Delete sentiment_api.py

sentiment_api.py

@@ -1,135 +0,0 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-from typing import List, Dict
-import os
-import requests
-import yfinance as yf
-import numpy as np
-from transformers import pipeline
-from cachetools import TTLCache, cached
-
-# -----------------------------
-# CONFIG
-# -----------------------------
-NEWSAPI_KEY = os.environ.get("NEWSAPI_KEY", "").strip()  # optional
-MAX_HEADLINES = 3
-MODEL_A = "yiyanghkust/finbert-tone"
-MODEL_B = "mrm8488/distilroberta-finetuned-financial-news-sentiment"
-
-# -----------------------------
-# Load models
-# -----------------------------
-sentiment_a = pipeline("sentiment-analysis", model=MODEL_A, device=-1)
-sentiment_b = pipeline("sentiment-analysis", model=MODEL_B, device=-1)
-
-LABEL_MAP = {
-    "positive": "positive", "neutral": "neutral", "negative": "negative",
-    "Positive": "positive", "Neutral": "neutral", "Negative": "negative",
-    "LABEL_0": "negative", "LABEL_1": "neutral", "LABEL_2": "positive"
-}
-
-# -----------------------------
-# Caching
-# -----------------------------
-# Cache up to 100 stocks, expires after 10 minutes
-stock_cache = TTLCache(maxsize=100, ttl=600)
-
-# -----------------------------
-# News fetchers
-# -----------------------------
-def fetch_news_newsapi(query: str, limit: int = MAX_HEADLINES) -> List[str]:
-    if not NEWSAPI_KEY:
-        return []
-    url = "https://newsapi.org/v2/everything"
-    params = {
-        "q": query,
-        "language": "en",
-        "pageSize": limit,
-        "sortBy": "publishedAt",
-        "apiKey": NEWSAPI_KEY,
-    }
-    try:
-        r = requests.get(url, params=params, timeout=6)
-        r.raise_for_status()
-        data = r.json()
-        articles = data.get("articles", [])[:limit]
-        return [a.get("title", "") for a in articles if a.get("title")]
-    except:
-        return []
-
-def fetch_news_yfinance(ticker: str, limit: int = MAX_HEADLINES) -> List[str]:
-    try:
-        t = yf.Ticker(ticker)
-        news_items = getattr(t, "news", None) or []
-        return [n.get("title") for n in news_items if n.get("title")][:limit]
-    except:
-        return []
-
-def fetch_headlines(stock: str, limit: int = MAX_HEADLINES) -> List[str]:
-    headlines = fetch_news_newsapi(stock, limit)
-    if headlines:
-        return headlines
-    return fetch_news_yfinance(stock, limit)
-
-# -----------------------------
-# Ensemble utilities
-# -----------------------------
-def model_to_vector(pred: Dict) -> np.ndarray:
-    label = pred.get("label", "")
-    score = float(pred.get("score", 0.0))
-    mapped = LABEL_MAP.get(label, label.lower())
-    vec = np.zeros(3)
-    if mapped == "negative":
-        vec[0] = score
-    elif mapped == "neutral":
-        vec[1] = score
-    elif mapped == "positive":
-        vec[2] = score
-    else:
-        vec[1] = score
-    return vec
-
-def headline_score_ensemble(headline: str) -> np.ndarray:
-    a = sentiment_a(headline)[0]
-    b = sentiment_b(headline)[0]
-    return (model_to_vector(a) + model_to_vector(b)) / 2.0
-
-def aggregate_headlines_vectors(vectors: List[np.ndarray]) -> np.ndarray:
-    if not vectors:
-        return np.array([0.0,1.0,0.0])
-    mean_vec = np.mean(vectors, axis=0)
-    mean_vec = np.clip(mean_vec, 0.0, None)
-    total = mean_vec.sum()
-    return mean_vec / total if total > 0 else np.array([0.0,1.0,0.0])
-
-def vector_to_score(vec: np.ndarray) -> float:
-    neg, neu, pos = vec.tolist()
-    score = pos + 0.5 * neu
-    return max(0.0, min(1.0, score))
-
-# -----------------------------
-# FastAPI app
-# -----------------------------
-app = FastAPI(title="Financial Sentiment API")
-
-class StocksRequest(BaseModel):
-    stocks: List[str]
-
-@cached(stock_cache)
-def analyze_single_stock(stock: str) -> float:
-    headlines = fetch_headlines(stock)
-    vectors = [headline_score_ensemble(h) for h in headlines if h and len(h.strip())>10]
-    agg = aggregate_headlines_vectors(vectors)
-    score = round(vector_to_score(agg), 2)
-    return score if score else 0.5
-
-@app.post("/analyze")
-def analyze_stocks(req: StocksRequest):
-    results = {}
-    for stock in req.stocks:
-        results[stock] = analyze_single_stock(stock)
-    return results
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)