2024-08-25 21:55:45 +03:00
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import json
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import pickle
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import pandas as pd
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import numpy as np
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from datetime import datetime
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from flask import Flask, jsonify, Response
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2024-09-04 22:25:48 +03:00
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from model import download_data, format_data, train_model, get_training_data_path
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2024-08-25 21:55:45 +03:00
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from config import model_file_path
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app = Flask(__name__)
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def update_data():
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2024-09-04 22:25:48 +03:00
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"""Download price data, format data and train model for each token."""
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tokens = ["ETH", "BTC", "SOL", "BNB", "ARB"]
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2024-08-25 21:55:45 +03:00
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download_data()
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2024-09-04 22:25:48 +03:00
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for token in tokens:
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format_data(token)
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train_model(token)
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2024-08-25 21:55:45 +03:00
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2024-09-04 22:22:25 +03:00
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def get_inference(token, period):
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2024-09-03 04:24:43 +03:00
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try:
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2024-09-04 22:25:48 +03:00
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model_path = model_file_path[token]
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with open(model_path, "rb") as f:
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2024-09-03 04:24:43 +03:00
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loaded_model = pickle.load(f)
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2024-09-04 22:25:48 +03:00
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# Загружаем последние данные для данного токена
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training_price_data_path = get_training_data_path(token)
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2024-09-03 04:24:43 +03:00
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price_data = pd.read_csv(training_price_data_path)
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2024-08-25 21:55:45 +03:00
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2024-09-03 04:24:43 +03:00
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# Используем последние значения признаков для предсказания
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last_row = price_data.iloc[-1]
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last_timestamp = last_row["timestamp"]
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2024-09-04 22:25:48 +03:00
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# Преобразуем период в секунды
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2024-09-04 22:22:25 +03:00
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period_seconds = convert_period_to_seconds(period)
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new_timestamp = last_timestamp + period_seconds
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2024-09-04 22:25:48 +03:00
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# Формируем данные для предсказания с новым timestamp
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X_new = np.array(
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[
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new_timestamp,
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last_row["price_diff"],
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last_row["volatility"],
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last_row["volume"],
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last_row["moving_avg_7"],
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last_row["moving_avg_30"],
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2024-09-03 04:24:43 +03:00
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]
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).reshape(1, -1)
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2024-09-04 22:25:48 +03:00
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# Делаем предсказание
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future_price_pred = loaded_model.predict(X_new)
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return future_price_pred[0]
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2024-09-03 04:24:43 +03:00
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except Exception as e:
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print(f"Error during inference: {str(e)}")
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raise
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2024-08-25 21:55:45 +03:00
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2024-09-04 22:22:25 +03:00
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def convert_period_to_seconds(period):
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"""Конвертируем период в секунды."""
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if period.endswith("min"):
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return int(period[:-3]) * 60
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elif period.endswith("h"):
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return int(period[:-1]) * 3600
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elif period.endswith("d"):
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return int(period[:-1]) * 86400
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else:
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raise ValueError(f"Unknown period format: {period}")
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2024-09-04 22:22:25 +03:00
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@app.route("/inference/<string:token>/<string:period>")
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def generate_inference(token, period):
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"""Generate inference for given token and period."""
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try:
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inference = get_inference(token, period)
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return Response(str(inference), status=200)
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except Exception as e:
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return Response(
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json.dumps({"error": str(e)}), status=500, mimetype="application/json"
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)
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2024-08-25 21:55:45 +03:00
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@app.route("/update")
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def update():
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"""Update data and return status."""
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try:
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update_data()
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return "0"
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except Exception:
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return "1"
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if __name__ == "__main__":
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update_data()
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app.run(host="0.0.0.0", port=8080)
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