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import flask |
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from flask import request |
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import os |
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from dotenv import load_dotenv |
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load_dotenv() |
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app = flask.Flask(__name__, template_folder="./") |
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from transformers import pipeline |
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classifier = pipeline('text-classification', model="bsenst/classify_services_model") |
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@app.route('/') |
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def index(): |
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return flask.render_template('index.html') |
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@app.route("/", methods=["POST"]) |
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def predict(): |
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incoming = request.get_json() |
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print(incoming) |
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prediction = classifier(incoming["text"])[0] |
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print(prediction) |
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return prediction |
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if __name__ == '__main__': |
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app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860))) |
