import asyncio
import os
import sys
from pathlib import Path

# Add src to path
sys.path.append(str(Path(__file__).resolve().parents[1] / "src"))

from dotenv import load_dotenv

from database.vector import QdrantManager
from llm.models import Model
from query_modes.query_engine import QueryEngine
from utils import setup_logging

load_dotenv()
setup_logging(verbose=False)


def build_query_engine():
    qdrant_manager = QdrantManager(
        url=os.getenv("QDRANT_URL"),
        api_key=os.getenv("QDRANT_API_KEY"),
        host=os.getenv("QDRANT_HOST"),
    )

    embed_base_url = os.getenv("EMBED_BASE_URL", "http://127.0.0.1:11434/v1")
    embed_api_key = os.getenv("EMBED_API_KEY", "ollama")

    dense_embedding_model = Model(
        base_url=embed_base_url,
        api_key=embed_api_key,
        model_name="qwen3-embedding:0.6b",
        provider="ollama",
        embed_model=True,
        use_provider_prefix=False,
    )

    sparse_embedding_model = Model(
        base_url=embed_base_url,
        api_key=embed_api_key,
        model_name="qwen3-embedding:0.6b",
        provider="ollama",
        embed_model=True,
        use_provider_prefix=False,
    )

    chat_model = Model(
        model_name=os.environ.get("MODEL_NAME", "gpt-3.5-turbo"),
        provider=os.environ.get("MODEL_PROVIDER", "openai"),
        use_provider_prefix=False,
        base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
        api_key=os.getenv("GEMINI_API_KEY"),
    )

    return QueryEngine(
        chat_model, dense_embedding_model, sparse_embedding_model, qdrant_manager
    )


TEST_QUERIES = [
    ("Hello there!", "greeting"),
    ("What are your shipping policies?", "faq"),
    ("I want to return my order #12345", "complaint"),
    ("Where is my order #12345?", "order_status"),
    ("Do you have any protein powder?", "product_enquiry"),
    ("Recommend me a pre-workout for beginners", "recommendation_request"),
    ("Thanks, bye!", "goodbye"),
    ("What is the capital of France?", "general_question"),
]


async def run_analysis():
    print("Initializing Query Engine...")
    try:
        engine = build_query_engine()
    except Exception as e:
        print(f"Failed to initialize engine: {e}")
        return

    results: dict[str, dict[str, list[int]]] = {}

    print("\nRunning token analysis...")

    for query, expected_mode in TEST_QUERIES:
        print(f"Testing: {query} ({expected_mode})")
        try:
            # We use get_response_with_suggestions to get full usage
            # We pass an empty history for independent tests
            response = engine.get_response_with_suggestions(query, conv_history=[])
            usage = response.get("token_usage") or {}
            total = (usage.get("total") or {}).get("total_tokens", 0)
            stages = usage.get("stages", {})

            mode_result = results.setdefault(expected_mode, {"total": [], "stages": {}})
            mode_result["total"].append(total)
            for stage, metrics in stages.items():
                stage_totals = mode_result["stages"].setdefault(stage, [])
                stage_totals.append(metrics.get("total_tokens", 0))

            stage_breakdown = " | ".join(
                f"{stage}: {metrics.get('total_tokens', 0)}"
                for stage, metrics in stages.items()
            )
            print(
                f"  -> Tokens: {total} (Prompt: {(usage.get('total') or {}).get('prompt_tokens', 0)}, Completion: {(usage.get('total') or {}).get('completion_tokens', 0)})"
            )
            if stage_breakdown:
                print(f"     Stage breakdown: {stage_breakdown}")
        except Exception as e:
            print(f"  -> Error: {e}")

        await asyncio.sleep(10)

    print("\n--- Analysis Report ---")
    overall_tokens = []
    overall_stage_totals: dict[str, list[int]] = {}
    for mode, data in results.items():
        tokens = data.get("total", [])
        if tokens:
            avg = sum(tokens) / len(tokens)
            print(f"Mode: {mode:<25} | Avg Tokens: {avg:.2f} | Samples: {len(tokens)}")
            overall_tokens.extend(tokens)

            stage_summary = []
            for stage, samples in data.get("stages", {}).items():
                if not samples:
                    continue
                stage_avg = sum(samples) / len(samples)
                stage_summary.append(f"{stage}~{stage_avg:.0f}")
                overall_stage_totals.setdefault(stage, []).extend(samples)

            if stage_summary:
                print(f"   Stage avgs: {', '.join(stage_summary)}")
        else:
            print(f"Mode: {mode:<25} | No successful samples")

    if overall_tokens:
        overall_avg = sum(overall_tokens) / len(overall_tokens)
        print(f"\nOverall Average Tokens per Turn: {overall_avg:.2f}")
        if overall_stage_totals:
            print("Stage contribution averages:")
            for stage, samples in overall_stage_totals.items():
                avg = sum(samples) / len(samples)
                print(f" - {stage}: {avg:.2f}")


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