Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np  
+# Load sentiment analysis model
+@st.cache_resource
+def load_model():
+    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    return pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+
+classifier = load_model()
+
+# Streamlit UI
+st.title("Sentiment Analysis App")
+st.header("Analyze Text Sentiment")
+
+user_input = st.text_area("Enter text to analyze:", "I love using Hugging face! It's awesome.")
+
+if st.button("Analyze"):
+    if user_input:
+        result = classifier(user_input)
+        sentiment = result[0]['label']
+        confidence = result[0]['score']
+        
+        st.subheader("Result:")
+        if sentiment == 'POSITIVE':
+            st.success(f"Positive sentiment (confidence: {confidence:.2%})")
+        else:
+            st.error(f"Negative sentiment (confidence: {confidence:.2%})")
+    else:
+        st.warning("Please enter some text to analyze!")