Update app.py

app.py

@@ -1,138 +1,124 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[37]:
-
-
-import gradio as gr
-import hopsworks
+from datetime import datetime
+import requests
+import os
 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"])
 
 
 
 
-	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")
-
+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'
+    ]
 
     
-if __name__ == "__main__":
-    demo.launch()
+
+    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)