Update app.py

app.py

@@ -2,6 +2,13 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 import pickle
+import requests as rs
+import pandas as pd
+import json
+import requests
+import datetime
+
+
 # Load model, including its weights and the optimizer
 model = tf.keras.models.load_model('core4.h5')
 # load tokenizer
@@ -17,8 +24,7 @@ def greet(string):
   print("Predicted label: " + predicted_label + "\n")
   
   ###################
-  import requests as rs
-  import pandas as pd
+
   
   spreadsheet_id = '1vjWnYsnGc0J6snT67NVbA-NWSGZ5b0eDBVHmg9lbf9s'  # Please set the Spreadsheet ID.
   csv_url='https://docs.google.com/spreadsheets/d/' + spreadsheet_id + '/export?format=csv&id=' + spreadsheet_id + '&gid=0'
@@ -27,8 +33,7 @@ def greet(string):
   open('google.csv', 'wb').write(res.content)
   df = pd.read_csv('google.csv')
   
-  import json
-  import requests
+
   
   spreadsheet_id = '1vjWnYsnGc0J6snT67NVbA-NWSGZ5b0eDBVHmg9lbf9s'  # Please set the Spreadsheet ID.  
   url = 'https://script.google.com/macros/s/AKfycbwXP5fsDgOXJ9biZQC293o6bTBL3kDOJ07PlmxKjabzdTej6WYdC8Yos6NpDVqAJeVM/exec?spreadsheetId=' + spreadsheet_id
@@ -44,7 +49,7 @@ def greet(string):
   }
   res = requests.post(url, json.dumps(body), headers={'Content-Type': 'application/json'})
   
-  import datetime
+
   current_time = datetime.datetime.now()
   body = {
       "arguments": {"range": "Sheet1!D"+str(len(df)+2), "valueInputOption": "USER_ENTERED"},