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import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
import pandas as pd
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):
clasifer = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model, return_all_scores=True)
preds = clasifer(news)
preds_dict = {categories.get(pred['label'], pred['label']): pred['score'] for pred in preds[0]}
return preds_dict
def file_prediction(file):
# Load the file (CSV or text)
if file.name.endswith('.csv'):
df = pd.read_csv(file.name)
news_list = df.iloc[:, 0].tolist()
else:
news_list = [file.read().decode('utf-8')]
results = []
for news in news_list:
results.append(prediction(news))
return results
gradio_ui = gr.Interface(
fn=prediction,
title="Setswana News Classification",
description=f"Enter Setswana news article to see the category of the news.\n For this classification, the {MODEL_URL} model was used.",
inputs=gr.Textbox(lines=10, label="Paste some Setswana news here"),
outputs=gr.Label(num_top_classes=5, label="News categories probabilities"),
theme="huggingface", # Apply the dark mode theme
article="<p style='text-align: center'>For our other AI works: <a href='https://www.kodiks.com/ai_solutions.html' target='_blank'>https://www.kodiks.com/ai_solutions.html</a> | <a href='https://twitter.com/KodiksBilisim' target='_blank'>Contact us</a></p>",
)
gradio_file_ui = gr.Interface(
fn=file_prediction,
title="Upload File for Setswana News Classification",
description=f"Upload a text or CSV file with Setswana news articles. The first column in the CSV should contain the news text.",
inputs=gr.File(label="Upload text or CSV file"),
outputs=gr.Dataframe(headers=["News Text", "Category Predictions"], label="Predictions from file"),
theme="huggingface" # Apply the dark mode theme
)
gradio_combined_ui = gr.TabbedInterface([gradio_ui, gradio_file_ui], ["Text Input", "File Upload"])
gradio_combined_ui.launch()
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