"""Sentiment Analyzer service — main entry point.

Consumes ``news:raw`` articles from Redis Streams, scores them using a
tiered approach (FinBERT first, Ollama fallback for low-confidence results),
extracts ticker mentions, and publishes ``ScoredArticle`` messages to
``news:scored``.
"""

from __future__ import annotations

import asyncio
import logging
import time

from redis.asyncio import Redis

from services.sentiment_analyzer.analyzers.finbert import FinBERTAnalyzer
from services.sentiment_analyzer.analyzers.ollama_analyzer import OllamaAnalyzer
from services.sentiment_analyzer.config import SentimentAnalyzerConfig
from services.sentiment_analyzer.ticker_extractor import extract_tickers
from shared.redis_streams import StreamConsumer, StreamPublisher
from shared.schemas.news import RawArticle, ScoredArticle
from shared.telemetry import setup_telemetry

logger = logging.getLogger(__name__)


async def process_article(
    article: RawArticle,
    finbert: FinBERTAnalyzer,
    ollama: OllamaAnalyzer,
    publisher: StreamPublisher,
    config: SentimentAnalyzerConfig,
    counters: dict,
) -> None:
    """Score a single article and publish one ScoredArticle per extracted ticker.

    Parameters
    ----------
    article:
        The raw article consumed from the ``news:raw`` stream.
    finbert:
        FinBERT analyzer instance.
    ollama:
        Ollama analyzer instance (used as fallback).
    publisher:
        Publishes results to ``news:scored``.
    config:
        Service configuration (confidence threshold, etc.).
    counters:
        Dict of OpenTelemetry counter/histogram instruments.
    """
    start = time.monotonic()

    # --- Step 1: Run FinBERT ---
    score, confidence = await finbert.analyze(article.title, article.content)
    model_used = "finbert"
    counters["finbert_count"].add(1)

    # --- Step 2: Fallback to Ollama if confidence is too low ---
    if confidence < config.finbert_confidence_threshold:
        logger.info(
            "FinBERT confidence %.2f below threshold %.2f — falling back to Ollama",
            confidence,
            config.finbert_confidence_threshold,
        )
        score, confidence = await ollama.analyze(article.title, article.content)
        model_used = "ollama"
        counters["ollama_count"].add(1)

    elapsed = time.monotonic() - start
    counters["inference_latency"].record(elapsed)

    # --- Step 3: Extract tickers ---
    combined_text = f"{article.title} {article.content}"
    tickers = extract_tickers(combined_text)

    if not tickers:
        logger.debug("No tickers found in article: %s", article.title[:80])
        # Still count the article as scored even if no tickers found.
        counters["articles_scored"].add(1)
        return

    # --- Step 4: Publish one ScoredArticle per ticker ---
    for ticker in tickers:
        scored = ScoredArticle(
            source=article.source,
            url=article.url,
            title=article.title,
            content=article.content,
            published_at=article.published_at,
            fetched_at=article.fetched_at,
            content_hash=article.content_hash,
            ticker=ticker,
            sentiment_score=score,
            confidence=confidence,
            model_used=model_used,
            entities=tickers,
        )
        await publisher.publish(scored.model_dump(mode="json"))
        logger.debug("Published scored article for %s (score=%.2f)", ticker, score)

    counters["articles_scored"].add(1)


async def run(config: SentimentAnalyzerConfig | None = None) -> None:
    """Main service loop.

    Connects to Redis, initialises analysers and telemetry, then
    continuously consumes from ``news:raw`` and publishes to ``news:scored``.
    """
    if config is None:
        config = SentimentAnalyzerConfig()

    logging.basicConfig(level=config.log_level)
    logger.info("Starting Sentiment Analyzer service")

    # --- Telemetry ---
    meter = setup_telemetry("sentiment-analyzer", config.otel_metrics_port)
    counters = {
        "articles_scored": meter.create_counter(
            "articles_scored",
            description="Total articles scored by the sentiment analyzer",
        ),
        "finbert_count": meter.create_counter(
            "finbert_count",
            description="Number of articles scored by FinBERT",
        ),
        "ollama_count": meter.create_counter(
            "ollama_count",
            description="Number of articles scored by Ollama (fallback)",
        ),
        "inference_latency": meter.create_histogram(
            "inference_latency_seconds",
            description="Time spent on sentiment inference per article",
            unit="s",
        ),
    }

    # --- Redis ---
    redis = Redis.from_url(config.redis_url, decode_responses=False)
    consumer = StreamConsumer(redis, "news:raw", "sentiment-analyzer", "worker-1")
    publisher = StreamPublisher(redis, "news:scored")

    # --- Analyzers ---
    finbert = FinBERTAnalyzer(
        model_name=config.finbert_model,
        max_content_length=config.max_content_length,
    )
    ollama = OllamaAnalyzer(model=config.ollama_model, host=config.ollama_host)

    logger.info("Consuming from news:raw, publishing to news:scored")

    # --- Consume loop ---
    async for _msg_id, data in consumer.consume():
        try:
            article = RawArticle.model_validate(data)
            await process_article(article, finbert, ollama, publisher, config, counters)
        except Exception:
            logger.exception("Error processing article: %s", data.get("title", "<unknown>"))


def main() -> None:
    """CLI entry point."""
    asyncio.run(run())


if __name__ == "__main__":
    main()