app.py

import streamlit as st
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification

#tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
#model = AutoModelForSequenceClassification.from_pretrained("Howosn/Sentiment_Model")

# Load the summarization & translation model pipeline
tran_sum_pipe = pipeline("translation", model='utrobinmv/t5_summary_en_ru_zh_base_2048',return_all_scores=True)
sentiment_pipeline = pipeline("text-classification", model='Howosn/Sentiment_Model',return_all_scores=True)
tokenizer = T5Tokenizer.from_pretrained('utrobinmv/t5_summary_en_ru_zh_base_2048')

# Streamlit application title
st.title("Emotion analysis")
st.write("Turn Your Input Into Sentiment Score")

# Text input for the user to enter the text to analyze
text = st.text_area("Enter the text", "")

# Perform analysis result when the user clicks the "Analyse" button
if st.button("Analyse"):
    # Perform text classification on the input text
    trans_sum = tran_sum_pipe(text)[0]
    results = sentiment_pipeline(trans_sum)[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)