sentiment.py

import requests
import logging
from config import HEADERS, MODEL_OPTIONS, DEFAULT_MODEL

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

CURRENT_MODEL = DEFAULT_MODEL
API_URL = f"https://api-inference.huggingface.co/models/{CURRENT_MODEL}"

# 📌 **轉換英文分類為台灣用語**
def translate_sentiment(label):
    label = label.lower()
    if "positive" in label:
        return "😃 **開心、正面**"
    elif "neutral" in label:
        return "😐 **普通、沒特別感覺**"
    else:
        return "😡 **負面、沒那麼開心**"

# 📌 呼叫 Hugging Face API 進行情緒分析
def analyze_sentiment(text, model_name=None):
    global CURRENT_MODEL, API_URL

    if model_name and MODEL_OPTIONS[model_name] != CURRENT_MODEL:
        CURRENT_MODEL = MODEL_OPTIONS[model_name]
        API_URL = f"https://api-inference.huggingface.co/models/{CURRENT_MODEL}"
        logging.info(f"🔄 切換模型: {CURRENT_MODEL}")

    try:
        logging.info("🚀 發送 API 請求...")
        print(f"📢 [Debug] API URL: {API_URL}")
        print(f"📢 [Debug] 輸入文本: {text}")

        response = requests.post(API_URL, headers=HEADERS, json={"inputs": text})
        response.raise_for_status()
        result = response.json()

        print(f"📢 [Debug] API 回應: {result}")

        # 📌 **修正回應格式**
        if isinstance(result, list) and len(result) > 0 and isinstance(result[0], list):
            result = result[0]  # 取得內層列表

        if isinstance(result, list) and len(result) > 0:
            # 取得最高分的情緒分類
            best_sentiment = max(result, key=lambda x: x["score"])
            sentiment = translate_sentiment(best_sentiment["label"])  # ✅ **轉換為台灣用語**
            confidence = best_sentiment["score"]
            return f"**情緒分類**: {sentiment}\n**AI 信心度**: {confidence*100:.2f}%", confidence
        else:
            return "⚠️ **無法分析文本，請稍後再試**", 0.0

    except requests.exceptions.RequestException as e:
        logging.error(f"❌ API 請求錯誤: {e}")
        return f"❌ **API 請求錯誤**: {str(e)}", 0.0