#!/usr/bin/env python
import json

import pandas as pd
from eas_prediction import TFRequest

if __name__ == '__main__':
    fg_config = {}
    with open("/Users/zhao/Downloads/feature_list_20260403.json", "r") as f:
        fg_config = json.load(f)

    df = pd.read_csv("/Users/zhao/Downloads/DataWorks_SQL查询_结果3_20260515170950_0.csv").sample(n=500, random_state=50)
    df['r_vid'] = df['vid'].copy()

    shape = df.shape[0]

    req = TFRequest('serving_default')

    for feature_info in fg_config['features']:
        feature_name = feature_info['feature_name']
        value_type = feature_info['value_type']
        if value_type == 'Double':
            df[feature_name] = pd.to_numeric(df[feature_name], errors='coerce').fillna(0).astype(float)
            content = df[feature_name].tolist()

            req.add_feed(feature_name, [shape], TFRequest.DT_DOUBLE, content)
        else:
            df[feature_name] = df[feature_name].astype(object).fillna('-1024').astype(str)
            content = [s.encode('utf-8') for s in df[feature_name].tolist()]

            req.add_feed(feature_name, [shape], TFRequest.DT_STRING, content)

    req.add_fetch('y_return_n_uv')
    req.add_fetch('probs_is_share')

    with open("/Users/zhao/Desktop/warm_up_20260413.bin", "wb") as fw:
        fw.write(req.to_string())