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import gradio as gr
import tensorflow as tf
import numpy as np
import pickle
# Load model, including its weights and the optimizer
model = tf.keras.models.load_model('core4.h5')
# load tokenizer
with open('tokenizer.pickle', 'rb') as handle:
tokenize = pickle.load(handle)
text_labels = ['How to apply', 'how much can I get', 'who can apply']
# model.summary() # model architecture
def greet(string):
tokenizedText = tokenize.texts_to_matrix([string])
prediction = model.predict(np.array([tokenizedText[0]]))
predicted_label = text_labels[np.argmax(prediction)]
print(prediction[0][np.argmax(prediction)])
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'
res=rs.get(url=csv_url)
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
body = {
"arguments": {"range": "Sheet1!A"+str(len(df)+2), "valueInputOption": "USER_ENTERED"},
"body": {"values": [[string]]}
}
res = requests.post(url, json.dumps(body), headers={'Content-Type': 'application/json'})
body = {
"arguments": {"range": "Sheet1!B"+str(len(df)+2), "valueInputOption": "USER_ENTERED"},
"body": {"values": [[predicted_label]]}
}
res = requests.post(url, json.dumps(body), headers={'Content-Type': 'application/json'})
import datetime
current_time = datetime.datetime.now()
body = {
"arguments": {"range": "Sheet1!C"+str(len(df)+2), "valueInputOption": "USER_ENTERED"},
"body": {"values": [[str(current_time)]]}
}
res = requests.post(url, json.dumps(body), headers={'Content-Type': 'application/json'})
#print(res.text)
#######################
return predicted_label
#One testing case
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |