Create app.py

app.py

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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
+
+###################################################
+import gradio as gr
+from transformers import pipeline
+from datetime import datetime
+import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+import re
+benefits = [
+  {"benefitName": "Universal Credit", "coreName": "what is this benefit", "link": "https://www.gov.uk/universal-credit/"},
+  {"benefitName": "Universal Credit", "coreName": "who can apply", "link": "https://www.gov.uk/universal-credit/eligibility"},
+  {"benefitName": "Universal Credit", "coreName": "how much can I get", "link": "https://www.gov.uk/universal-credit/what-youll-get,https://www.gov.uk/universal-credit/how-youre-paid"},
+  {"benefitName": "Universal Credit", "coreName": "How to apply", "link": "https://www.gov.uk/universal-credit/how-to-claim"}
+]
+def requestPage(link):
+  page = requests.get(link)
+  # print(page.text)
+  soup = BeautifulSoup(page.content, "html.parser")
+  return soup
+  
+def scrapeTable(table):
+  columns = [col.text.strip() for col in table.thead.tr.find_all()]
+  columns
+  rows = table.tbody.find_all(recursive=False)
+  clean_rows = ""
+  for row in rows:
+    elements = ["{}: {}".format(columns[index], element.text.strip()) for index, element in enumerate(row.find_all(recursive=False))]
+    elements = " ".join(elements)
+    # print(elements)
+    clean_rows += elements + "\n"
+  return clean_rows
+def scrapePage(page):
+  # Scrape the text
+  corpus = ""
+  # starting from the main page
+  content = page.find('div', {"id":"guide-contents"})
+  title = content.find('h1', {"class":"part-title"})
+  title = title.text.strip()
+  corpus += title +"\n\n"
+  print(title)
+  content = content.find('div', {"class":"gem-c-govspeak"})
+  fragments = content.find_all(recursive=False)
+  for frag in fragments:
+    text= frag.text.strip()
+    if frag.name == 'ul':
+      clean = re.sub('\n+', "{;}", text)
+      corpus += "{;}" + clean
+    elif frag.name == 'table':
+      corpus += scrapeTable(frag)
+    else:
+      corpus += text 
+    corpus += "\n"
+  # print(corpus)
+  return corpus
+  
+  
+for benefit in benefits:
+  links = benefit['link'].split(',')
+  print(benefit['benefitName'], benefit['coreName'], len(links))
+  context = ""
+  for link in links:
+    page = requestPage(link)
+    context += scrapePage(page)
+  benefit['context'] = context
+  benefit['contextLen'] = len(context)
+  print("--------------------------------")
+benefitsClasses = list(set(list(map(lambda x: x['benefitName'], benefits))))
+core4Classes = list(set(list(map(lambda x: x['coreName'], benefits))))
+# contexts
+benefitsClasses, core4Classes
+question_answerer = pipeline("question-answering")
+coreName = 'how much can I get'
+def testQA(question):
+  predictedBenefit = "Universal Credit"
+  predictedCore = coreName
+  time = datetime.now()
+  context = list(filter(lambda x: x['benefitName']==predictedBenefit and x['coreName']==predictedCore, benefits))[0]
+  answer = question_answerer(question = question, context = context['context'])['answer']
+  time3 = (datetime.now() - time).total_seconds()
+  return answer
+  
+iface = gr.Interface(fn=testQA, inputs="text", outputs="text")
+iface.launch()
+
+
+
+
+
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()