Add DeepSeek/xAI/OpenAI-compatible sentiment providers; custom dark dropdown
Providers (admin-switchable, no redeploy): - DeepSeek and any OpenAI-compatible endpoint (OpenRouter, Together, Groq, local Ollama) via a generic Chat Completions adapter + base_url - xAI Grok with Live Search (search_parameters web+X, citations) — grounded tier alongside OpenAI and Gemini - DeepSeek / generic compatible endpoints are ungrounded (no web search); UI shows an amber warning and labels each provider's grounding - Optional env fallbacks DEEPSEEK_API_KEY / XAI_API_KEY UI: replace native <select> (unstyleable white popup on Windows) with a custom dark Dropdown component everywhere — sentiment provider, scanner filters, market sort, indicators, admin universe, user role. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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"""Sentiment provider for any OpenAI-compatible Chat Completions endpoint.
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Covers DeepSeek, OpenRouter, Together, Groq, Mistral, local Ollama, etc. — any
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service exposing the OpenAI Chat Completions API at a custom base_url.
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NOTE: Unlike the OpenAI Responses provider and Gemini, this path has NO web
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search grounding. Sentiment reflects the model's training knowledge, not live
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news. Cheap, but not real-time.
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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from datetime import datetime, timezone
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from pathlib import Path
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import httpx
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from openai import AsyncOpenAI
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from app.exceptions import ProviderError, RateLimitError
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from app.providers.protocol import SentimentData
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logger = logging.getLogger(__name__)
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_CA_BUNDLE = os.environ.get("SSL_CERT_FILE", "")
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_SENTIMENT_PROMPT = """\
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Assess the CURRENT market sentiment for the stock ticker {ticker} based on your \
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knowledge of the company, its sector, and recent developments you are aware of.
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Respond ONLY with a JSON object in this exact format (no markdown, no extra text):
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{{"classification": "<bullish|bearish|neutral>", "confidence": <0-100>, "reasoning": "<brief explanation>"}}
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Rules:
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- classification must be exactly one of: bullish, bearish, neutral
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- confidence must be an integer from 0 to 100
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- reasoning should be a brief one-sentence explanation
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"""
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_SENTIMENT_PROMPT_SEARCH = """\
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Search the web and X for the LATEST news, analyst opinions, and market developments \
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about the stock ticker {ticker} from the past 24-48 hours.
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Based on your search findings, analyze the CURRENT market sentiment.
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Respond ONLY with a JSON object in this exact format (no markdown, no extra text):
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{{"classification": "<bullish|bearish|neutral>", "confidence": <0-100>, "reasoning": "<brief explanation citing recent news>"}}
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Rules:
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- classification must be exactly one of: bullish, bearish, neutral
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- confidence must be an integer from 0 to 100
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- reasoning should cite specific recent news or events you found
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"""
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VALID_CLASSIFICATIONS = {"bullish", "bearish", "neutral"}
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def _clean_json_text(raw: str) -> str:
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clean = raw.strip()
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if clean.startswith("```"):
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clean = clean.split("\n", 1)[1] if "\n" in clean else clean[3:]
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if clean.endswith("```"):
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clean = clean[:-3]
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return clean.strip()
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class OpenAICompatibleSentimentProvider:
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"""Sentiment via the OpenAI Chat Completions API at a configurable base_url."""
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def __init__(
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self,
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api_key: str,
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model: str,
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base_url: str,
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source: str = "openai_compatible",
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live_search: bool = False,
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extra_body: dict | None = None,
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) -> None:
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if not api_key:
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raise ProviderError("API key is required")
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if not base_url:
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raise ProviderError("base_url is required for an OpenAI-compatible provider")
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if not model:
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raise ProviderError("model is required")
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http_kwargs: dict = {}
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if _CA_BUNDLE and Path(_CA_BUNDLE).exists():
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http_kwargs["verify"] = _CA_BUNDLE
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http_client = httpx.AsyncClient(**http_kwargs)
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self._client = AsyncOpenAI(api_key=api_key, base_url=base_url, http_client=http_client)
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self._model = model
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self._source = source
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self._live_search = live_search
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self._extra_body = extra_body
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@staticmethod
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def _extract_citations(response: object) -> list[dict[str, str]]:
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"""Best-effort extraction of xAI Live Search citations (list of URLs)."""
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raw = getattr(response, "citations", None)
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if not raw:
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extra = getattr(response, "model_extra", None) or {}
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raw = extra.get("citations") if isinstance(extra, dict) else None
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citations: list[dict[str, str]] = []
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for item in raw or []:
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if isinstance(item, str):
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citations.append({"url": item, "title": ""})
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elif isinstance(item, dict) and item.get("url"):
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citations.append({"url": str(item["url"]), "title": str(item.get("title", ""))})
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return citations
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async def fetch_sentiment(self, ticker: str) -> SentimentData:
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prompt = _SENTIMENT_PROMPT_SEARCH if self._live_search else _SENTIMENT_PROMPT
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kwargs: dict = {}
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if self._extra_body:
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kwargs["extra_body"] = self._extra_body
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try:
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response = await self._client.chat.completions.create(
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model=self._model,
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messages=[
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{
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"role": "system",
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"content": "You are a financial sentiment analyst. Always respond with valid JSON only, no markdown fences.",
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},
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{"role": "user", "content": prompt.format(ticker=ticker)},
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],
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temperature=0.3,
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**kwargs,
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)
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raw_text = (response.choices[0].message.content or "").strip()
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if not raw_text:
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raise ProviderError(f"Empty response from {self._source} for {ticker}")
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parsed = json.loads(_clean_json_text(raw_text))
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classification = str(parsed.get("classification", "")).lower()
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if classification not in VALID_CLASSIFICATIONS:
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raise ProviderError(
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f"Invalid classification '{classification}' from {self._source} for {ticker}"
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)
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confidence = max(0, min(100, int(parsed.get("confidence", 50))))
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reasoning = str(parsed.get("reasoning", ""))
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if reasoning:
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logger.info(
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"%s sentiment for %s: %s (confidence=%d) — %s",
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self._source, ticker, classification, confidence, reasoning,
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)
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return SentimentData(
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ticker=ticker,
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classification=classification,
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confidence=confidence,
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source=self._source,
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timestamp=datetime.now(timezone.utc),
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reasoning=reasoning,
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citations=self._extract_citations(response) if self._live_search else [],
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)
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except json.JSONDecodeError as exc:
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logger.error("Failed to parse %s JSON for %s: %s", self._source, ticker, exc)
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raise ProviderError(f"Invalid JSON from {self._source} for {ticker}") from exc
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except ProviderError:
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raise
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except Exception as exc:
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msg = str(exc).lower()
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if "429" in msg or "rate" in msg or "quota" in msg or "insufficient" in msg:
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raise RateLimitError(f"{self._source} rate limit hit for {ticker}") from exc
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logger.error("%s provider error for %s: %s", self._source, ticker, exc)
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raise ProviderError(f"{self._source} provider error for {ticker}: {exc}") from exc
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