app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

model_id = "xlm-roberta-base"
peft_model_id = "rasyosef/xlm-roberta-base-lora-amharic-news-classification"

categories = ['ሀገር አቀፍ ዜና', 'መዝናኛ', 'ስፖርት', 'ቢዝነስ', 'ዓለም አቀፍ ዜና', 'ፖለቲካ']
id2label = {i: lbl for i, lbl in enumerate(categories)}
label2id = {lbl: i for i, lbl in enumerate(categories)}

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForSequenceClassification.from_pretrained(
    model_id,
    num_labels=len(categories), # 6
    id2label=id2label,
    label2id=label2id
)

model.load_adapter(peft_model_id)

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

def predict(text):
    return classifier([text])[0]


with gr.Blocks() as demo:
    gr.Markdown(
        """
        # Amharic News Article Classification 
        This RoBERTa model (xlm-roberta-base) was finetuned using Low-Rank Adaptation (LoRA) that classifies amharic news articles into one of the following 6 categories.
        - ሀገር አቀፍ ዜና (Local News)
        - መዝናኛ (Entertainment)
        - ስፖርት (Sports)
        - ቢዝነስ (Business)
        - ዓለም አቀፍ ዜና (International News)
        - ፖለቲካ (Politics)
        """
    )

    with gr.Row():
        with gr.Column():
            input = gr.Textbox(label="Amharic text", placeholder="Enter text here", lines=3)
            classify_btn = gr.Button(value="Classify")
        with gr.Column():
            output = gr.Textbox(label="Predicted class")

    classify_btn.click(predict, inputs=input, outputs=output)
    examples = gr.Examples(
        examples=[
            "ኢትዮጵያ ፕሪምየር ሊግ 6ኛ ሳምንት የእሁድ ጨዋታዎች ቅድመ ዳሰሳ",
            "በአፄ ቴዎድሮስ የንግስና ቦታ ደረስጌ ማሪያም ተጀምሮ የቆመው የሙዚየሙ ግንባታ ተጠናቀቆ ስራ እንዲጀምር ነዋሪዎች ጠይቀዋል።"
        ],
        inputs=[input],
    )

demo.launch()