app.py

import gradio as gr
from transformers import pipeline

# Load the model
model_name = "cirimus/modernbert-base-go-emotions"
classifier = pipeline("text-classification", model=model_name, top_k=None)

def classify_text(text):
    predictions = classifier(text)
    return {pred["label"]: pred["score"] for pred in predictions[0]}

# Create the Gradio interface
interface = gr.Interface(
    fn=classify_text,
    inputs=gr.Textbox(lines=2, placeholder="Enter text to analyze emotions..."),
    outputs=gr.Label(num_top_classes=5),
    title="Emotion Classifier",
    description="Enter a sentence to see its associated emotions and confidence scores.",
)

# Launch the app
interface.launch()