Text classification model

[shibiyamvp]

Build a text classification model to categorize news articles into one of four categories: World, Sports, Business, and Sci/Tech.

Create an API to check the Responses. The API will take input as a news article and response will be the category. [test.csv.zip](https://github.com/user-attachments/files/16609058/test.csv.zip)

[Priyanka-3008]

from flask import Flask, request,jsonify import joblib import string import re import sklearn as sk from sklearn import * import nltk from nltk.stem import WordNetLemmatizer from nltk.corpus import stopwords from nltk.tokenize import word_tokenize

def preprocess_text(text): def remove_url(text): url_pattern=re.compile(r'https?://\S+|www.\S+|.\w+', flags=re.IGNORECASE) return url_pattern.sub(r'',text) def tolower(text): return text.lower() def striphtml(text): p=re.compile(r'<.*?>|.') return p.sub('',text)

def remove_punctuation(text):
    punctuation_free = "".join([i for i in text if i not in string.punctuation])
    return punctuation_free
def remove_digits(text):
    result = ''.join([i for i in text if not i.isdigit()])
    return result
def token_wordremoval(text):
    stop_words=set(stopwords.words('english'))
    word_tokens=word_tokenize(text)
    filtered_text=[word for word in word_tokens if word not in stop_words]
    return filtered_text
def lemmatize_text(tokens):
    WNL = WordNetLemmatizer()
    return [WNL.lemmatize(word) for word in tokens]
text=remove_url(text)
text=tolower(text)
text=striphtml(text)
text=remove_punctuation(text)
text=remove_digits(text)
tokens=token_wordremoval(text)
lemmatized_tokens=lemmatize_text(tokens)
return ' '.join(lemmatized_tokens)

app=Flask(name)

model=joblib.load('/Users/mvp/Desktop/taskflask/MNBClassifier (1).pkl') vectoriser=joblib.load('/Users/mvp/Desktop/taskflask/tfidfVector (1).pkl') @app.route('/predict',methods=['POST']) def predict(): data=request.json.get('text') if not data: return jsonify({'Error':'No text provided'}),400 processed_text=preprocess_text(data) vectorised_text=vectoriser.transform([processed_text]) prediction=model.predict(vectorised_text) return jsonify({'Category':prediction[0]})

if name=='main': app.run(debug=True)