import streamlit as st
from transformers import pipeline
import numpy as np

print ("Load model...")

# Load the pre-trained emotion classification pipeline
model_name = "bhadresh-savani/distilbert-base-uncased-emotion"
emotion_classifier = pipeline("text-classification", model=model_name)

# Title and Description
st.title("Emotion Classifier")
st.write("""write down how your day went or what your mood is.""")
st.write("""On this space used model "bhadresh-savani/distilbert-base-uncased-emotion".
""")

# Input text box
input_text = st.text_area("Enter text to analyze emotions:", "")

if st.button("Classify Emotion"):
    if input_text.strip() == "":
        st.write("Please enter some text to classify.")
    else:
        # Get classification results
        results = emotion_classifier(input_text, top_k=None)

        # Extract scores and normalize to sum to 1
        scores = np.array([result["score"] for result in results])
        normalized_scores = scores / scores.sum()

        # Display normalized results
        st.subheader("Emotions:")
        for i, result in enumerate(results):
            st.write(f"**{result['label']}**: {normalized_scores[i]:.4f}")