app.py

import pip._internal

print("Installing required libraries...")
pip._internal.main(["install", "-q", "transformers", "torch"])

from transformers import pipeline, AutoTokenizer, AutoConfig, AutoModelForSequenceClassification

import streamlit as st
st.title("Sentiment Analysis App")
models = [
    "distilbert-base-uncased-finetuned-sst-2-english",
    "cardiffnlp/twitter-roberta-base-sentiment",
    "roberta-base-openai-detector",
    "xlnet-base-cased",
    "ProsusAI/finbert",
    "roberta-large-mnli",
    "roberta-large-openai-detector",
    "bhadresh-savani/distilbert-base-uncased-emotion",
    "nlptown/bert-base-multilingual-uncased-sentiment",
    "Seethal/sentiment_analysis_generic_dataset",
    "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis",
    "ahmedrachid/FinancialBERT-Sentiment-Analysis",
]
defaultModelName = models[0]
modelName = st.selectbox("Select a model", options=models, index=models.index(defaultModelName))
sampleText = """Once there were brook trouts in the streams in the mountains. 
You could see them standing in the amber current where the white edges of their fins wimpled softly in the flow. 
They smelled of moss in your hand. Polished and muscular and torsional. 
On their backs were vermiculate patterns that were maps of the world in its becoming. 
Maps and mazes. Of a thing which could not be put back. Not be made right again. 
In the deep glens where they lived all things were older than man and they hummed of mystery."""
textInput = st.text_area("Enter some text to analyze", value=sampleText, height=200)
submitButton = st.button("Analyze")

tokenizer = AutoTokenizer.from_pretrained(modelName)
model = AutoModelForSequenceClassification.from_pretrained(modelName)
sentimentPipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

if submitButton:
    if not textInput.strip():  
        st.write("Please enter some text to analyze.")
    else:
        results = sentimentPipeline(textInput)
        st.write(f"Sentiment: {results[0]['label']}, Score: {results[0]['score']:.2f}")