app.py

import streamlit as st
from transformers import pipeline

# Initialize the emotion detection pipeline
emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")

# Set up the title and description
st.title("Emotion Detector in Text")
st.write("Type a text below, and the model will predict the emotion behind it.")

# Input field for text
user_input = st.text_area("Enter your text here:")

if st.button("Detect Emotion"):
    if user_input.strip():
        # Get emotion predictions
        emotion = emotion_model(user_input)[0]
        label = emotion['label']
        score = emotion['score']
        
        # Display emotion and confidence score
        st.write(f"**Detected Emotion:** {label}")
        st.write(f"**Confidence Score:** {score:.2f}")
    else:
        st.error("Please enter some text.")

# Footer
st.markdown("Developed by [Your Name]. Deployed on Hugging Face Spaces.")