app.py

import gradio as gr
from sentiwordnet_calculator import SentimentPipeline

pipe = SentimentPipeline("Tanor/SRGPTSENTPOS4", "Tanor/SRGPTSENTNEG4")


def calculate(text):
    result = pipe(text)
    # Visual representation
    visual = result
    # Numerical representation
    numerical = {key: round(value, 2) for key, value in result.items()}
    # Create a formatted string
    numerical_str = ", ".join(f"{key}: {value}" for key, value in numerical.items())
    return visual, numerical_str

iface = gr.Interface(
    fn=calculate, 
    inputs=gr.inputs.Textbox(lines=5, placeholder="Enter your text here..."), 
    outputs=[gr.outputs.Label(num_top_classes=3), "text"],
    title="Sentiment Analysis for Serbian",
    description="""
    This tool performs sentiment analysis on the input text using a model trained on Serbian dictionary definitions. 
    The pretrained model [sr-gpt2-large model by Mihailo Škorić](https://huggingface.co/JeRTeh/sr-gpt2-large), 
    was fine-tuned on selected definitions from the Serbian WordNet. Please limit the input to 300 tokens. 
    The outputs represent the Positive (POS), Negative (NEG), and Objective (OBJ) sentiment scores.
    """,
    examples=[
        ["osoba koja ne prihvata nove ideje"],
        ["intenzivna ojađenost"],
        ["uopštenih osećanja tuge"],
        ["žalostan zbog gubitka ili uskraćenosti"],
        ["činjenje dobra; osećaj dobrotvornosti"],
        ["Jako pozitivno osećanje poštovanja i privrežen..."],
        ["usrećiti ili zadovoljiti"],
        ["Korisna ili vredna osobina"],
        ["uzrokovati strah"],
        ["svojstvo onoga kome nedostaje živost"],
        ["koji čini živahnim i veselim"],
        ["Biti zdrav, osećati se dobro."],

    ]

)

iface.launch()