testing ajax for async prediction and small changes in files

Author: g-paras

.gitignore

@@ -1,3 +1,5 @@
 __pycache__/
 data.sqlite
-info.sqlite3
+info.sqlite3
+test-101.txt
+test-150.txt

README.md

@@ -29,4 +29,4 @@ It might take few seconds to load please give it a try
     <li>app.py is the flask application file</li>
     <li>model_nltk.py contains the source code for the Naive Bayes classifer which has been used in the production</li>
     <li>model_keras.py is another source code for model usign keras but we haven't used this in production because of its accuracy.</li>
-</ul>
+</ul>

app.py

@@ -1,12 +1,14 @@
 # importing libraries for flask, database, model
-from flask import Flask, render_template, request, jsonify, redirect, url_for
-from flask_sqlalchemy import SQLAlchemy
-from model_nltk import predict_sentiment
+import os
 from datetime import datetime
-from textblob import TextBlob
 from pickle import load
+
 import pytz
-import os
+from flask import Flask, jsonify, redirect, render_template, request, url_for
+from flask_sqlalchemy import SQLAlchemy
+from textblob import TextBlob
+
+from model_nltk import predict_sentiment
 
 app = Flask(__name__, template_folder='templates')
 
@@ -20,6 +22,7 @@
 
 db = SQLAlchemy(app)
 
+# since the app is hosted on heroku so this line of code is to change the timezone
 IST = pytz.timezone('Asia/Kolkata')
 
 
@@ -32,6 +35,7 @@ class New_Data(db.Model):
     Id = db.Column(db.Integer, primary_key=True)
     Text = db.Column(db.Text)
     Sentiment = db.Column(db.String(20))
+    # .now(IST).strftime('%Y-%m-%d %H:%M:%S'))
     Date = db.Column(db.DateTime, default=datetime.now(IST).strftime('%Y-%m-%d %H:%M:%S'))
 
     def __init__(self, Text, Sentiment):
@@ -45,6 +49,7 @@ def __init__(self, Text, Sentiment):
 
 
 def allowed_file(filename):
+    '''Checking file extension i.e. text file or not'''
     return '.' in filename and filename.split('.')[1] == 'txt'
 
 
@@ -66,6 +71,7 @@ def home():
         else:
             pass
 
+        # creating an instance of the data table for the database and commiting the changes
         usr_data = New_Data(sentence, sentiment.split()[0])
         db.session.add(usr_data)
         db.session.commit()
@@ -89,21 +95,31 @@ def contact():
 
 
 # route for fastapi
-@app.route('/fast-api/', defaults={'sentence' : 'Great'})
+# setting default value for the api
+@app.route('/fast-api/', defaults={'sentence': 'Great'})
 @app.route('/fast-api/<sentence>')
 def fast_api(sentence):
     sentiment = predict_sentiment(sentence, classifier)
 
     return jsonify({'sentence': sentence, 'sentiment': sentiment})
 
 
+# setting post method for the api
+@app.route('/fastapi', methods=['POST'])
+def fastapi():
+    text = request.form['text']
+    return jsonify({'sentiment' : 'Positive' if TextBlob(text).sentiment.polarity > 0 else 'Negative'})
+
+
 # route for uploading and saving temperary file
 @app.route('/upload')
 def upload():
     mssg = request.args.get('msg')
+    # if the uploaded file is not a text file
     if mssg == "ntxt":
         mssg = "Kindly Upload a text file"
 
+    # if the uploaded textfile is not readable
     elif mssg == "incrt":
         mssg = "Upload file of correct format"
 
@@ -122,18 +138,21 @@ def canvas():
         subject = []
         polar = []
         file = request.files['file']
-        
+
+        # if the file is correct and readable then save it
         if allowed_file(file.filename):
             file.save(file.filename)
 
             try:
+                # open file, read the content perform the analysis and then return the template with the values
                 with open(file.filename) as fl:
                     content = fl.read().split('\n')
-                    for t in content:
+                    for line in content:
                         # t = fl.readline()
-                        a = TextBlob(t).sentiment.polarity*100
-                        subject.append(TextBlob(t).sentiment.subjectivity*100)
+                        a = TextBlob(line).sentiment.polarity*100
                         polar.append(a)
+                        subject.append(
+                            TextBlob(line).sentiment.subjectivity*100)
                         if a > 0:
                             pos += 1
                         else:
@@ -191,6 +210,11 @@ def show():
     return redirect(url_for('login', er="lnf"))
 
