# import streamlit as st | |
# from openai import OpenAI | |
# import os | |
# import sys | |
# from dotenv import load_dotenv, dotenv_values | |
# load_dotenv() | |
# # initialize the client | |
# client = OpenAI( | |
# base_url="https://api-inference.huggingface.co/v1", | |
# api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')#"hf_xxx" # Replace with your token | |
# ) | |
# #Create supported models | |
# model_links ={ | |
# "Mistral-7b":"mistralai/Mistral-7B-Instruct-v0.2", | |
# "Mistral-8x7b":"mistralai/Mixtral-8x7B-Instruct-v0.1" | |
# # "Gemma-7B":"google/gemma-7b-it", | |
# # "Gemma-2B":"google/gemma-2b-it", | |
# # "Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta", | |
# } | |
# #Pull info about the model to display | |
# model_info ={ | |
# "Mistral-7b": | |
# {'description':"""The Mistral 7B model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
# \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-7b/) team as has over **7 billion parameters.** \n""", | |
# 'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'}, | |
# "Mistral-8x7b": | |
# {'description':"""The Mistral 8x7B model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
# \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-8x7b/) team as has based on MOE arch.** \n""", | |
# 'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'}, | |
# # "Gemma-7B": | |
# # {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
# # \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over **7 billion parameters.** \n""", | |
# # 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'}, | |
# # "Gemma-2B": | |
# # {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
# # \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over **2 billion parameters.** \n""", | |
# # 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'}, | |
# # "Zephyr-7B": | |
# # {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
# # \nFrom Huggingface: \n\ | |
# # Zephyr is a series of language models that are trained to act as helpful assistants. \ | |
# # [Zephyr 7B Gemma](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\ | |
# # is the third model in the series, and is a fine-tuned version of google/gemma-7b \ | |
# # that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""", | |
# # 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png'}, | |
# # "Zephyr-7B-β": | |
# # {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
# # \nFrom Huggingface: \n\ | |
# # Zephyr is a series of language models that are trained to act as helpful assistants. \ | |
# # [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\ | |
# # is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 \ | |
# # that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""", | |
# # 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png'}, | |
# } | |
# def reset_conversation(): | |
# ''' | |
# Resets Conversation | |
# ''' | |
# st.session_state.conversation = [] | |
# st.session_state.messages = [] | |
# return None | |
# # Define the available models | |
# models =[key for key in model_links.keys()] | |
# # Create the sidebar with the dropdown for model selection | |
# selected_model = st.sidebar.selectbox("Select Model", models) | |
# #Create a temperature slider | |
# temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5)) | |
# #Add reset button to clear conversation | |
# st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button | |
# # Create model description | |
# st.sidebar.write(f"You're now chatting with **{selected_model}**") | |
# st.sidebar.markdown(model_info[selected_model]['description']) | |
# st.sidebar.image(model_info[selected_model]['logo']) | |
# # st.sidebar.markdown("*Generated content may be inaccurate or false.*") | |
# # st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).") | |
# # st.sidebar.markdown("\nRun into issues? Try the [back-up](https://huggingface.co/spaces/ngebodh/SimpleChatbot-Backup).") | |
# if "prev_option" not in st.session_state: | |
# st.session_state.prev_option = selected_model | |
# if st.session_state.prev_option != selected_model: | |
# st.session_state.messages = [] | |
# # st.write(f"Changed to {selected_model}") | |
# st.session_state.prev_option = selected_model | |
# reset_conversation() | |
# #Pull in the model we want to use | |
# repo_id = model_links[selected_model] | |
# st.subheader(f'AI - {selected_model}') | |
# # st.title(f'ChatBot Using {selected_model}') | |
# # Set a default model | |
# if selected_model not in st.session_state: | |
# st.session_state[selected_model] = model_links[selected_model] | |
# # Initialize chat history | |
# if "messages" not in st.session_state: | |
# st.session_state.messages = [] | |
# # Display chat messages from history on app rerun | |
# for message in st.session_state.messages: | |
# with st.chat_message(message["role"]): | |
# st.markdown(message["content"]) | |
# # Accept user input | |
# if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"): | |
# # Display user message in chat message container | |
# with st.chat_message("user"): | |
# st.markdown(prompt) | |
# # Add user message to chat history | |
# st.session_state.messages.append({"role": "user", "content": prompt}) | |
# # Display assistant response in chat message container | |
# with st.chat_message("assistant"): | |
# stream = client.chat.completions.create( | |
# model=model_links[selected_model], | |
# messages=[ | |
# {"role": m["role"], "content": m["content"]} | |
# for m in st.session_state.messages | |
# ], | |
# temperature=temp_values,#0.5, | |
# stream=True, | |
# max_tokens=3000, | |
# ) | |
# response = st.write_stream(stream) | |
# st.session_state.messages.append({"role": "assistant", "content": response}) | |
from huggingface_hub import InferenceClient | |
import gradio as gr | |
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2") | |
def format_prompt(message, history): | |
prompt = "<s>" | |
for user_prompt, bot_response in history: | |
prompt += f"[INST] {user_prompt} [/INST]" | |
prompt += f" {bot_response}</s> " | |
prompt += f"[INST] {message} [/INST]" | |
return prompt | |
def generate( | |
prompt, history, temperature=0.2, max_new_tokens=3000, top_p=0.95, repetition_penalty=1.0, | |
): | |
temperature = float(temperature) | |
if temperature < 1e-2: | |
temperature = 1e-2 | |
top_p = float(top_p) | |
generate_kwargs = dict( | |
temperature=temperature, | |
max_new_tokens=max_new_tokens, | |
top_p=top_p, | |
repetition_penalty=repetition_penalty, | |
do_sample=True, | |
seed=42, | |
) | |
formatted_prompt = format_prompt(prompt, history) | |
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) | |
output = "" | |
for response in stream: | |
output += response.token.text | |
yield output | |
return output | |
mychatbot = gr.Chatbot( | |
avatar_images=["./user.png", "./bot.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,) | |
demo = gr.ChatInterface(fn=generate, | |
chatbot=mychatbot, | |
title="Mistral-Chat", | |
retry_btn=None, | |
undo_btn=None | |
) | |
demo.queue().launch(show_api=False) |