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application.py
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application.py
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import os
from flask import Flask, request, jsonify, render_template
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import pickle
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import re
nltk.download('stopwords', download_dir='/tmp')
nltk.download('punkt', download_dir='/tmp')
TEMPLATE_DIR = os.path.abspath('../templates')
STATIC_DIR = os.path.abspath('../static')
application = Flask(__name__)
app = application
# Import the vectorizer and trained model
# vectorizer = pickle.load(open("models/vectorizer.pkl", "rb"))
trained_model = pickle.load(open("models/modelPred.pkl", "rb"))
stop_words = set(stopwords.words('english'))
def remove_stop_words(text):
tokens = word_tokenize(text)
filt_text = [word for word in tokens if word.lower() not in stop_words]
return ' '.join (filt_text)
def remove_special_char(text):
pattern = r'[^\w\s]|[\n\t\r]'
return re.sub(pattern, '', text)
@app.route("/", methods=['GET', 'POST'])
def index():
if (request.method == 'POST'):
data = [str(request.form.get('inputText'))]
df = pd.DataFrame({'text': data})
df['text'] = df['text'].apply(remove_special_char)
df['text'] = df['text'].apply(remove_stop_words)
val = trained_model.predict(df['text'])
val = "The entered text is generated by AI" if val[0] == 1 else "The entered text is written by Human"
return render_template('index.html', results=val, textContent=data[0])
else:
return render_template("index.html")
if __name__ == "__main__":
app.run(host="0.0.0.0")