 
+@app.route('/test')
+def test():
+    return render_template('index.html')
+
+
 @app.errorhandler(404)
 def error404(error):
     return render_template("error404.html"), 404

fastapi.py

@@ -19,4 +19,4 @@
             neg += 1
     total = len(file.readlines)
 
-print("Out of {total} reviews, {pos} are Positive and {neg} are Negative reviews")
+print(f"Out of {total} reviews, {pos} are Positive and {neg} are Negative reviews")

model_keras.py

@@ -1,12 +1,12 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from keras.models import load_model
+from tensorflow.python.keras.callbacks import LambdaCallback
 from tensorflow.python.keras.datasets import imdb
+from tensorflow.python.keras.layers import (Dense, Embedding,
+                                            GlobalAveragePooling1D)
 from tensorflow.python.keras.models import Sequential
 from tensorflow.python.keras.preprocessing.sequence import pad_sequences
-from tensorflow.python.keras.layers import Embedding, Dense, GlobalAveragePooling1D
-from tensorflow.python.keras.callbacks import LambdaCallback
-from keras.models import load_model
-import matplotlib.pyplot as plt
-import numpy as np
-
 
 # This model was used in early stage of this project and used as a reference, learned from coursesa project
 
@@ -111,14 +111,15 @@ def show_lengths():
 
 decode(x_test[1])
 
+# these are samples for manually testing examples
 text = "i was working on that project and i find it quiet amazing and funny overall the experience was good and satisfying"
-text = "working on this was a worst experience for me i hate this very much and wish no one should get though this"
-text = "you are a waste"
+text1 = "working on this was a worst experience for me i hate this very much and wish no one should get though this"
+text2 = "you are a waste"
 t_list = []
 
 for i in text.split():
     t_list.append(word_index[i])
-    
+
 print(t_list)
 
 prediction = md.predict(np.expand_dims(t_list, axis=0))

model_nltk.py

@@ -1,13 +1,14 @@
 # importing libraries for persorming the sentiment analysis, cleaning data, training and saving model
-from nltk.tag import pos_tag
-from nltk.tokenize import word_tokenize
-from nltk.stem.wordnet import WordNetLemmatizer
-from nltk.corpus import twitter_samples, stopwords
-from nltk import FreqDist, classify, NaiveBayesClassifier
 import pickle
+import random
 import re
 import string
-import random
+
+from nltk import FreqDist, NaiveBayesClassifier, classify
+from nltk.corpus import stopwords, twitter_samples
+from nltk.stem.wordnet import WordNetLemmatizer
+from nltk.tag import pos_tag
+from nltk.tokenize import word_tokenize
 
 
 def remove_noise(tweet_tokens, stop_words=()):
@@ -35,6 +36,7 @@ def remove_noise(tweet_tokens, stop_words=()):
 
 
 def get_all_words(cleaned_tokens_list):
+    '''It acts as an generator for the tokens'''
     for tokens in cleaned_tokens_list:
         for token in tokens:
             yield token
@@ -62,11 +64,13 @@ def save_model():
 
 if __name__ == "__main__":
 
+    # loading dataset for model trainig
     positive_tweets = twitter_samples.strings('positive_tweets.json')
     negative_tweets = twitter_samples.strings('negative_tweets.json')
     text = twitter_samples.strings('tweets.20150430-223406.json')
     tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
 
+    # saving the stopwords from the nltk into a variable
     stop_words = stopwords.words('english')
 
     positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')

nltk.txt

@@ -1,4 +1,4 @@
 punkt
 stopwords
 wordnet
-averaged_perceptron_tagger
+averaged_perceptron_tagger

requirements.txt

@@ -4,4 +4,4 @@ gunicorn==20.0.4
 Flask-SQLAlchemy==2.4.4
 psycopg2==2.8.6
 textblob==0.15.3
-pytz==2020.1
+pytz==2020.1

static/js/app.js

@@ -0,0 +1,15 @@
+// this is same as the script.js but i am using a different approach
+$(document).ready(function () {
+
+    $("#text").keyup(function () {
+
+        var text = $("#text").val();
+        $.post('/fastapi', {
+            text : text
+        }, function (data, status) {
+            $("#ans").html(data.sentiment);
+        });
+
+    });
+
+});

templates/index.html

@@ -34,6 +34,7 @@ <h1>Sentiment&nbspAnalysis</h1>
     <h3>{{text}}</h3>
     <br>
     <h3>{{sentiment}}</h3>
+    <h3 id="ans"></h3>
     <br>
     <div class="main">
         <p>Note:</p>