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app.py
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app.py
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from flask import Flask, request, Response, send_file
from flask_cors import CORS, cross_origin
import pandas as pd
import numpy as np
import re
import os
import waterfall_chart
import matplotlib
matplotlib.use('Agg')
import nltk
nltk.download("stopwords")
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from nltk.corpus import stopwords
stopwords = stopwords.words('english')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.backends.backend_svg import FigureCanvasSVG
from matplotlib.figure import Figure
import io
app = Flask(__name__)
CORS(app, support_credentials=True)
def pre_process(text):
"""
:param text: Unprocessed text
:return: Processed text
"""
text = text.lower()
text = re.sub('</?.*?>', '<>', text)
text = re.sub("(\\d|\\W)+", " ", text)
return text
def sort_coo(coo_matrix):
"""
:param coo_matrix: COO Matrix
:return: Sorted matrix based on data in descending order
"""
tuples = zip(coo_matrix.col, coo_matrix.data)
return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)
def extract_topn_from_vector(feature_names, sorted_items, topn=10):
"""
:param feature_names: Feature names of keywords from word count vector
:param sorted_items: Matrix from sort_coo matrix
:param topn: Number of keywords required
:return: Dictionary with words as keys and score as values
"""
sorted_items = sorted_items[:topn]
score_vals, feature_vals = [], []
for idx, score in sorted_items:
score_vals.append(round(score, 3))
feature_vals.append(feature_names[idx])
results = {}
for idx in range(len(feature_vals)):
results[feature_vals[idx]] = score_vals[idx]
return results
@app.route('/')
def hi():
return 'hello world'
@app.route('/test')
def hello_world():
# Sandbox testing
skills = [['hi', 'hello', 'hey', 'heya'], ['hi', 'hello', 'hey', 'heya']]
y_pos = [[10, 20, 30, 50], [20, 40, 60, 80]]
atts = ['jan', 'feb']
df = pd.DataFrame({})
output = plot_stacked_bar_chart(atts, y_pos, skills, 'Performance', 'Fast')
return Response(output.getvalue(), mimetype="image/png")
@app.route('/student/<performance_type>', methods=['POST'])
def create_bar_graph(performance_type):
"""
:param performance_type: self : students own performance
:return: path to stored bar chart for given months and skill
"""
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
data = request.json
skills = request.form.get('skills')
skills = eval(skills)
values = request.form.get('values')
values = eval(values)
print(values)
months = request.form.get('months')
months = eval(months)
N = len(skills)
x = np.arange(N)
p = []
for i in range(N):
p.append(ax.bar(x + i * 0.25, values[i], width=0.25))
z = [*p]
ax.legend(handles=z , labels=skills, loc='upper left')
ax.figure.savefig('image.png')
return {'path': f'{os.getcwd()}/image.png'}
@app.route('/comments/keywords', methods=['POST'])
def extract_keywords():
skills = request.form.get('skills')
print(skills)
values = request.form.get('comments')
attribute_values = eval(skills)
values = eval(values)
res = {}
for i in range(len(values)):
values[i] = [pre_process(z) for z in values[i]]
# print(text_values[i])
cv = CountVectorizer(max_df=0.85, stop_words=stopwords, max_features=10000)
word_count_vector = cv.fit_transform(values[i])
tfidf_transformer = TfidfTransformer(smooth_idf=True, use_idf=True)
tfidf_transformer.fit(word_count_vector)
feature_names = cv.get_feature_names()
doc = ''.join(d for d in values[i])
tf_idf_vector = tfidf_transformer.fit_transform(cv.transform([doc]))
sorted_items = sort_coo(tf_idf_vector.tocoo())
keywords = {}
keywords = extract_topn_from_vector(feature_names, sorted_items)
# print(keywords)
res[attribute_values[i]] = keywords
return res
@app.route('/comments/sentiment', methods=['POST'])
def decide_which_product_kit():
skills = request.form.get('skills')
values = request.form.get('comments')
points = request.form.get('points')
points = eval(points)
attribute_values = eval(skills)
text_values = eval(values)
assert len(attribute_values) == len(points.keys()) # Sanity check
res = {}
analyser = SentimentIntensityAnalyzer()
for i in range(len(text_values)):
text_values[i] = '.'.join(t for t in text_values[i])
score = analyser.polarity_scores(text_values[i])
res[attribute_values[i]] = score['compound']
for z in list(points.keys()):
res[z] += points[z]
return res # Recommend the one with the lowest score
@app.route('/class/perform', methods=['POST'])
def send_waterfall_chart():
data = {'skill': request.form.get('skill'),
'values': request.form.get('values'),
'months': request.form.get('months')}
print(data)
data['months'] = eval(data['months'])
data['values'] = eval(data['values'])
data['values'] = [float(x) for x in data['values']]
for i in range(1, len(data['values'])):
data['values'][i] -= data['values'][i - 1]
waterfall_chart.plot(data['months'], data['values'], Title=data['skill']).savefig(
f'{data["skill"]}{len(data["months"])}.png')
return {'Path': f"{os.getcwd()}/{data['skill']}{len(data['months'])}.png"}
if __name__ == '__main__':
app.run()