Update app.py

app.py

@@ -1,64 +1,63 @@
 import streamlit as st
+from streamlit_chat import message
+from streamlit_extras.colored_header import colored_header
+from streamlit_extras.add_vertical_space import add_vertical_space
 from hugchat import hugchat
-from hugchat.login import Login
-import os
 
-# App title
-st.set_page_config(page_title="🤗💬 HugChat")
+st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app")
 
-# Hugging Face Credentials
+# Sidebar contents
 with st.sidebar:
-    st.title('🤗💬 HugChat')
-    hf_email = st.text_input('Enter E-mail:', type='password')
-    hf_pass = st.text_input('Enter password:', type='password')
-    if not (hf_email and hf_pass):
-        st.warning('Please enter your credentials!', icon='⚠️')
-    else:
-        st.success('Proceed to entering your prompt message!', icon='👉')
-    st.markdown('📖 Learn how to build this app in this [blog](https://blog.streamlit.io/how-to-build-an-llm-powered-chatbot-with-streamlit/)!')
-
-# Store LLM generated responses
-if "messages" not in st.session_state:
-    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
-
-# Display or clear chat messages
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.write(message["content"])
-
-def clear_chat_history():
-    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
-st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
-
-# Function for generating LLM response
-def generate_response(prompt_input, email, passwd):
-    # Hugging Face Login
-    sign = Login(email, passwd)
-    cookies = sign.login()
-    # Create ChatBot                        
-    chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
-
-    for dict_message in st.session_state.messages:
-        string_dialogue = "You are a helpful assistant."
-        if dict_message["role"] == "user":
-            string_dialogue += "User: " + dict_message["content"] + "\n\n"
-        else:
-            string_dialogue += "Assistant: " + dict_message["content"] + "\n\n"
-
-    prompt = f"{string_dialogue} {prompt_input} Assistant: "
-    return chatbot.chat(prompt)
-
-# User-provided prompt
-if prompt := st.chat_input(disabled=not (hf_email and hf_pass)):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    with st.chat_message("user"):
-        st.write(prompt)
-
-# Generate a new response if last message is not from assistant
-if st.session_state.messages[-1]["role"] != "assistant":
-    with st.chat_message("assistant"):
-        with st.spinner("Thinking..."):
-            response = generate_response(prompt, hf_email, hf_pass) 
-            st.write(response) 
-    message = {"role": "assistant", "content": response}
-    st.session_state.messages.append(message)
+    st.title('🤗💬 HugChat App')
+    st.markdown('''
+    ## About
+    This app is an LLM-powered chatbot built using:
+    - [Streamlit](https://streamlit.io/)
+    - [HugChat](https://github.com/Soulter/hugging-chat-api)
+    - [OpenAssistant/oasst-sft-6-llama-30b-xor](https://huggingface.co/OpenAssistant/oasst-sft-6-llama-30b-xor) LLM model
+    
+    💡 Note: No API key required!
+    ''')
+    add_vertical_space(5)
+    st.write('Made with ❤️ by [Data Professor](https://youtube.com/dataprofessor)')
+
+# Generate empty lists for generated and past.
+## generated stores AI generated responses
+if 'generated' not in st.session_state:
+    st.session_state['generated'] = ["I'm HugChat, How may I help you?"]
+## past stores User's questions
+if 'past' not in st.session_state:
+    st.session_state['past'] = ['Hi!']
+
+# Layout of input/response containers
+input_container = st.container()
+colored_header(label='', description='', color_name='blue-30')
+response_container = st.container()
+
+# User input
+## Function for taking user provided prompt as input
+def get_text():
+    input_text = st.text_input("You: ", "", key="input")
+    return input_text
+## Applying the user input box
+with input_container:
+    user_input = get_text()
+
+# Response output
+## Function for taking user prompt as input followed by producing AI generated responses
+def generate_response(prompt):
+    chatbot = hugchat.ChatBot()
+    response = chatbot.chat(prompt)
+    return response
+
+## Conditional display of AI generated responses as a function of user provided prompts
+with response_container:
+    if user_input:
+        response = generate_response(user_input)
+        st.session_state.past.append(user_input)
+        st.session_state.generated.append(response)
+        
+    if st.session_state['generated']:
+        for i in range(len(st.session_state['generated'])):
+            message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
+            message(st.session_state["generated"][i], key=str(i))