app.py

import os
import gradio as gr
import xgboost as xgb
import numpy as np
import pandas as pd
import requests
from huggingface_hub import hf_hub_download

# **📌 先运行 `preprocess.py` 处理数据**
if not os.path.exists("processed_data.csv"):
    print("📌 运行 `preprocess.py` 进行数据处理...")
    os.system("python preprocess.py")

# **📌 加载处理后的数据**
processed_data_path = "processed_data.csv"
if not os.path.exists(processed_data_path):
    raise FileNotFoundError("❌ `processed_data.csv` 未找到，请先运行 `preprocess.py` 处理数据！")

df = pd.read_csv(processed_data_path)

# **📌 加载 XGBoost 预测模型**
model_path = hf_hub_download(repo_id="YDluffy/lottery_prediction", filename="lottery_xgboost_model.json")
model = xgb.XGBRegressor()
model.load_model(model_path)

# **📌 调用 LLM API 解析用户输入**
LLM_API_URL = "https://your-llm-space.gradio.app"  # 替换为 LLM 服务器地址

def get_prediction_from_llm(user_input):
    # 调用 LLM 提取预测所需的特征
    response = requests.post(LLM_API_URL + "/api/predict", json={"input": user_input})
    llm_output = response.json().get("prediction", "")

    # 解析 LLM 输出（示例解析）
    year, period = 2025, 16
    nums = [5, 12, 23, 34, 45, 56]
    special = 7

    # **📌 预测**
    prediction = predict_lottery(year, period, *nums, special)
    
    return f"预测的号码是: {prediction}\n\n模型解析的特征：{llm_output}"

# **📌 预测函数**
def predict_lottery(year, period, num1, num2, num3, num4, num5, num6, special):
    # 从历史数据查找对应的月份和日期
    history = df[(df['期号_年份'] == year) & (df['期数'] == period)]
    if not history.empty:
        month = history.iloc[0]['月份']
        day = history.iloc[0]['日期']
    else:
        month, day = 1, 1  # 默认值

    # 计算中奖号码均值
    avg_number = np.mean([num1, num2, num3, num4, num5, num6])

    # 形成特征数组
    features = np.array([[year, period, month, day, num1, num2, num3, num4, num5, num6, special, avg_number]])

    # 进行预测
    prediction = model.predict(features)
    return prediction

# **📌 Gradio Web 界面**
iface = gr.Interface(
    fn=get_prediction_from_llm,
    inputs=gr.Textbox(label="请输入问题或期号信息"),
    outputs="text",
    title="六合彩预测模型",
    description="通过与 LLM 交互，解析输入信息并进行预测"
)

# **📌 启动 Gradio 应用**
iface.launch(share=True)