gsheet

.ipynb_checkpoints/secrets-checkpoint.toml

@@ -0,0 +1 @@
+public_gsheets_url = "https://docs.google.com/spreadsheets/d/1_LCwPOJYujuRtRE8KWrks76aSye_uu6lY39rsoFGjh4/edit?usp=sharing"

app.py

@@ -10,10 +10,12 @@ import seaborn as sns
 from streamlit_folium import st_folium
 import matplotlib.pyplot as plt
 from datetime import datetime
-from datasets import Dataset
+import gspread
+from oauth2client.service_account import ServiceAccountCredentials
 from utils import *
 
 ########## Title for the Web App ##########
+st.write(client_email)
 st.title("Property Price Predictor")
 st.markdown('_Creator: GOH Hong Aik [[LinkedIn]](https://www.linkedin.com/in/hongaikgoh/)_')
 st.markdown("""This app predicts your house price based on a few indicators, and displays amenities within 2 km. Please note the following:
@@ -113,19 +115,6 @@ with st.form("inputs"):
         elif input_propertyType == 'HDB':
             propertyType_HDB = 1        
 
-        # log searches
-        search_master = pd.read_csv('data/search.csv')
-        search_df = pd.DataFrame({'datetime': [datetime.now().strftime('%Y-%m-%d %H:%M:%S')],
-                                'postal': [int(input_postal)],
-                                'storey': [input_storey],
-                                'age': [input_age_asof_transaction],
-                                'floor_area_sqft': [input_floor_area_sqft],
-                                'property_type': [input_propertyType]
-        })
-        search_final = pd.concat([search_master, search_df])
-        search_dataset = Dataset.from_pandas(search_final)
-        search_dataset.to_csv('data/search.csv', index=False)
-
 
         latlong_geo['district'] = postal_final.loc[postal_final['2dpostal'] == input_postal[:2], 'district'].item() # all districts are covered in train set
         latlong_geo['storey'] = input_storey
@@ -302,5 +291,24 @@ with st.form("inputs"):
     
         st_folium(m, width=700, height=450)
     
-
-    
+        # Log searches
+        keydict = {'type': service_account,
+                'project_id': project_id,
+                'private_key_id': private_key_id,
+                'private_key': private_key,
+                'client_email': client_email,
+                'client_id': client_id,
+                'auth_uri': auth_uri,
+                'token_uri': token_uri,
+                'auth_provider_x509_cert_url': auth_provider_x509_cert_url,
+                'client_x509_cert_url': client_x509_cert_url}
+
+        scope = ['https://www.googleapis.com/auth/spreadsheets',
+         "https://www.googleapis.com/auth/drive"]
+
+        credentials = ServiceAccountCredentials.from_json_keyfile_dict(keydict, scope)
+        client = gspread.authorize(credentials)
+
+        sheet = client.open("PropertyPricePredictor_Sheet").sheet1
+        sheet.append_row([datetime.now().strftime('%Y-%m-%d %H:%M:%S'), int(input_postal),input_storey,input_age_asof_transaction,input_floor_area_sqft,input_propertyType])
+            

data/search.csv

@@ -1 +0,0 @@
-datetime,postal,storey,age,floor_area_sqft,property_type

requirements.txt

@@ -8,4 +8,6 @@ seaborn==0.11.1
 streamlit_folium
 matplotlib==3.3.4
 xgboost==1.5.1
-category_encoders==2.5.1.post0
+category_encoders==2.5.1.post0
+gspread
+oauth2client