Create app.py

app.py

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+# import part
+import streamlit as st
+from transformers import pipeline
+import numpy as np  # Import numpy
+
+# function part
+# text classification
+def textclassification():
+    sentiment_pipeline = pipeline(model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+    st.title("Sentiment Analysis with HuggingFace Spaces")
+    st.write("Enter a sentence to analyze its sentiment:")
+
+    user_input = st.text_input("")
+    if user_input:
+        result = sentiment_pipeline(user_input)
+        sentiment = result[0]["label"]
+        confidence = result[0]["score"]
+
+        st.write(f"Sentiment: {sentiment}")
+        st.write(f"Confidence: {confidence:.2f}")
+
+        # Determine the rating based on sentiment
+        if sentiment == "POSITIVE":
+            rating = 5  # Assuming positive sentiment corresponds to a higher rating
+        else:
+            rating = 1  # Assuming negative sentiment corresponds to a lower rating
+
+        # Print the rating
+        st.write(f"The rating is {rating} stars")  # Use Streamlit to display instead of print
+
+def main():
+    textclassification()
+
+if __name__ == "__main__":
+    main()