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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() | |
| # 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.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"): | |
| 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}) |