str(Path(__file__).parent.parent))

MOCK_SENSORS = {"temp": 28.5, "rh": 70.0, "light": 15000}

# ─────────────────────────────────────────────────────────────────────────────
# Test Cases
# ─────────────────────────────────────────────────────────────────────────────

TEST_CASES = [
    # --- Tier 1: Deterministic DB (plant in plants_database.json) ---
    {
        "id": "T1-A",
        "description": "Known plant in DB - Indonesian query (tomat)",
        "query": "berapa suhu ideal tomat fase vegetatif?",
        "expected_emoji": "📚",
    },
    {
        "id": "T1-B",
        "description": "Known plant in DB - English query (watermelon)",
        "query": "what are the parameters for growing watermelon seedling?",
        "expected_emoji": "📚",
    },
    {
        "id": "T1-C",
        "description": "Known plant in DB - Indonesian (semangka)",
        "query": "apa parameter pertumbuhan semangka?",
        "expected_emoji": "📚",
    },
    {
        "id": "T1-D",
        "description": "Known plant in DB - lettuce germination",
        "query": "suhu dan kelembaban untuk perkecambahan selada?",
        "expected_emoji": "📚",
    },
    {
        "id": "T1-E",
        "description": "Chamber status - real-time sensor data",
        "query": "berapa suhu chamber sekarang?",
        "expected_emoji": "📚",
    },

    # --- Tier 3: LLM Fallback (plant NOT in DB) ---
    {
        "id": "T3-A",
        "description": "Unknown plant - LLM fallback (durian)",
        "query": "apa parameter pertumbuhan durian?",
        "expected_emoji": "⚠️",
    },
    {
        "id": "T3-B",
        "description": "Unknown plant - LLM fallback (strawberry)",
        "query": "what temperature does strawberry need to grow?",
        "expected_emoji": "⚠️",
    },

    # --- General / Technical (always AI Generated) ---
    {
        "id": "T3-C",
        "description": "General plant question (no specific plant)",
        "query": "bagaimana cara mempercepat pertumbuhan tanaman secara umum?",
        "expected_emoji": "⚠️",
    },
    {
        "id": "T3-D",
        "description": "Technical IoT question",
        "query": "how does a DHT22 humidity sensor work?",
        "expected_emoji": "⚠️",
    },
    {
        "id": "T3-E",
        "description": "General knowledge question",
        "query": "what is photosynthesis?",
        "expected_emoji": "⚠️",
    },
]


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

def detect_source_emoji(response_text: str) -> str | None:
    """
    Detect the trailing source attribution emoji in the response.
    Checks the last 8 lines for Source attribution lines.
    """
    lines = response_text.strip().split("\n")
    for line in reversed(lines[-8:]):
        if "📚" in line and "Source" in line:
            return "📚"
        if "📖" in line and "Source" in line:
            return "📖"
        if "⚠️" in line and "Source" in line:
            return "⚠️"
    # Fallback: scan full response for opener indicators
    if "📚 According" in response_text or "📚 Source" in response_text:
        return "📚"
    if "📖 Source" in response_text:
        return "📖"
    if "⚠️ Note" in response_text or "⚠️ Source" in response_text:
        return "⚠️"
    return None


def truncate(text: str, n: int = 400) -> str:
    return text[:n] + "\n    ...[truncated]" if len(text) > n else text


# ─────────────────────────────────────────────────────────────────────────────
# Main Test Runner
# ─────────────────────────────────────────────────────────────────────────────

async def run_tests():
    from app.ai_engine import generate_context_aware_response

    passed = 0
    failed = 0
    results = []

    W = 72
    print("\n" + "=" * W)
    print("  THREE-TIER SOURCE ATTRIBUTION TEST SUITE")
    print("=" * W)
    print(f"  📚 Tier 1: Verified DB  |  📖 Tier 2: Verified Doc (>=85%)  |  ⚠️ Tier 3: AI Generated")
    print("=" * W)
    print(f"{'ID':<8} {'EXPECTED':<12} {'GOT':<12} {'STATUS':<8} DESCRIPTION")
    print("-" * W)

    for case in TEST_CASES:
        try:
            result = await generate_context_aware_response(
                query=case["query"],
                sensors=MOCK_SENSORS,
            )
            response_text = result.get("response", "")
            detected = detect_source_emoji(response_text)
            ok = (detected == case["expected_emoji"])
            status = "✅ PASS" if ok else "❌ FAIL"
            if ok:
                passed += 1
            else:
                failed += 1

            print(f"{case['id']:<8} {case['expected_emoji']:<12} {str(detected):<12} {status:<8} {case['description']}")
            results.append({
                "case": case,
                "response": response_text,
                "detected": detected,
                "data_source": result.get("data_source", "unknown"),
                "query_type": result.get("query_type", "unknown"),
                "passed": ok,
            })

        except Exception as e:
            failed += 1
            print(f"{case['id']:<8} {'?':<12} {'ERROR':<12} ❌ FAIL  {case['description']}")
            print(f"         Exception: {e}")
            results.append({
                "case": case,
                "response": f"ERROR: {e}",
                "detected": None,
                "data_source": "error",
                "query_type": "error",
                "passed": False,
            })

    # ─── Detailed Response Printout ───────────────────────────────────────────
    print("\n" + "=" * W)
    print("  DETAILED RESPONSES")
    print("=" * W)

    for r in results:
        case = r["case"]
        icon = "✅" if r["passed"] else "❌"
        print(f"\n{icon} [{case['id']}] {case['description']}")
        print(f"   Query      : {case['query']}")
        print(f"   Query Type : {r['query_type']}")
        print(f"   Data Source: {r['data_source']}")
        print(f"   Detected   : {r['detected']}   (Expected: {case['expected_emoji']})")
        print(f"   Response   :")
        for line in truncate(r["response"], 500).split("\n"):
            print(f"      {line}")

    # ─── Summary ─────────────────────────────────────────────────────────────
    total = len(TEST_CASES)
    print("\n" + "=" * W)
    print(f"  RESULTS: {passed}/{total} passed  |  {failed} failed")
    if failed == 0:
        print("  ✅ All source attribution tests PASSED!")
    else:
        print("  ❌ Some tests FAILED — check prompt attribution rules above.")
    print("=" * W + "\n")


if __name__ == "__main__":
    asyncio.run(run_tests())