Create app.py

app.py

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+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}")