Update app.py

app.py

@@ -1,59 +1,133 @@
+#!/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 decode_features
+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)
 
-    project = hopsworks.login()
-    mr = project.get_model_registry()
-    #api = project.get_dataset_api()
-    fs = project.get_feature_store()
-    feature_view = fs.get_feature_view(
-        name = 'hel_air_fv1',
-        version = 1
-    )
 
-    # start_time = 1672614000000
-    # #start_date = datetime.now() - timedelta(days=1)
-    # #start_time = int(start_date.timestamp()) * 1000
+	# In[38]:
 
 
-    # X = feature_view.get_batch_data(start_time=start_time)
-    # latest_date_unix = str(X.date.values[0])[:10]
-    # latest_date = time.ctime(int(latest_date_unix))
+	X.head()
+	X.columns.values.tolist()
 
-    # X = X.drop(columns=["date"]).fillna(0)
 
+	# In[39]:
 
-    model = mr.get_model("gradient_boost_model",version = 4)
-    model_dir = model.download()
 
-#     preds = model.predict(X)
+	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)
 
-#    # cities = [city_tuple[0] for city_tuple in cities_coords.keys()]
 
-#     next_day_date = datetime.today() + timedelta(days=1)
-#     next_day = next_day_date.strftime ('%d/%m/%Y')
-#  #   df = pd.DataFrame(data=preds[0], columns=[f"AQI Predictions for {next_day}"], dtype=int)
-#     str1 = ""
-# #    return int(preds[0])
+	# In[40]:
 
 
-#     for x in range(8):
-#       if(x != 0):
-#          str1 += (datetime.now() + timedelta(days=x)).strftime('%Y-%m-%d') + " predicted aqi:      " + str(int(preds[len(preds) - 8 + x]))+"\n"
-    
-#     print(str1)
-    return "model got"
+	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")
@@ -61,4 +135,4 @@ demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 
     
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()