import requests
from config import HEADERS

# 📌 預設載入的模型
CURRENT_MODEL = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
API_URL = f"https://api-inference.huggingface.co/models/{CURRENT_MODEL}"

# 轉換英文分類為中文
def translate_sentiment(label):
    if "positive" in label.lower():
        return "😃 **正向**"
    elif "neutral" in label.lower():
        return "😐 **中立**"
    else:
        return "😡 **負向**"

# 轉換信心度
def convert_confidence(score):
    percentage = round(score * 100)
    if score >= 0.90:
        return f"🌟 **極高信心** ({percentage}%)"
    elif score >= 0.75:
        return f"✅ **高信心** ({percentage}%)"
    elif score >= 0.50:
        return f"⚠️ **中等信心** ({percentage}%)"
    elif score >= 0.30:
        return f"❓ **低信心** ({percentage}%)"
    else:
        return f"❌ **極低信心（建議忽略）** ({percentage}%)"

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

    # **只在模型變更時更新 API URL**
    if model_id and model_id != CURRENT_MODEL:
        CURRENT_MODEL = model_id
        API_URL = f"https://api-inference.huggingface.co/models/{CURRENT_MODEL}"

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

        if isinstance(result, list) and len(result) > 0:
            sentiment = translate_sentiment(result[0]["label"])
            confidence = result[0]["score"]
            confidence_label = convert_confidence(confidence)

            return f"**情緒分類**: {sentiment}\n**AI 判斷的信心度**: {confidence_label}", confidence
        else:
            return "⚠️ **無法分析文本，請稍後再試**", 0.0
    
    except Exception as e:
        return f"❌ **錯誤**: {str(e)}", 0.0