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import streamlit as st
from openai import OpenAI
import os
import sys
from langchain.callbacks import StreamlitCallbackHandler
from dotenv import load_dotenv, dotenv_values
load_dotenv()
if 'key' not in st.session_state:
st.session_state['key'] = 'value'
# initialize the client but point it to TGI
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":"google/gemma-7b-it",
# "Llama-2":"meta-llama/Llama-2-7b-chat-hf"
}
#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":
{'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'},
}
# Define the available models
# models = ["Mistral", "Gemma"]
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 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'])
#Pull in the model we want to use
repo_id = model_links[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("What is up?"):
# 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"):
st_callback = StreamlitCallbackHandler(st.container())
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=0.5,
stream=True,
max_tokens=3000,
)
response = st.write_stream(stream)
st.session_state.messages.append({"role": "assistant", "content": response}) |