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app.py
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app.py
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from flask import Flask,render_template,url_for,request
import pandas as pd
import numpy as np
from nltk.stem.porter import PorterStemmer
import re
import string
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
## Definitions
def remove_pattern(input_txt,pattern):
r = re.findall(pattern,input_txt)
for i in r:
input_txt = re.sub(i,'',input_txt)
return input_txt
def count_punct(text):
count = sum([1 for char in text if char in string.punctuation])
return round(count/(len(text) - text.count(" ")),3)*100
app = Flask(__name__)
data = pd.read_csv("sentiment.tsv",sep = '\t')
data.columns = ["label","body_text"]
# Features and Labels
data['label'] = data['label'].map({'pos': 0, 'neg': 1})
data['tidy_tweet'] = np.vectorize(remove_pattern)(data['body_text'],"@[\w]*")
tokenized_tweet = data['tidy_tweet'].apply(lambda x: x.split())
stemmer = PorterStemmer()
tokenized_tweet = tokenized_tweet.apply(lambda x: [stemmer.stem(i) for i in x])
for i in range(len(tokenized_tweet)):
tokenized_tweet[i] = ' '.join(tokenized_tweet[i])
data['tidy_tweet'] = tokenized_tweet
data['body_len'] = data['body_text'].apply(lambda x:len(x) - x.count(" "))
data['punct%'] = data['body_text'].apply(lambda x:count_punct(x))
X = data['tidy_tweet']
y = data['label']
# Extract Feature With CountVectorizer
cv = CountVectorizer()
X = cv.fit_transform(X) # Fit the Data
X = pd.concat([data['body_len'],data['punct%'],pd.DataFrame(X.toarray())],axis = 1)
from sklearn.model_selection import train_test_split
#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
## Using Classifier
clf = LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='liblinear', tol=0.0001, verbose=0,
warm_start=False)
clf.fit(X,y)
@app.route('/')
def home():
return render_template('home.html')
@app.route('/predict',methods=['POST'])
def predict():
if request.method == 'POST':
message = request.form['message']
data = [message]
vect = pd.DataFrame(cv.transform(data).toarray())
body_len = pd.DataFrame([len(data) - data.count(" ")])
punct = pd.DataFrame([count_punct(data)])
total_data = pd.concat([body_len,punct,vect],axis = 1)
my_prediction = clf.predict(total_data)
return render_template('result.html',prediction = my_prediction)
if __name__ == '__main__':
app.run(host='0.0.0.0',port=4000)