I'm trying to make Front-end for my “Sentiment Analysis” project but can't figure out how to make my “prediction_function” work at the Front-end

Question

I'm making a Sentiment Analysis project which can be used for any language. Here's how it works: At the end part of the code "result" translates a sentence into English. Then predict_function(result.text) classifies the English text as positive, negative or neutral.

The code works fine if I run it separately. Now I'm trying to make Front-end and the only issue is I can't figure out how to link prediction_function with it. Translation function is working there but the only thing left is to classify that translated text on Front-end. I'm new to this and I did make many changes but couldn't get it worked.

This is my whole code: (I guess no need to look at the whole code because I feel the issue is at the end part, after @app.route('/', methods=['POST']) line)

from flask import Flask, request, render_template
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import nltk
import pandas as pd
import numpy as np
import seaborn as sns
import regex as re
import math

import googletrans

from googletrans import Translator
from nltk.tokenize import word_tokenize

app = Flask(__name__)

@app.route('/')
def my_form():
    return render_template('form.html')

df = pd.read_csv('C:/Users/path/file.csv')
df = df.rename(columns = {'clean_text':'Comment'})
df.head()

df.describe()

cat = []
for val in df['category'].values:
  if val not in cat:
    cat.append(val)
print(cat)

index_arr = []
for index, val in df.iterrows():
    if val['category'] not in [-1.0, 0.0, 1.0]:
        index_arr.append(index)
print(index_arr)
df.drop(index_arr, axis = 0, inplace = True)

sns.countplot(x='category',data=df)

def clean_comments(comment):
    comment = re.sub(r'\$\w*', '', str(comment))
    comment = re.sub(r'^RT[\s]+', '', str(comment))
    comment = re.sub(r'https?:\/\/.*[\r\n]*', '', str(comment))
    comment = re.sub(r'#', '', str(comment))
    comment = re.sub(r"@[^\s]+[\s]?",'',comment)
    comment = re.sub('[^ a-zA-Z0-9]', '', comment)
    comment = re.sub('[0-9]', '', comment)
    return comment

df['Comment'] = df['Comment'].apply(clean_comments)
df.head()

nltk.download('stopwords')
from nltk.corpus import stopwords

stop_words = stopwords.words('english')

def removing_stopwords(words):
  cleaned_tokens = []
  for val in words.split(' '):
    val = val.lower()
    if val not in stop_words and val != '':
      cleaned_tokens.append(val)
  return(cleaned_tokens)

df['Comment'] = df['Comment'].apply(removing_stopwords) 
df.head()

from nltk.stem.porter import PorterStemmer

def stem_comments(words):
  ps = PorterStemmer()
  stemmed_review = []
  for review in  words:
    stemmed_review.append(ps.stem(review))
  return stemmed_review

df['Comment'] = df['Comment'].apply(stem_comments)   
df.head()

temp = df.iloc[:,0].values
X = [' '.join(ele) for ele in temp]
X = np.array(X)
Y = df.iloc[:,1].values

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(max_features=5000)
X = vectorizer.fit_transform(X).toarray()
print(X.shape)

print(Y[:5])
print(Y.shape)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.01)

print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)

del X
del Y
del temp
del df

from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB()
classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
print("Accuracy = ", accuracy_score(y_pred, y_test))

import seaborn as sn
from matplotlib.figure import Figure
df_cm = pd.DataFrame(cm, index = [0,1,2],columns = [0,1,2])
f = Figure(figsize = (20,10))
sn.heatmap(df_cm, annot=True)

def predict_function(sentence):
    sentence = clean_comments(sentence)
    sentence = removing_stopwords(sentence)
    sentence = stem_comments(sentence)
    
    X = [' '.join([str(elem) for elem in sentence])]
    X = np.array(X)
    X = vectorizer.transform(X).toarray()
    
    result = classifier.predict(X)

    if result == -1.0:
        print("Negative")
    elif result == 0.0:
        print("Neutral")
    else:
        print("Positive")

@app.route('/', methods=['POST'])
def my_form_post():
    text1 = request.form['text1'].lower()

    translator = Translator(service_urls=['translate.googleapis.com'])
    result = translator.translate(text1, dest='en')
    senti=predict_function(result.text)

    return render_template('form.html', final=result.text, last=senti, text1=text1)



if __name__ == "__main__":
    app.run(debug=True, host="127.0.0.1", port=5002, threaded=True)

HTML code for Front-end:

<body>
    <h1>Welcome To Sentiment Analyzer</h1>
    <form method="POST">
        <textarea name="text1" placeholder="Say Something: ...." rows="10" cols="109"></textarea><br><br>

        <input class="example_a" type="submit">
    </form>
    {% if final %}
    <div> 
        <h2>The Sentiment of</h2> '{{ text1 }}' <h2>is {{ final }} </h2> <h2>is {{ last }} </h2>
        {% else %}
        <p></p>
        {% endif %}
    </div>
</body>

Answer 1

In your predict_function function you're not returning any value just printing whether it is positive or not. Try replacing those print statements at the end with return statements.