app.py

import gradio as gr
from transformers import pipeline

# Load the model and tokenizer from the same repository
classifier = pipeline(
    "text-classification",
    model="YonasMersha/fine-tuned-distilbert-emotion",
    tokenizer="YonasMersha/fine-tuned-distilbert-emotion"
)

# Define the prediction function
def classify(text):
    result = classifier(text)[0]
    return result["label"]

# Create the Gradio interface
iface = gr.Interface(
    fn=classify,
    inputs=gr.Textbox(label="Enter a sentence", placeholder="e.g., I love this!"),
    outputs=gr.Label(label="Predicted Emotion"),
    title="Emotion Classifier",
    description="Enter a sentence to classify its emotion (e.g., sadness, joy, love, anger, fear, surprise)."
)

# Launch the interface
if __name__ == "__main__":
    iface.launch()