import gradio as gr import streamlit as st import requests import time from transformers import pipeline import os # Set the page configuration st.set_page_config( page_title="Hate Speech Detection", page_icon="📖", #":bar_chart:" layout='centered' ) #title = r"$\textsf{\small Hate Speech Detection}$" #st.title(title) #st.write("In this HuggingFace space you will be able to use our Hate Speech Detection model built at [VERİM - Center of Excellence in Data Analytics - Sabanci University](https://github.com/verimsu).") # Turkish sentiment_pipeline_tr = pipeline(task = "text-classification", model = "SoDehghan/BERTurk-hate-speech") # "gritli/bert-sentiment-analyses-imdb" label_dict = {'LABEL_1': 'Hate ❌', 'LABEL_0': 'Non-hate ✅'} def hsd_turkish(text_in): result_tr = sentiment_pipeline_tr(tr_input) sentiment_tr = result_tr[0]["label"] label_dict = {'LABEL_1': 'Hate ❌', 'LABEL_0': 'Non-hate ✅'} #🚫 sentiment_tr = label_dict[sentiment_tr] return sentiment_tr iface = gr.Interface(fn=[hsd_turkish], inputs=gr.inputs.Textbox(lines=2, placeholder=None, label="Enter text here:"), outputs=['text'], # a list should match the number of values returned by fn to have one input and 2 putputs. #description = "This App translates text from Danish to the English language.", title = "HSD in Turkish", theme = "peach") iface.launch(share=False, enable_queue=True) st.sidebar.title("Hate Speech Detection") #st.sidebar.write("In this HuggingFace space you can use Hate Speech Detection model built at [VERİM - Center of Excellence in Data Analytics - Sabanci University](https://github.com/verimsu).") st.sidebar.write('This tool is developed in the context of the EU project "Utilizing Digital Technology for Social Cohesion, Positive Messaging and Peace by Boosting Collaboration, Exchange and Solidarity" (EuropeAid/170389/DD/ACT/Multi) by [Sabanci University Center of Excellence in Data Analytics](https://github.com/verimsu).')