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import gradio as gr
import hopsworks
import joblib
import pandas as pd
import numpy as np
import json
import time
from datetime import timedelta, datetime
from functions import *
project = hopsworks.login()
fs = project.get_feature_store()
def air_quality(city):
weather_df = pd.DataFrame()
for i in range(8):
weather_data = get_weather_df([get_weather_data((datetime.now() + timedelta(days=i)).strftime("%Y-%m-%d"))])
weather_df = weather_df.append(weather_data)
weather_df = weather_df.drop(columns=["feelslikemin", "feelslikemax", "precipprob", "snowdepth", "uvindex", "date","city","conditions"]).fillna(0)
mr = project.get_model_registry()
model = mr.get_model("hel_gradient_boost_model", version=1)
model_dir = model.download()
model = joblib.load(model_dir + "/hel_model.pkl")
preds = model.predict(weather_df)
predictions = ''
for k in range(7):
predictions += "Predicted AQI on " + (datetime.now() + timedelta(days=k)).strftime('%Y-%m-%d') + ": " + str(int(preds[k]))+"\n"
print(predictions)
return predictions
demo = gr.Interface(fn=air_quality, title="Air quality predictor",
description="Input a value to get next weeks AQI prediction for Helsinki", inputs="text", outputs="text")
if __name__ == "__main__":
demo.launch()
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