Update app.py

app.py

@@ -2,8 +2,8 @@ import streamlit as st
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 import torch
 
-# Load pre-trained T5 model and tokenizer
-model_name = "t5-small"  # Use t5-small for faster responses
+# Load pre-trained model and tokenizer from the "KhantKyaw/T5-small_new_chatbot"
+model_name = "KhantKyaw/T5-small_new_chatbot"  # Use the fine-tuned model
 model = T5ForConditionalGeneration.from_pretrained(model_name)
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 
@@ -12,7 +12,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
 # Streamlit Interface
-st.title("Simple Chatbot with T5")
+st.title("Mental Health Chatbot with T5")
 
 def generate_response(input_text):
     # Add conversational context to input