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""" Simple Chatbot | |
@author: Nigel Gebodh | |
@email: nigel.gebodh@gmail.com | |
""" | |
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":"mistralai/Mistral-7B-Instruct-v0.2", | |
"Gemma-7B":"google/gemma-7b-it", | |
"Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta", | |
"Mesolitica":"mesolitica/malaysian-mistral-3B-4096", | |
} | |
#Pull info about the model to display | |
model_info ={ | |
"Mistral": | |
{'description':"""The Mistral 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'}, | |
"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'}, | |
"Llama-2": | |
{'description':"""Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters.\n \ | |
\nFrom Huggingface: \n\ | |
[Llama-2](https://huggingface.co/meta-llama/Llama-2-7b)\ | |
is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \n""",}, | |
"Command-R": | |
{'description':"""Command-R is a **Large Language Model (LLM)** with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities.\n \ | |
\nFrom Huggingface: \n\ | |
[Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)\ | |
is a research release of a 35 billion parameter highly performant generative model. \n""",}, | |
"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'}, | |
"Mesolitica": | |
{'description':"""GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way.\n \ | |
\nFrom Huggingface: \n\ | |
This is the smallest version of [GPT-2](https://huggingface.co/openai-community/gpt2)\ | |
with 124M parameters. \n""",}, | |
} | |
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}) |