foohm71

from google.colab import drive
drive.mount('/content/gdrive')

import gensim
from gensim.models import KeyedVectors
import numpy as np
import pandas as pd

import nltk
from nltk import word_tokenize

foohm71

def make_autopct(values):
    def my_autopct(pct):
        total = sum(values)
        val = int(round(pct*total/100.0))
        return '{p:.1f}%  ({v:d})'.format(p=pct,v=val)
    return my_autopct
  
def plotPieChart(column):
  freq_count = Counter(df[column])
  fig, ax = plt.subplots(figsize=(15, 7), subplot_kw=dict(aspect="equal"))

foohm71

import pandas as pd
import matplotlib.pylab as plt
import numpy as np 
from collections import Counter
from wordcloud import WordCloud, STOPWORDS 

from gensim.summarization.summarizer import summarize 
from gensim.summarization import keywords 

from IPython.display import display, Markdown

foohm71

from sklearn.metrics import confusion_matrix
import matplotlib.pylab as plt

# Train/Test split was done earlier hence I just need to use Xtest and Ytest
result = []
for feature in Xtest:
   p = get_prediction(feature, model_name)
   result.append(p.payload[0].display_name)
  
labels = ['Optional', 'Trivial', 'Minor', 'Major', 'Critical', 'Blocker']

foohm71

# write training data function
def write_training_data_to_zipfile(path, Xdata, Ydata):
  os.chdir(path)
  count = {}
  num_rows = Xdata.shape[0]
  for i in range(1,num_rows):
    label = Ydata.iloc[i]
    feature = Xdata.iloc[i]
    folder = path + "/" + label
    if not os.path.exists(folder):

foohm71

# [START gae_python38_app]
from flask import Flask, request
import sys
import os
from google.api_core.client_options import ClientOptions
from google.cloud import automl_v1
from google.cloud.automl_v1.proto import service_pb2

# If `entrypoint` is not defined in app.yaml, App Engine will look for an app
# called `app` in `main.py`.