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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()
def air_quality(city):
start_date = datetime.now() - timedelta(days=1)
start_time = int(start_date.timestamp()) * 1000
X = pd.read_csv('x.csv')
#X = X.drop(columns=["date"]).fillna(0)
X = X.drop(X.columns[0],axis=1)
mr = project.get_model_registry()
model = mr.get_model("gradient_boost_paris_model", version=1)
model_dir = model.download()
print(model_dir)
model = joblib.load(model_dir + "/model.pkl")
preds = model.predict(X)
#print(model.predict(X)[:7])
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 + model_dir
demo = gr.Interface(fn=air_quality, title="Air quality predictor",
description="Input a value to get next weeks AQI prediction for Paris", inputs="text", outputs="text")
demo.launch() |