chatllm / app.py
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Update app.py
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# 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=0.3, max_new_tokens=3000, top_p=0.80, repetition_penalty=0.90,
):
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=30,
)
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
additional_inputs=[
gr.Slider(0, 1, 0.5, label="temperature"),
gr.Slider(500, 5000, 3000, label="max_new_tokens")
]
# [gr.Slider(
# [label="temperature",
# value=0.3,
# minimum=0.0,
# maximum=1.0,
# step=0.1,
# interactive=True,
# info="Higher values generate more diverse outputs",]
# ),
# gr.Slider(
# label="top_p",
# value=0.3,
# minimum=0.0,
# maximum=1.0,
# step=0.1,
# interactive=True,
# info="Higher values generate more diverse outputs",
# ),
# ]
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",
additional_inputs=additional_inputs,
retry_btn=None,
undo_btn=None
)
demo.queue().launch(show_api=False)