Update app.py

app.py

@@ -5,6 +5,7 @@ import torch
 # Load DialoGPT model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
 # Function to generate a response
 def generate_response(chat_history_ids, new_user_input):
     new_user_input_ids = tokenizer.encode(new_user_input + tokenizer.eos_token, return_tensors='pt')
@@ -14,8 +15,9 @@ def generate_response(chat_history_ids, new_user_input):
 # Streamlit app
 st.title("DialoGPT Chat")
 
-# Initialize chat history
-chat_history_ids = None
+# Initialize chat history using st.session_state
+if 'chat_history_ids' not in st.session_state:
+    st.session_state.chat_history_ids = None
 
 # User input text box
 user_input = st.text_input("You:", "")
@@ -23,11 +25,11 @@ user_input = st.text_input("You:", "")
 # Check if the user pressed Enter
 if st.button("Send"):
     if user_input:
-        # Generate response
-        chat_history_ids = generate_response(chat_history_ids, user_input)
+        # Generate response using st.session_state
+        st.session_state.chat_history_ids = generate_response(st.session_state.chat_history_ids, user_input)
 
         # Display DialoGPT response
-        st.text("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, -1][0], skip_special_tokens=True)))
+        st.text("DialoGPT: {}".format(tokenizer.decode(st.session_state.chat_history_ids[:, -1][0], skip_special_tokens=True)))
 
 # Inform the user that the conversation has ended
 st.text("Press 'Send' to continue the conversation.")