| import gradio as gr | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline | |
| import csv | |
| MODEL_URL = "https://huggingface.co/dsfsi/PuoBERTa-News" | |
| WEBSITE_URL = "https://www.kodiks.com/ai_solutions.html" | |
| tokenizer = AutoTokenizer.from_pretrained("dsfsi/PuoBERTa-News") | |
| model = AutoModelForSequenceClassification.from_pretrained("dsfsi/PuoBERTa-News") | |
| categories = { | |
| "arts_culture_entertainment_and_media": "Botsweretshi, setso, boitapoloso le bobegakgang", | |
| "crime_law_and_justice": "Bosenyi, molao le bosiamisi", | |
| "disaster_accident_and_emergency_incident": "Masetlapelo, kotsi le tiragalo ya maemo a tshoganyetso", | |
| "economy_business_and_finance": "Ikonomi, tsa kgwebo le tsa ditšhelete", | |
| "education": "Thuto", | |
| "environment": "Tikologo", | |
| "health": "Boitekanelo", | |
| "politics": "Dipolotiki", | |
| "religion_and_belief": "Bodumedi le tumelo", | |
| "society": "Setšhaba" | |
| } | |
| def prediction(news): | |
| classifier = pipeline("text-classification", tokenizer=tokenizer, model=model, return_all_scores=True) | |
| preds = classifier(news) | |
| preds_dict = {categories.get(pred['label'], pred['label']): round(pred['score'], 4) for pred in preds[0]} | |
| return preds_dict | |
| def file_prediction(file): | |
| news_list = [] | |
| if file.name.endswith('.csv'): | |
| file.seek(0) | |
| reader = csv.reader(file.read().decode('utf-8').splitlines()) | |
| news_list = [row[0] for row in reader if row] | |
| else: | |
| file.seek(0) | |
| file_content = file.read().decode('utf-8') | |
| news_list = file_content.splitlines() | |
| results = [] | |
| for news in news_list: | |
| if news.strip(): | |
| pred = prediction(news) | |
| results.append([news, pred]) | |
| return results | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| pass | |
| with gr.Column(scale=4, min_width=1000): | |
| gr.Image("logo_transparent_small.png", elem_id="logo", show_label=False, width=500) | |
| gr.Markdown(""" | |
| <h1 style='text-align: center;'>Setswana News Classification</h1> | |
| <p style='text-align: center;'>This space provides a classification service for news in Setswana.</p> | |
| """) | |
| with gr.Column(scale=1): | |
| pass | |
| with gr.Tabs(): | |
| with gr.Tab("Text Input"): | |
| gr.Markdown(f""" | |
| Enter Setswana news article to see the category of the news. <br> | |
| For this classification, the <a href='{MODEL_URL}' target='_blank'>PuoBERTa-News</a> model was used. | |
| """) | |
| inp_text = gr.Textbox(lines=10, label="Paste some Setswana news here") | |
| output_label = gr.Label(num_top_classes=5, label="News categories probabilities") | |
| translate_button = gr.Button("Classify") | |
| translate_button.click(prediction, inputs=inp_text, outputs=output_label) | |
| with gr.Tab("File Upload"): | |
| gr.Markdown(""" | |
| Upload a text or CSV file with Setswana news articles. The first column in the CSV should contain the news text. | |
| """) | |
| file_input = gr.File(label="Upload text or CSV file") | |
| file_output = gr.Dataframe(headers=["News Text", "Category Predictions"], label="Predictions from file") | |
| file_button = gr.Button("Classify File") | |
| file_button.click(file_prediction, inputs=file_input, outputs=file_output) | |
| gr.Markdown(""" | |
| <div style='text-align: center;'> | |
| <a href='https://github.com/dsfsi/PuoBERTa-News' target='_blank'>GitHub</a> | | |
| <a href='https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/viewform' target='_blank'>Feedback Form</a> | |
| </div> | |
| """) | |
| with gr.Accordion("More Information", open=False): | |
| gr.Markdown(""" | |
| <h4 style="text-align: center;">Authors</h4> | |
| <div style='text-align: center;'> | |
| Vukosi Marivate, Moseli Mots'Oehli, Valencia Wagner, Richard Lastrucci, Isheanesu Dzingirai | |
| </div> | |
| """) | |
| gr.Markdown(""" | |
| <h4 style="text-align: center;">Citation</h4> | |
| <pre style="text-align: left; white-space: pre-wrap;"> | |
| @inproceedings{marivate2023puoberta, | |
| title = {PuoBERTa: Training and evaluation of a curated language model for Setswana}, | |
| author = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai}, | |
| year = {2023}, | |
| booktitle= {Artificial Intelligence Research. SACAIR 2023. Communications in Computer and Information Science}, | |
| url= {https://link.springer.com/chapter/10.1007/978-3-031-49002-6_17}, | |
| keywords = {NLP}, | |
| preprint_url = {https://arxiv.org/abs/2310.09141}, | |
| dataset_url = {https://github.com/dsfsi/PuoBERTa}, | |
| software_url = {https://huggingface.co/dsfsi/PuoBERTa} | |
| } | |
| </pre> | |
| """) | |
| gr.Markdown(""" | |
| <h4 style="text-align: center;">DOI</h4> | |
| <div style='text-align: center;'> | |
| DOI: <a href="https://doi.org/10.1007/978-3-031-49002-6_17" target="_blank">10.1007/978-3-031-49002-6_17</a> | |
| </div> | |
| """) | |
| demo.launch() | |