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| import gradio as gr | |
| import spaces | |
| from transformers import pipeline | |
| import torch | |
| ımport os | |
| token = os.getenv('HF_AUTH_TOKEN') | |
| binary_example = [["Yahudi terörüne karşı protestolar kararlılıkla devam ediyor."]] | |
| category_example = [["Ermeni zulmü sırasında hayatını kaybeden kadınlar anısına dikilen anıt ziyarete açıldı."]] | |
| target_example = [["Dün 5 bin suriyeli enik doğmuştur zaten Türkiyede aq 5 bin suriyelinin gitmesi çok çok az"]] | |
| DESCRIPTION = """" | |
| ## Hate Speech Detection in Turkish News | |
| """ | |
| CITATION = """" | |
| """ | |
| def binary_classification(input): | |
| model = pipeline(model='gokceuludogan/berturk_tr_hate_print_w0.1', token=token) | |
| return model(input)[0] | |
| def category_classification(): | |
| model = pipeline(model='gokceuludogan/berturk/tr_hateprint_cat_w0.1_b128', token=token) | |
| return model(input)[0] | |
| def target_detection(): | |
| model = pipeline(model='gokceuludogan/turna_generation_tr_hateprint_target', token=token) | |
| return model(input)[0] | |
| with gr.Blocks(theme="abidlabs/Lime") as demo: | |
| #gr.Markdown("# TURNA") | |
| #gr.Image("images/turna-logo.png", width=100, show_label=False, show_download_button=False, show_share_button=False) | |
| with gr.Tab("TRHatePrint"): | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Tab("Binary Classification"): | |
| gr.Markdown("Enter text to analyse hatefulness and pick the model.") | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| # sentiment_choice = gr.Radio(choices = ["turna_", "berturk"], label ="Model", value="turna_") | |
| sentiment_input = gr.Textbox(label="Input") | |
| sentiment_submit = gr.Button() | |
| sentiment_output = gr.Textbox(label="Output") | |
| sentiment_submit.click(binary_classification, inputs=[sentiment_input], outputs=sentiment_output) | |
| sentiment_examples = gr.Examples(examples = binary_example, inputs = [sentiment_input], outputs=sentiment_output, fn=binary_classification) | |
| with gr.Tab("Hate Speech Categorization"): | |
| gr.Markdown("Enter a hateful text to categorize or try the example.") | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| text_input = gr.Textbox(label="Input") | |
| text_submit = gr.Button() | |
| text_output = gr.Textbox(label="Output") | |
| text_submit.click(category_classification, inputs=[text_input], outputs=text_output) | |
| text_examples = gr.Examples(examples = category_example,inputs=[text_input], outputs=text_output, fn=category_classification) | |
| with gr.Tab("Target Detection"): | |
| gr.Markdown("Enter text to detect targets ") | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| nli_first_input = gr.Textbox(label="Input") | |
| nli_submit = gr.Button() | |
| nli_output = gr.Textbox(label="Output") | |
| nli_submit.click(target_detection, inputs=[nli_first_input], outputs=nli_output) | |
| nli_examples = gr.Examples(examples = target_example, inputs = [nli_first_input], outputs=nli_output, fn=target_example) | |
| gr.Markdown(CITATION) | |
| demo.launch() |