app.py

#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 = ["Explicit", "Not_Explicit"]

#Defining the models and tokenuzer
model_name = 'valurank/finetuned-distilbert-explicit_content_detection'
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='Explicit Content Detection')
                                      
#Launching the gradio app
if __name__ == '__main__':
  demo.launch(debug=True)