app.py

from transformers import pipeline
import gradio as gr

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

def classify_and_visualize_emotion(text):
    # Get the predicted emotion label
    emotion_label = emotion_model(text)[0]["label"]
    
    # Map emotion labels to corresponding emojis
    emoji_mapping = {
        "joy": "😄",
        "sadness": "😢",
        "love": "❤️",
        "anger": "😡",
        "fear": "😱",
        "surprise": "😲",
    }
    
    # Get the corresponding emoji for the predicted emotion
    predicted_emoji = emoji_mapping.get(emotion_label, "🤔")

    return f"{emotion_label.capitalize()} {predicted_emoji}"

# Define interface title and description
title = "Emotion Classifier with Emoji Visualization"
description = "This AI model classifies text expressions into six emotions: joy, sadness, love, anger, fear, and surprise. The result is visualized with emojis."

# Set up Gradio Interface with HTML visualization
iface = gr.Interface(
    fn=classify_and_visualize_emotion,
    inputs="textbox",
    outputs=gr.Html(),
    title=title,
    description=description,
)

iface.launch()