flask_http.py

import pandas as pd
from sklearn.linear_model import LogisticRegression
import flask

df = pd.read_csv("https://github.com/bgweber/Twitch/raw/master/Recommendations/games-expand.csv")
model = LogisticRegression()
model.fit(df.drop(['label'], axis=1), df['label'])

app = flask.Flask(__name__)

@app.route("/", methods=["GET","POST"])
def predict():
    data = {"success": False}
    
    params = flask.request.json
    if params is None:
        params = flask.request.args

    if "G1" in params.keys(): 
        new_row = { "G1": params.get("G1"), "G2": params.get("G2"), 
                    "G3": params.get("G3"), "G4": params.get("G4"), 
                    "G5": params.get("G5"), "G6": params.get("G6"), 
                    "G7": params.get("G7"), "G8": params.get("G8"), 
                    "G9": params.get("G9"), "G10": params.get("G10") }

        new_x = pd.DataFrame.from_dict(new_row, orient = "index").transpose()                
        data["response"] = str(model.predict_proba(new_x)[0][1])
        data["success"] = True

    return flask.jsonify(data)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=80)