import numpy as np 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 ={ "Meta-Llama-3-8B":"meta-llama/Meta-Llama-3-8B-Instruct", "Mistral-7B":"mistralai/Mistral-7B-Instruct-v0.2", "Gemma-7B":"google/gemma-1.1-7b-it", "Gemma-2B":"google/gemma-1.1-2b-it", "Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta", } #Pull info about the model to display model_info ={ "Mistral-7B": {'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'}, "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'}, "Meta-Llama-3-8B": {'description':"""The Llama (3) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ \nIt was created by the [**Meta's AI**](https://llama.meta.com/) team and has over **8 billion parameters.** \n""", 'logo':'Llama_logo.png'}, } #Random dog images for error message random_dog = ["0f476473-2d8b-415e-b944-483768418a95.jpg", "1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg", "526590d2-8817-4ff0-8c62-fdcba5306d02.jpg", "1326984c-39b0-492c-a773-f120d747a7e2.jpg", "42a98d03-5ed7-4b3b-af89-7c4876cb14c3.jpg", "8b3317ed-2083-42ac-a575-7ae45f9fdc0d.jpg", "ee17f54a-83ac-44a3-8a35-e89ff7153fb4.jpg", "027eef85-ccc1-4a66-8967-5d74f34c8bb4.jpg", "08f5398d-7f89-47da-a5cd-1ed74967dc1f.jpg", "0fd781ff-ec46-4bdc-a4e8-24f18bf07def.jpg", "0fb4aeee-f949-4c7b-a6d8-05bf0736bdd1.jpg", "6edac66e-c0de-4e69-a9d6-b2e6f6f9001b.jpg", "bfb9e165-c643-4993-9b3a-7e73571672a6.jpg"] 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"): try: 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) except Exception as e: # st.empty() response = "😵‍💫 Looks like someone unplugged something!😵‍💫\ \n Either the model space is being updated or something is down.\ \n\ \n Try again later. \ \n\ \n Here's a random pic of a 🐶:" st.write(response) random_dog_pick = 'https://random.dog/'+ random_dog[np.random.randint(len(random_dog))] st.image(random_dog_pick) st.write("This was the error message:") st.write(e) st.session_state.messages.append({"role": "assistant", "content": response})