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

# 設定 logging
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}"

# 📌 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...")
        response = requests.post(API_URL, headers=HEADERS, json={"inputs": text})
        response.raise_for_status()
        result = response.json()
        logging.info(f"✅ API 回應: {result}")

        if isinstance(result, list) and len(result) > 0:
            sentiment = result[0]["label"]
            confidence = result[0]["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
    except ValueError as e:
        logging.error(f"❌ JSON 解碼錯誤: {e}")
        return f"❌ **JSON 解碼錯誤**: {str(e)}", 0.0
    except KeyError as e:
        logging.error(f"❌ 字典鍵錯誤: {e}")
        return f"❌ **字典鍵錯誤**: {str(e)}", 0.0
    except Exception as e:
        logging.error(f"❌ 未知錯誤: {e}")
        return f"❌ **未知錯誤**: {str(e)}", 0.0