Update app.py

app.py

@@ -1,63 +1,69 @@
 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
 
-st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app")
+# App title
+st.set_page_config(page_title="🤗💬 HugChat")
 
-# Sidebar contents
+# Hugging Face Credentials
 with st.sidebar:
-    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))
+    st.title('🤗💬 HugChat')
+    if ('EMAIL' in st.secrets) and ('PASS' in st.secrets):
+        st.success('HuggingFace Login credentials already provided!', icon='✅')
+        hf_email = st.secrets['EMAIL']
+        hf_pass = st.secrets['PASS']
+    else:
+        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)