Update app.py

app.py

@@ -1,25 +1,31 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import streamlit as st
-import joblib  # Replace with torch if using a PyTorch model
+import torch
 
-# Load the trained model (ensure the model file is in the same directory)
-model = joblib.load('path_to_your_model.pkl')
+# Load tokenizer and model from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
 
-# Streamlit UI
+# Streamlit UI setup
 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"
+        # Tokenize the user input
+        inputs = tokenizer(user_input, return_tensors="pt")
+        
+        # Perform inference
+        with torch.no_grad():
+            outputs = model(**inputs)
+        
+        # Interpret the results
+        predicted_class = torch.argmax(outputs.logits, dim=1).item()
+        sentiment = ["Negative", "Neutral", "Positive"][predicted_class]  # Assuming 3 classes
+
         st.write(f"**Predicted Sentiment:** {sentiment}")
     else:
         st.warning("Please enter some text to analyze.")
-
-# Optional: Footer
-st.write("---")
-st.caption("Built with Streamlit and GenAI models.")