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| 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() | |
| # Comment_test_11_09_2024 | |
| # Initialize the client | |
| client = OpenAI( | |
| base_url="https://api-inference.huggingface.co/v1", | |
| api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN') # 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", | |
| # "Meta-Llama-3.1-8B": "meta-llama/Meta-Llama-3.1-8B-Instruct", # TODO: Update when/if Serverless Inference available | |
| } | |
| # 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 team and has over **7 billion parameters.** \n""" | |
| }, | |
| "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 Google's AI Team and has over **7 billion parameters.** \n""" | |
| }, | |
| "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 Google's AI Team and has over **2 billion parameters.** \n""" | |
| }, | |
| "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 is the third model in the series, and is a fine-tuned version of google/gemma-7b that was trained on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\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 is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""" | |
| }, | |
| "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 Meta's AI team and has over **8 billion parameters.** \n""" | |
| }, | |
| "Meta-Llama-3.1-8B": { | |
| 'description': """The Llama (3.1) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \ | |
| \nIt was created by Meta's AI team and has over **8 billion parameters.** \n""" | |
| }, | |
| } | |
| # 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']) | |
| # Only display the logo if it exists | |
| if 'logo' in model_info[selected_model]: | |
| st.sidebar.image(model_info[selected_model]['logo']) | |
| st.sidebar.markdown("*Generated content may be inaccurate or false.*") | |
| st.sidebar.markdown("\nFor More Visit **Womener AI**") | |
| st.sidebar.markdown("\nRun into issues? \nTry coming back in a bit, GPU access might be limited or something is down.") | |
| 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.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}') | |
| # 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: | |
| response = "😵💫 Looks like someone unplugged something! \ | |
| \n Either the model space is being updated or something is down. \ | |
| \n Try again later. \ | |
| \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}) | |