63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
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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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from model import download_data, format_data, train_model
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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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"""Download price data, format data and train model."""
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download_data()
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format_data()
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train_model()
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def get_eth_inference():
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"""Load model and predict current price."""
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with open(model_file_path, "rb") as f:
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loaded_model = pickle.load(f)
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now_timestamp = pd.Timestamp(datetime.now()).timestamp()
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X_new = np.array([now_timestamp]).reshape(-1, 1)
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current_price_pred = loaded_model.predict(X_new)
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return current_price_pred[0][0]
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@app.route("/inference/<string:token>")
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def generate_inference(token):
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"""Generate inference for given token."""
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if not token or token != "ETH":
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error_msg = "Token is required" if not token else "Token not supported"
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return Response(
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json.dumps({"error": error_msg}), status=400, mimetype="application/json"
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)
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try:
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inference = get_eth_inference()
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return jsonify({"value": str(inference)})
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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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@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=8000)
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