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app.py

@@ -1,22 +1,27 @@
 import streamlit as st
-# from setfit import SetFitModel
+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")
+@st.cache_resource
+def load_model():
+    st.write("Loading model...")
+    return SetFitModel.from_pretrained("Tryfonas/setfit-paraphrase-mpnet-base-v2-sst2")
 
-# model = load_model()
+try:
+    model = load_model()
+    st.write("Model loaded successfully!")
+except Exception as e:
+    st.error(f"Failed to load model: {e}")
 
 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.")
+        try:
+            predictions = model([user_input])
+            st.write(f"Prediction for '{user_input}': {predictions[0]}")
+        except Exception as e:
+            st.error(f"Error during prediction: {e}")
     else:
         st.write("Please enter a sentence to analyze.")