Update app.py

app.py

@@ -1,124 +1,138 @@
-from datetime import datetime
-import requests
-import os
+#!/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_data, get_weather_df, get_weather_json_quick
 
+def greet(name):
+	X = pd.DataFrame()
+	for i in range(8):
+		# Get, rename column and rescalef
+		next_day_date = datetime.today() + timedelta(days=i)
+		next_day = next_day_date.strftime ('%Y-%m-%d')
+		json = get_weather_json_quick(next_day)
+		temp = get_weather_data(json)
+		X = X.append(temp, 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"])
 
 
-def get_weather_csv():
-    return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki?unitGroup=metric&include=days&key=FYYH5HKD9558HBXD2D6KWXDGH&contentType=csv').csv()
-
-def get_weather_json_quick(date):
-    return requests.get(f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/helsinki/today?include=fcst%2Cobs%2Chistfcst%2Cstats%2Cdays&key=J7TT2WGMUNNHD8JBEDXAJJXB2&contentType=json').json()
-
-def get_air_quality_data():
-    AIR_QUALITY_API_KEY = os.getenv('AIR_QUALITY_API_KEY')
-    json = get_air_json(AIR_QUALITY_API_KEY)
-    iaqi = json['iaqi']
-    forecast = json['forecast']['daily']
-    return [
-        json['aqi'],                 # AQI
-        json['time']['s'][:10],      # Date
-        iaqi['h']['v'],
-        iaqi['p']['v'],
-        iaqi['pm10']['v'],
-        iaqi['t']['v'],
-        forecast['o3'][0]['avg'],
-        forecast['o3'][0]['max'],
-        forecast['o3'][0]['min'],
-        forecast['pm10'][0]['avg'],
-        forecast['pm10'][0]['max'],
-        forecast['pm10'][0]['min'],
-        forecast['pm25'][0]['avg'],
-        forecast['pm25'][0]['max'],
-        forecast['pm25'][0]['min'],
-        forecast['uvi'][0]['avg'],
-        forecast['uvi'][0]['avg'],
-        forecast['uvi'][0]['avg']
-    ]
-
-
-def get_weather_data(json):
-    #WEATHER_API_KEY = os.getenv('WEATHER_API_KEY')
-    
-    #csv = get_weather_csv()
-    data = json['days'][0]
-    print("data parsed sccessfully")
-    #return [
-    #    #json['address'].capitalize(),
-    #    data['datetime'],
-    #    data['feelslikemax'],
-    #    data['feelslikemin'],
-    #    data['feelslike'],
-    #    data['dew'],
-    #    data['humidity'],
-    #    data['precip'],
-    #    data['precipprob'],
-    #    data['precipcover'],
-    #    data['snow'],
-    #    data['snowdepth'],
-    #    data['windgust'],
-    #    data['windspeed'],
-    #    data['winddir'],
-    #    data['pressure'],
-    #    data['cloudcover'],
-    #    data['visibility'],
-    #    data['solarradiation'],
-    #    data['solarenergy'],
-    #    data['uvindex'],
-    #    data['conditions']
-    #]
-    return data
-
-
-def get_weather_df(data):
-    col_names = [
-        'name',
-        'datetime',
-        'tempmax',
-        'tempmin',
-        'temp',
-        'feelslikemax',
-        'feelslikemin',
-        'feelslike',
-        'dew',
-        'humidity',
-        'precip',
-        'precipprob',
-        'precipcover',
-        'snow',
-        'snowdepth',
-        'windgust',
-        'windspeed',
-        'winddir',
-        'sealevelpressure',
-        'cloudcover',
-        'visibility',
-        'solarradiation',
-        'solarenergy',
-        'uvindex',
-        'conditions'
-    ]
 
-    
 
-    new_data = pd.DataFrame(
-        data,
-        columns=col_names
-    )
-    new_data.datetime = new_data.datetime.apply(timestamp_2_time1)
-    #new_data.rename(columes={'pressure':'sealevelpressure'})
-    return new_data
-
-def timestamp_2_time1(x):
-    dt_obj = datetime.strptime(str(x), '%Y-%m-%d')
-    dt_obj = dt_obj.timestamp() * 1000
-    return int(dt_obj)
-
-def timestamp_2_time(x):
-    dt_obj = datetime.strptime(str(x), '%m/%d/%Y')
-    dt_obj = dt_obj.timestamp() * 1000
-    return int(dt_obj)
+	X.insert(0,"pm25",0)
+	X.insert(0,"pm10",0)
+	X.insert(0,"o3",0)
+	X.insert(0,"aqi",0)
+	X.insert(27,"conditions",0)
+
+
+	# In[44]:
+
+
+	X.head()
+
+
+	# In[46]:
+
+
+	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")
+	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[ ]:
+
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+
+    
+if __name__ == "__main__":
+    demo.launch()