app.py

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()