app.py

import streamlit as st
from transformers import pipeline

# Load the text classification model pipeline
classifier = pipeline("text-classification", model='djohari/EmployeeEmotionAnalysis', return_all_scores=True)

# Streamlit application title
st.title("Emotion Classification for Employee Reviews")
st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
st.write("Please note: this is a model for Text Classification and can only work with input text")

# Text input for user to enter the text to classify
text = st.text_area("Add your Employee Reviews in the box to classify", "")

# Perform text classification when the user clicks the "Classify" button
if st.button("Classify Emotion"):
# Perform text classification on the input text
    results = classifier(text)[0]

# Display the classification result
    max_score = float('-inf')
    max_label = ''
    for result in results:
        if result['score'] > max_score:
            max_score = result['score']
            max_label = result['label']
            
    st.write("Text:", text)
    st.write("Label:", max_label)
    st.write("Score:", max_score)