app.py

import streamlit as st

import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from transformers import XLMRobertaTokenizer, AutoModelForSequenceClassification, pipeline

# Load tokenizer and model
tokenizer = XLMRobertaTokenizer.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-xlm-roberta-base-sentiment")

# Define pipeline explicitly
nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)

# Define function to analyze sentiment
def analyze_sentiment(text):
    result = nlp(text)
    return result[0]['label'], result[0]['score']

# Streamlit UI
st.set_page_config(page_title="Sentiment Analysis", layout="wide")
col1, col2 = st.columns([6, 1])  # Divide the screen into two columns



with col2:  # Right-aligned column for the logo
    st.image("https://huggingface.co/spaces/orYx-models/Leadership-sentiment-analyzer/resolve/main/oryx_logo%20(2).png", width=200, use_column_width=False)  # Provide the path to your company logo

with col1:  # Main content area

    # Text titles below the text box
    st.markdown("This sentiment analysis model serves as a testing prototype, specifically developed for LDS to assess the variability and precision of OrYx Models' sentiment analysis tool.")
    st.markdown(" ")     
    st.markdown("All feedback gathered from LDS, including both structured and unstructured data, will be incorporated into the model to enhance its domain specificity and maximize accuracy.")

    
    st.title("Sentiment Analysis Prototype Tool by orYx Models (De)")
    user_input = st.text_area("Enter text to analyze:", height=200)
    
    submit_button = st.button("Analyze")

    if submit_button and user_input:
        sentiment, score = analyze_sentiment(user_input)
        st.markdown("Sentiment Analysis Result:")
        st.write(f"Sentiment:  {sentiment}")
        st.write(f"Confidence:  {score*100:.2f}%")