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app.py

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+import streamlit as st
+# from setfit import SetFitModel
+
+# Cache the model loading process to optimize performance on Hugging Face Spaces
+# @st.cache_resource
+# def load_model():
+#     return SetFitModel.from_pretrained("Tryfonas/setfit-paraphrase-mpnet-base-v2-sst2")
+
+# model = load_model()
+
+st.title("Text Sentiment Analysis 😊😞")
+
+# Create a text input for a single sentence
+user_input = st.text_input("Enter a sentence for sentiment analysis:", placeholder="I love this!")
+
+# Add a button to trigger predictions
+if st.button("Analyze Sentiment"):
+    if user_input:
+        # Temporarily show the input instead of model predictions
+        st.write(f"Prediction for '{user_input}': This is a placeholder response.")
+    else:
+        st.write("Please enter a sentence to analyze.")

requirements.txt

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+streamlit
+setfit
+transformers
+sentence-transformers