import json import pickle import pandas as pd import numpy as np from datetime import datetime from flask import Flask, jsonify, Response from model import download_data, format_data, train_model from config import model_file_path app = Flask(__name__) def update_data(): """Download price data, format data and train model.""" download_data() format_data() train_model() def get_eth_inference(): """Load model and predict current price.""" with open(model_file_path, "rb") as f: loaded_model = pickle.load(f) now_timestamp = pd.Timestamp(datetime.now()).timestamp() X_new = np.array([now_timestamp]).reshape(-1, 1) current_price_pred = loaded_model.predict(X_new) return current_price_pred[0] @app.route("/inference/") def generate_inference(token): """Generate inference for given token.""" if not token or token != "ETH": error_msg = "Token is required" if not token else "Token not supported" return Response(json.dumps({"error": error_msg}), status=400, mimetype='application/json') try: inference = get_eth_inference() return Response(str(inference), status=200) except Exception as e: return Response(json.dumps({"error": str(e)}), status=500, mimetype='application/json') @app.route("/update") def update(): """Update data and return status.""" try: update_data() return "0" except Exception: return "1" if __name__ == "__main__": update_data() app.run(host="0.0.0.0", port=8000)