signal-platform/app/providers/gemini_sentiment.py

"""Gemini sentiment provider using google-genai with search grounding."""

from __future__ import annotations

import json
import logging
import os
from datetime import datetime, timezone
from pathlib import Path

from google import genai
from google.genai import types

from app.exceptions import ProviderError, RateLimitError
from app.providers.protocol import SentimentData

logger = logging.getLogger(__name__)

# Ensure aiohttp's cached SSL context includes our corporate CA bundle.
# aiohttp creates _SSL_CONTEXT_VERIFIED at import time; we must patch it
# after import so that google-genai's aiohttp session trusts our proxy CA.
_CA_BUNDLE = os.environ.get("SSL_CERT_FILE", "")
if _CA_BUNDLE and Path(_CA_BUNDLE).exists():
    try:
        import aiohttp.connector as _aio_conn
        if hasattr(_aio_conn, "_SSL_CONTEXT_VERIFIED") and _aio_conn._SSL_CONTEXT_VERIFIED is not None:
            _aio_conn._SSL_CONTEXT_VERIFIED.load_verify_locations(cafile=_CA_BUNDLE)
            logger.debug("Patched aiohttp _SSL_CONTEXT_VERIFIED with %s", _CA_BUNDLE)
    except Exception:
        logger.warning("Could not patch aiohttp SSL context", exc_info=True)

_SENTIMENT_PROMPT = """\
Search the web for the latest news, analyst ratings/opinions, and retail/social \
discussion (e.g. Reddit, StockTwits) about the stock ticker {ticker} from roughly \
the past 1-2 weeks.

Assess (1) the current market sentiment and (2) whether BUYING here looks advisable now.

Respond ONLY with a JSON object (no markdown, no extra text):
{{"classification": "<bullish|bearish|neutral>", "confidence": <0-100>, "recommendation": "<buy|hold|avoid>", "reasoning": "<a thorough paragraph citing specific analyst views, news, and retail sentiment you found, and what drives the recommendation>"}}

Rules:
- classification = overall mood/tone (bullish, bearish, neutral)
- recommendation = actionable view on buying now (buy, hold, avoid)
- confidence must be an integer from 0 to 100
- reasoning should be several sentences citing specific, recent findings
"""

VALID_CLASSIFICATIONS = {"bullish", "bearish", "neutral"}
VALID_RECOMMENDATIONS = {"buy", "hold", "avoid"}


def _parse_recommendation(value: object) -> str | None:
    v = str(value or "").strip().lower()
    return v if v in VALID_RECOMMENDATIONS else None


def _extract_citations(response: object) -> list[dict[str, str]]:
    """Pull source URLs/titles from Gemini's grounding metadata."""
    citations: list[dict[str, str]] = []
    try:
        candidates = getattr(response, "candidates", None) or []
        for cand in candidates:
            meta = getattr(cand, "grounding_metadata", None)
            for chunk in (getattr(meta, "grounding_chunks", None) or []):
                web = getattr(chunk, "web", None)
                if web is not None:
                    citations.append({
                        "url": getattr(web, "uri", "") or "",
                        "title": getattr(web, "title", "") or "",
                    })
    except Exception:
        pass
    return citations


class GeminiSentimentProvider:
    """Fetches sentiment analysis from Gemini with search grounding."""

    def __init__(self, api_key: str, model: str = "gemini-2.0-flash") -> None:
        if not api_key:
            raise ProviderError("Gemini API key is required")
        self._client = genai.Client(api_key=api_key)
        self._model = model

    async def fetch_sentiment(self, ticker: str) -> SentimentData:
        """Send a structured prompt to Gemini and parse the JSON response."""
        try:
            response = await self._client.aio.models.generate_content(
                model=self._model,
                contents=_SENTIMENT_PROMPT.format(ticker=ticker),
                config=types.GenerateContentConfig(
                    tools=[types.Tool(google_search=types.GoogleSearch())],
                    response_mime_type="application/json",
                ),
            )

            raw_text = response.text.strip()
            logger.debug("Gemini raw response for %s: %s", ticker, raw_text)
            parsed = json.loads(raw_text)

            classification = parsed.get("classification", "").lower()
            if classification not in VALID_CLASSIFICATIONS:
                raise ProviderError(
                    f"Invalid classification '{classification}' from Gemini for {ticker}"
                )

            confidence = int(parsed.get("confidence", 50))
            confidence = max(0, min(100, confidence))

            reasoning = parsed.get("reasoning", "")
            if reasoning:
                logger.info("Gemini sentiment for %s: %s (confidence=%d) — %s",
                            ticker, classification, confidence, reasoning)

            return SentimentData(
                ticker=ticker,
                classification=classification,
                confidence=confidence,
                source="gemini",
                timestamp=datetime.now(timezone.utc),
                reasoning=reasoning,
                citations=_extract_citations(response),
                recommendation=_parse_recommendation(parsed.get("recommendation")),
            )

        except json.JSONDecodeError as exc:
            logger.error("Failed to parse Gemini JSON for %s: %s", ticker, exc)
            raise ProviderError(f"Invalid JSON from Gemini for {ticker}") from exc
        except ProviderError:
            raise
        except Exception as exc:
            msg = str(exc).lower()
            if "429" in msg or "resource exhausted" in msg or "quota" in msg or ("rate" in msg and "limit" in msg):
                raise RateLimitError(f"Gemini rate limit hit for {ticker}") from exc
            logger.error("Gemini provider error for %s: %s", ticker, exc)
            raise ProviderError(f"Gemini provider error for {ticker}: {exc}") from exc