app.py

from fastapi import FastAPI, Request
from sentence_transformers import SentenceTransformer, util
import json
import torch
import os

app = FastAPI()

# 阈值 (低于这个分数强制返回 neutral)
THRESHOLD = 0.35

print("正在加载 BGE-Large-ZH-v1.5...")
# 这里会自动下载模型，如果日志卡在这里请耐心等待
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
print("模型加载完成")

def load_data():
    if not os.path.exists('emoji_labels.json'):
        print("警告: 找不到 emoji_labels.json")
        return [], None
    with open('emoji_labels.json', 'r', encoding='utf-8') as f:
        data = json.load(f)
    texts = [item['text'] for item in data]
    # 预计算向量
    embeddings = model.encode(texts, normalize_embeddings=True, convert_to_tensor=True)
    return data, embeddings

# 初始化数据
emoji_data, emoji_embeddings = load_data()

@app.get("/")
def home():
    return "Kouri 5-Emotion System Ready"

@app.post("/match")
async def match_emoji(request: Request):
    """
    输入: {"text": "我想吃汉堡"}
    输出: {"label": "happy", "score": 0.85}
    """
    try:
        body = await request.json()
        user_text = body.get("text", "")
        
        # 兜底：空输入或者数据没加载好，返回 neutral
        if not user_text or emoji_embeddings is None:
            return {"label": "neutral", "score": 0.0}

        # 构造查询 (BGE模型建议加前缀)
        query_text = "为这个句子分类情感：" + user_text
        query_emb = model.encode(query_text, normalize_embeddings=True, convert_to_tensor=True)
        
        # 计算相似度
        scores = util.cos_sim(query_emb, emoji_embeddings)[0]
        best_score = float(torch.max(scores))
        best_idx = int(torch.argmax(scores))
        
        matched_item = emoji_data[best_idx]
        
        # 打印日志
        print(f"输入: {user_text} | 匹配: {matched_item['label']} | 分数: {best_score:.4f}")

        # 逻辑判断
        if best_score > THRESHOLD:
            return {
                "label": matched_item['label'], # 例如 "happy"
                "score": best_score             # 例如 0.8512
            }
        else:
            # 分数太低，返回 neutral
            return {
                "label": "neutral", 
                "score": best_score
            }
            
    except Exception as e:
        print(f"Error: {e}")
        # 发生任何报错都返回 neutral，保证程序不崩
        return {"label": "neutral", "score": 0.0}