Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load sentiment analysis model
+model = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+sentiment_analysis = pipeline("text-classification", model=model)
+
+
+# Streamlit app
+def main():
+    # Set app title and description
+    st.title("Sentiment Analysis App")
+    st.write("Enter text to predict sentiment.")
+
+    # User input
+    text = st.text_area("Text", "")
+
+    # Predict sentiment
+    if st.button("Predict"):
+        if text.strip() != "":
+            sentiment = predict_sentiment(text)
+            score = sentiment['score'] * 100
+            st.metric(label = f"Sentiment: {sentiment['label']}", value = f"Score: {score:.2f}%")
+            #st.write(f"Sentiment: {sentiment['label']}")
+            #st.write(f"Score: {sentiment['score']}")
+        else:
+            st.warning("Please enter some text.")
+
+
+def predict_sentiment(text):
+    result = sentiment_analysis(text)[0]
+    return result
+
+
+if __name__ == "__main__":
+    main()