Update app.py

app.py

@@ -1,33 +1,25 @@
 import streamlit as st
-from transformers import pipeline
+import joblib  # Replace with torch if using a PyTorch model
 
-# 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()
+# Load the trained model (ensure the model file is in the same directory)
+model = joblib.load('path_to_your_model.pkl')
 
 # 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
-        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
+        prediction = model.predict([user_input])
+        sentiment = "Positive" if prediction[0] == 1 else "Negative"
         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 Hugging Face's GenAI models.")
+st.caption("Built with Streamlit and GenAI models.")