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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # In[37]: | |
| import gradio as gr | |
| import hopsworks | |
| import joblib | |
| import pandas as pd | |
| import numpy as np | |
| import folium | |
| import sklearn.preprocessing as proc | |
| import json | |
| import time | |
| from datetime import timedelta, datetime | |
| from branca.element import Figure | |
| from functions import get_weather_by_date | |
| def greet(name): | |
| X = pd.DataFrame() | |
| for i in range(8): | |
| # Get, rename column and rescalef | |
| target_date = datetime.today() + timedelta(days=i) | |
| target_date = target_date.strftime('%Y-%m-%d') | |
| json = get_weather_by_date(target_date) | |
| X = X.append(json['days'][0], ignore_index=True) | |
| # In[38]: | |
| X.head() | |
| X.columns.values.tolist() | |
| # In[39]: | |
| X.drop('preciptype', inplace = True, axis = 1) | |
| X.drop('severerisk', inplace = True, axis = 1) | |
| X.drop('stations', inplace = True, axis = 1) | |
| X.drop('sunrise', inplace = True, axis = 1) | |
| X.drop('sunset', inplace = True, axis = 1) | |
| X.drop('moonphase', inplace = True, axis = 1) | |
| X.drop('description', inplace = True, axis = 1) | |
| X.drop('icon', inplace = True, axis = 1) | |
| X.drop('datetime', inplace = True, axis = 1) | |
| # In[40]: | |
| X.head() | |
| # In[41]: | |
| X = X.rename(columns={'sunriseEpoch':'pm25'}) | |
| X = X.rename(columns={'sunsetEpoch':'pm10'}) | |
| X = X.rename(columns={'source':'o3'}) | |
| X = X.rename(columns={'normal':'aqi'}) | |
| X = X.rename(columns={'datetimeEpoch':'city'}) | |
| # In[42]: | |
| X.head() | |
| # In[43]: | |
| # X = X.drop(columns = ['conditions', "pm25", "pm10", "o3", "aqi"]) | |
| X = X.drop(columns = ['conditions', "pm25", "pm10", "o3"]) | |
| X.insert(0,"aqi",0) | |
| X.insert(0,"o3",0) | |
| X.insert(0,"pm10",0) | |
| X.insert(0,"pm25",0) | |
| X.insert(27,"conditions",0) | |
| # In[44]: | |
| X.head() | |
| # In[46]: | |
| preds = model.predict(X) | |
| # In[51]: | |
| print(preds) | |
| # In[53]: | |
| str1 = "" | |
| for x in range(8): | |
| if(x != 0): | |
| str1 += (datetime.now() + timedelta(days=x)).strftime('%Y-%m-%d') + " predicted aqi: " + str(int(preds[x]))+"\n" | |
| print(str1) | |
| return str1 | |
| # In[ ]: | |
| project = hopsworks.login() | |
| mr = project.get_model_registry() | |
| # In[50]: | |
| model = mr.get_model("gradient_boost_model",version = 4) | |
| model_dir = model.download() | |
| model = joblib.load(model_dir + "/model.pkl") | |
| demo = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| if __name__ == "__main__": | |
| demo.launch() | |