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.devcontainer/devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "streamlit_app.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run streamlit_app.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

README.md

@@ -1,15 +1,3 @@
----
-title: Streamlit Huggingface
-emoji: 🏢
-colorFrom: purple
-colorTo: red
-sdk: streamlit
-sdk_version: 1.41.1
-app_file: app.py
-pinned: false
-license: mit
----
-
 # 🤗💬 HugChat App
 ```
 This app is an LLM-powered chatbot built using Streamlit and HugChat.

app_v1.py

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+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
+import os
+
+# Streamlit page config
+st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app")
+
+# Sidebar contents
+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)')
+
+# Initialize chatbot and session state
+if 'chatbot' not in st.session_state:
+    # Create ChatBot instance
+    st.session_state.chatbot = hugchat.ChatBot()
+
+if 'generated' not in st.session_state:
+    st.session_state['generated'] = ["I'm HugChat, How may I help you?"]
+
+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
+def get_text():
+    return st.text_input("You: ", "", key="input")
+
+with input_container:
+    user_input = get_text()
+
+# AI Response Generation
+def generate_response(prompt):
+    try:
+        response = st.session_state.chatbot.chat(prompt)
+        return response
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+# Display conversation
+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=f"{i}_user")
+            message(st.session_state["generated"][i], key=f"{i}")

app_v2.py

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+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
+
+st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app")
+
+# Sidebar contents
+with st.sidebar:
+    st.title('🤗💬 HugChat App')
+    
+    st.header('Hugging Face Login')
+    hf_email = st.text_input('Enter E-mail:', type='password')
+    hf_pass = st.text_input('Enter password:', type='password')
+    
+    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
+
+    ''')
+    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, email, passwd):
+    # Hugging Face Login
+    sign = Login(email, passwd)
+    cookies = sign.login()
+    sign.saveCookies()
+    # Create ChatBot                        
+    chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
+    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 and hf_email and hf_pass:
+        response = generate_response(user_input, hf_email, hf_pass)
+        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))

app_v3.py

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+import streamlit as st
+from hugchat import hugchat
+from hugchat.login import Login
+import os
+
+# App title
+st.set_page_config(page_title="🤗💬 HugChat")
+
+# Hugging Face Credentials
+with st.sidebar:
+    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)

img/placeholder.md

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+

langchain_app.py

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+import streamlit as st
+from langchain.chains import ConversationChain
+from hugchat import hugchat
+from hugchat.login import Login
+
+st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app")
+st.title('🤗💬 HugChat App')
+
+# Hugging Face Credentials
+with st.sidebar:
+    st.header('Hugging Face Login')
+    hf_email = st.text_input('Enter E-mail:', type='password')
+    hf_pass = st.text_input('Enter password:', type='password')
+
+# Store AI generated responses
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [{"role": "assistant", "content": "I'm HugChat, How may I help you?"}]
+
+# Display existing chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+# Function for generating LLM response
+def generate_response(prompt, email, passwd):
+    # Hugging Face Login
+    sign = Login(email, passwd)
+    cookies = sign.login()
+    sign.saveCookies()
+    # Create ChatBot                        
+    chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
+    chain = ConversationChain(llm=chatbot)
+    response = chain.run(input=prompt)
+    return response
+
+# Prompt for user input and save
+if prompt := st.chat_input():
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+
+# If last message is not from assistant, we need to generate a new response
+if st.session_state.messages[-1]["role"] != "assistant":
+    # Call LLM
+    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)

notebook/hf.env

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+EMAIL='user@email.com'
+PASS='xxxxxxxxxxx'