app.py

import gradio as gr
from transformers import pipeline

# Load the Hugging Face pipeline
classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")

def classify_emotion(text):
    # Make predictions using the Hugging Face pipeline
    predictions = classifier(text)
    return {item["label"]: item["score"] for item in predictions}

# Create a custom Gradio interface with title, description, and examples
gr.Interface(
    fn=classify_emotion,
    inputs=gr.Textbox(
        placeholder="Enter text to analyze...", 
        label="Input Text",
        lines=4
    ),
    outputs=gr.JSON(),  # Display results in JSON format
    title="Emotion Detection with DistilBERT",
    description="This app uses the DistilBERT model fine-tuned for emotion detection. Enter a piece of text to analyze its emotional content!",
    examples=[
        "I am so happy to see you!",
        "I'm really angry about what happened.",
        "The sunset was absolutely beautiful today.",
        "I'm worried about the upcoming exam.",
        "Fear is the mind-killer. I will face my fear."
    ]
).launch()