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Update app.py
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app.py
CHANGED
@@ -7,86 +7,76 @@ import sys
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from dotenv import load_dotenv, dotenv_values
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load_dotenv()
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#
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# initialize the client
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client = OpenAI(
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)
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"
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"
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"
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"
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"Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta",
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#"Meta-Llama-3.1-8B":"meta-llama/Meta-Llama-3.1-8B-Instruct", #TODO: Update when/if Serverless Inference available
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}
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#Pull info about the model to display
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model_info = {
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"Mistral-7B": {
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'description': """The Mistral model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by the Mistral AI team
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},
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"Gemma-7B": {
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'description': """The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by
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},
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"Gemma-2B": {
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'description': """The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by
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},
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"Zephyr-7B": {
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'description': """The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nFrom Huggingface: \n\
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Zephyr is a series of language models that are trained to act as helpful assistants. \
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Zephyr 7B
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is the third model in the series, and is a fine-tuned version of google/gemma-7b \
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that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n"""
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},
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"Zephyr-7B-β": {
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'description': """The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nFrom Huggingface: \n\
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Zephyr is a series of language models that are trained to act as helpful assistants. \
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Zephyr-7B
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is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 \
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that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n"""
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},
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"Meta-Llama-3-8B": {
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'description': """The Llama (3) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by
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},
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"Meta-Llama-3.1-8B": {
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'description': """The Llama (3.1) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by
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},
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}
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def reset_conversation():
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'''
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st.session_state.conversation = []
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st.session_state.messages = []
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return None
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# Define the available models
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models =[key for key in model_links.keys()]
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# Create the sidebar with the dropdown for model selection
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selected_model = st.sidebar.selectbox("Select Model", models)
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#Create a temperature slider
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temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
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#
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st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button
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# Create model description
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st.sidebar.write(f"You're now chatting with **{selected_model}**")
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st.sidebar.markdown(model_info[selected_model]['description'])
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st.sidebar.markdown("*Generated content may be inaccurate or false.*")
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st.sidebar.markdown("\nFor More Visit **Womener AI**")
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st.sidebar.markdown("\nRun into issues? \nTry coming back in a bit, GPU access might be limited or something is down.")
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if "prev_option" not in st.session_state:
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st.session_state.prev_option = selected_model
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if st.session_state.prev_option != selected_model:
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st.session_state.messages = []
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# st.write(f"Changed to {selected_model}")
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st.session_state.prev_option = selected_model
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reset_conversation()
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#Pull in the model we want to use
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repo_id = model_links[selected_model]
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st.subheader(f'AI - {selected_model}')
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# st.title(f'ChatBot Using {selected_model}')
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# Set a default model
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if selected_model not in st.session_state:
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st.session_state[selected_model] = model_links[selected_model]
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Accept user input
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if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
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@@ -167,7 +145,6 @@ if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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# Display assistant response in chat message container
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with st.chat_message("assistant"):
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@@ -178,28 +155,22 @@ if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
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{"role": m["role"], "content": m["content"]}
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for m in st.session_state.messages
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],
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temperature=temp_values
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stream=True,
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max_tokens=3000,
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)
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response = st.write_stream(stream)
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except Exception as e:
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\n Try again later. \
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\n\
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\n Here's a random pic of a 🐶:"
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st.write(response)
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random_dog_pick = 'https://random.dog/'+ random_dog[np.random.randint(len(random_dog))]
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st.image(random_dog_pick)
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st.write("This was the error message:")
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st.write(e)
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st.session_state.messages.append({"role": "assistant", "content": response})
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from dotenv import load_dotenv, dotenv_values
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load_dotenv()
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# Comment_test_11_09_2024
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# Initialize the client
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client = OpenAI(
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base_url="https://api-inference.huggingface.co/v1",
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api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN') # Replace with your token
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)
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# Create supported models
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model_links = {
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"Meta-Llama-3-8B": "meta-llama/Meta-Llama-3-8B-Instruct",
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"Mistral-7B": "mistralai/Mistral-7B-Instruct-v0.2",
