dream

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import logging
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import time
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import os
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import sentry_sdk
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from catboost import CatBoostClassifier
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from flask import Flask, request, jsonify
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from score import get_features
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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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logging.getLogger("werkzeug").setLevel("WARNING")
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app = Flask(__name__)
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def get_probas(contexts, hypotheses):
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    features = get_features(contexts, hypotheses)
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    pred = cb.predict_proba(features)[:, 1]
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    return pred
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try:
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    cb = CatBoostClassifier()
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    cb.load_model("model-confidence-convert-old_midas.cbm")
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    contexts = [
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        [
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            "i'm good how are you",
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            "Spectacular, by all reports! Do you want to know what I can do?",
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            "absolutely",
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            "I'm a socialbot, and I'm all about chatting with people like you. "
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            "I can answer questions, share fun facts, discuss movies, books and news. What do you want to talk about?",
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            "let's talk about movies",
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        ]
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    ]
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    hypotheses = [
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        {
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            "is_best": True,
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            "text": "Kong: Skull Island is a good action movie. What do you think about it?",
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            "confidence": 1.0,
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            "convers_evaluator_annotator": {
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                "isResponseOnTopic": 0.505,
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                "isResponseErroneous": 0.938,
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                "responseEngagesUser": 0.344,
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                "isResponseInteresting": 0.084,
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                "isResponseComprehensible": 0.454,
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            },
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        }
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    ]
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    get_probas(contexts, hypotheses)
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except Exception as e:
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    logger.exception("Scorer not loaded")
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    sentry_sdk.capture_exception(e)
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    raise e
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@app.route("/batch_model", methods=["POST"])
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def batch_respond():
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    st_time = time.time()
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    contexts = request.json["contexts"]
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    hypotheses = request.json["hypotheses"]
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    try:
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        responses = get_probas(contexts, hypotheses).tolist()
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    except Exception as e:
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        responses = [0] * len(hypotheses)
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        sentry_sdk.capture_exception(e)
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        logger.exception(e)
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    logging.warning(f"hypothesis_scorer exec time {time.time() - st_time}")
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    return jsonify([{"batch": responses}])
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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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