Update app.py

app.py

@@ -1,25 +1,33 @@
 import streamlit as st
-import joblib  # Replace with torch if using a PyTorch model
+from transformers import pipeline
 
-# Load the trained model (ensure the model file is in the same directory)
-model = joblib.load('cardiffnlp/twitter-roberta-base-sentiment-latest')
+# Load the sentiment analysis model from Hugging Face
+@st.cache_resource  # Cache the model for faster loading
+def load_model():
+    return pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment')
+
+model = load_model()
 
 # Streamlit UI
 st.title("Sentiment Analysis App using GenAI Models")
 
 # Text input from the user
-user_input = st.text_area("Enter text to analyze sentiment:", "")
+user_input = st.text_area("Enter text to analyze sentiment:")
 
 # Prediction button
 if st.button("Analyze"):
     if user_input:
         # Perform prediction
-        prediction = model.predict([user_input])
-        sentiment = "Positive" if prediction[0] == 1 else "Negative"
+        result = model(user_input)  # Hugging Face pipeline returns a dictionary
+        sentiment = result[0]['label']  # Get sentiment label (Positive/Negative/Neutral)
+        confidence = result[0]['score']  # Confidence score
+        
+        # Display the sentiment and confidence score
         st.write(f"**Predicted Sentiment:** {sentiment}")
+        st.write(f"**Confidence Score:** {confidence:.2f}")
     else:
         st.warning("Please enter some text to analyze.")
 
 # Optional: Footer
 st.write("---")
-st.caption("Built with Streamlit and GenAI models.")
+st.caption("Built with Streamlit and Hugging Face's GenAI models.")