dream

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import logging
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import json
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import os
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import time
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import numpy as np
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import sentry_sdk
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from flask import Flask, jsonify, request
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sentry_sdk.init(os.getenv("SENTRY_DSN"))
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logging.basicConfig(format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO)
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logger = logging.getLogger(__name__)
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app = Flask(__name__)
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with open("midas_prediction_counters.json", "r") as f:
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    counters = json.load(f)
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def inference(last_midas_label, return_probas):
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    global counters
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    # counters['appreciation'] = {'appreciation': 0.09, 'comment': 0.15, 'opinion': 0.39, 'pos_answer': 0.13, ...}
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    if return_probas:
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        return counters[last_midas_label]
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    else:
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        # randomly choose with probability
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        return np.random.choice(list(counters[last_midas_label].keys()), p=list(counters[last_midas_label].values()))
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        # return max(counters[last_midas_label], key=counters[last_midas_label].get)
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@app.route("/respond", methods=["POST"])
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def respond():
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    st_time = time.time()
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    last_midas_labels = request.json["last_midas_labels"]
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    return_probas = request.json.get("return_probas", 0)
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    result = [inference(midas_label, return_probas) for midas_label in last_midas_labels]
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    total_time = time.time() - st_time
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    logger.info(f"midas-predictor exec time: {total_time:.3f}s")
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    return jsonify(result)
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if __name__ == "__main__":
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    app.run(debug=False, host="0.0.0.0", port=3000)
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