Delete hugging_steam-master

hugging_steam-master/.devcontainer/devcontainer.json

@@ -1,33 +0,0 @@
-{
-  "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
-  ]
-}

hugging_steam-master/README.md

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-# 🤗💬 HugChat App
-```
-This app is an LLM-powered chatbot built using Streamlit and HugChat.
-```
-
-[HugChat](https://github.com/Soulter/hugging-chat-api) is an unofficial port to the [HuggingFace Chat](https://huggingface.co/chat/) API that is powered by the [OpenAssistant/oasst-sft-6-llama-30b-xor](https://huggingface.co/OpenAssistant/oasst-sft-6-llama-30b-xor) LLM model.
-
-## Demo App
-
-[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://hugchat.streamlit.app/)
-
-## Disclaimer
-The following disclaimer is from the GitHub repo from the authors of the [HugChat](https://github.com/Soulter/hugging-chat-api) port.
-> When you use this project, it means that you have agreed to the following two requirements of the HuggingChat:
->
-> AI is an area of active research with known problems such as biased generation and misinformation. Do not use this application for high-stakes decisions or advice. Your conversations will be shared with model authors.
-
-
-## Libraries used
-
-This app is built using the following Python libraries:
-- [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

hugging_steam-master/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}")

hugging_steam-master/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))

hugging_steam-master/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)

hugging_steam-master/img/placeholder.md

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-

hugging_steam-master/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)

hugging_steam-master/notebook/hf.env

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-EMAIL='[email protected]'
-PASS='xxxxxxxxxxx'