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#importing the necessary libraries
import gradio as gr
import numpy as np
import pandas as pd
import re
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
#Defining the labels of the models
labels = ['business', 'science','health', 'world', 'sport', 'politics','entertainment', 'tech']
#Defining the models and tokenuzer
model_name = 'valurank/finetuned-distilbert-news-article-categorization'
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
#Reading in the text file
def read_in_text(url):
with open(url, 'r') as file:
article = file.read()
return article
def clean_text(url):
text = url
text = text.encode("ascii", errors="ignore").decode(
"ascii"
) # remove non-ascii, Chinese characters
text = re.sub(r"\n", " ", text)
text = re.sub(r"\n\n", " ", text)
text = re.sub(r"\t", " ", text)
text = text.strip(" ")
text = re.sub(
" +", " ", text
).strip() # get rid of multiple spaces and replace with a single
text = re.sub(r'Date\s\d{1,2}\/\d{1,2}\/\d{4}', '', text) #remove date
text = re.sub(r'\d{1,2}:\d{2}\s[A-Z]+\s[A-Z]+', '', text) #remove time
return text
#Defining a function to get the category of the news article
def get_category(file):
text = clean_text(file)
input_tensor = tokenizer.encode(text, return_tensors='pt', truncation=True)
logits = model(input_tensor).logits
softmax = torch.nn.Softmax(dim=1)
probs = softmax(logits)[0]
probs = probs.cpu().detach().numpy()
max_index = np.argmax(probs)
emotion = labels[max_index]
return emotion
#Creating the interface for the radio app
demo = gr.Interface(get_category, inputs=gr.inputs.Textbox(label='Drop your articles here'),
outputs = 'text',
title='News Article Categorization')
#Launching the gradio app
if __name__ == '__main__':
demo.launch(debug=True) |