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"Gemma-7B": "google/gemma-1.1-7b-it",
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"Gemma-2B": "google/gemma-1.1-2b-it",
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"Zephyr-7B-β": "HuggingFaceH4/zephyr-7b-beta",
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# "Meta-Llama-3.1-8B": "meta-llama/Meta-Llama-3.1-8B-Instruct", # TODO: Update when/if Serverless Inference available
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}
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# Pull info about the model to display
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model_info = {
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"Mistral-7B": {
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'description': """The Mistral model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by the Mistral AI team and has over **7 billion parameters.** \n"""
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},
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"Gemma-7B": {
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'description': """The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by Google's AI Team and has over **7 billion parameters.** \n"""
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},
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"Gemma-2B": {
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'description': """The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by Google's AI Team and has over **2 billion parameters.** \n"""
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},
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"Zephyr-7B": {
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'description': """The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nFrom Huggingface: \n\
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Zephyr is a series of language models that are trained to act as helpful assistants. \
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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"""
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},
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"Zephyr-7B-β": {
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'description': """The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nFrom Huggingface: \n\
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Zephyr is a series of language models that are trained to act as helpful assistants. \
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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"""
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},
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"Meta-Llama-3-8B": {
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'description': """The Llama (3) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by Meta's AI team and has over **8 billion parameters.** \n"""
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},
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"Meta-Llama-3.1-8B": {
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'description': """The Llama (3.1) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
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\nIt was created by Meta's AI team and has over **8 billion parameters.** \n"""
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},
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}
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# Random dog images for error message
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random_dog = [
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"0f476473-2d8b-415e-b944-483768418a95.jpg",
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"1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg",
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"526590d2-8817-4ff0-8c62-fdcba5306d02.jpg",
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"1326984c-39b0-492c-a773-f120d747a7e2.jpg",
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"42a98d03-5ed7-4b3b-af89-7c4876cb14c3.jpg",
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"8b3317ed-2083-42ac-a575-7ae45f9fdc0d.jpg",
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"ee17f54a-83ac-44a3-8a35-e89ff7153fb4.jpg",
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"027eef85-ccc1-4a66-8967-5d74f34c8bb4.jpg",
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"08f5398d-7f89-47da-a5cd-1ed74967dc1f.jpg",
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"0fd781ff-ec46-4bdc-a4e8-24f18bf07def.jpg",
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"0fb4aeee-f949-4c7b-a6d8-05bf0736bdd1.jpg",
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"6edac66e-c0de-4e69-a9d6-b2e6f6f9001b.jpg",
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"bfb9e165-c643-4993-9b3a-7e73571672a6.jpg"
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]
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def reset_conversation():
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'''
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st.session_state.conversation = []
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st.session_state.messages = []
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return None
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# Define the available models
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models = [key for key in model_links.keys()]
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# Create the sidebar with the dropdown for model selection
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selected_model = st.sidebar.selectbox("Select Model", models)
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# Create a temperature slider
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temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
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# Add reset button to clear conversation
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st.sidebar.button('Reset Chat', on_click=reset_conversation) # Reset button
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# Create model description
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st.sidebar.write(f"You're now chatting with **{selected_model}**")
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st.sidebar.markdown(model_info[selected_model]['description'])
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# Only display the logo if it exists
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if 'logo' in model_info[selected_model]:
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st.sidebar.image(model_info[selected_model]['logo'])
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st.sidebar.markdown("*Generated content may be inaccurate or false.*")
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st.sidebar.markdown("\nFor More Visit **Womener AI**")
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st.sidebar.markdown("\nRun into issues? \nTry coming back in a bit, GPU access might be limited or something is down.")
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if "prev_option" not in st.session_state:
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st.session_state.prev_option = selected_model
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if st.session_state.prev_option != selected_model:
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st.session_state.messages = []
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st.session_state.prev_option = selected_model
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reset_conversation()
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# Pull in the model we want to use
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repo_id = model_links[selected_model]
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st.subheader(f'AI - {selected_model}')
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# Set a default model
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if selected_model not in st.session_state:
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st.session_state[selected_model] = model_links[selected_model]
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# Accept user input
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if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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# Display assistant response in chat message container
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with st.chat_message("assistant"):
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{"role": m["role"], "content": m["content"]}
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for m in st.session_state.messages
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],
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temperature=temp_values, # 0.5,
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stream=True,
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max_tokens=3000,
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)
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response = st.write_stream(stream)
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except Exception as e:
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response = "😵💫 Looks like someone unplugged something! \
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\n Either the model space is being updated or something is down. \
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\n Try again later. \
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\n Here's a random pic of a 🐶:"
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st.write(response)
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random_dog_pick = 'https://random.dog/' + random_dog[np.random.randint(len(random_dog))]
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st.image(random_dog_pick)
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st.write("This was the error message:")
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st.write(e)
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st.session_state.messages.append({"role": "assistant", "content": response})
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