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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') |
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