app.py

import gradio as gr
from transformers import pipeline

model_checkpoint = "MuntasirHossain/RoBERTa-base-finetuned-emotion"
emotion_model = pipeline("text-classification", model=model_checkpoint)

def classify_emotion(text):
    label = emotion_model(text)[0]["label"]
    return label

description = "This AI model is trained to classify texts expressing human emotion into different categories."
title = "Texts Expressing Emotion"
examples = [["He is very happy today", "Free Palestine"]]

theme = {
    "container": {
        "background-color": "#007bff",
        "color": "#fff",
        "padding": "20px",
    },
    "textbox": {
        "background-color": "#fff",
        "border-radius": "5px",
        "padding": "10px",
        "margin-bottom": "10px",
    },
    "button": {
        "background-color": "#007bff",
        "color": "#fff",
        "padding": "10px",
        "border-radius": "5px",
        "cursor": "pointer",
    },
    "label": {
        "color": "#fff",
    },
}

gr.Interface(
    fn=classify_emotion,
    inputs="textbox",
    outputs="text",
    title=title,
    theme=theme,
    description=description,
    examples=examples,
).launch()