Spaces:
Running
Running
| 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.*") | |
| 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}) | |
| # import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| # """ | |
| # For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| # """ | |
| # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| # ): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # messages.append({"role": "user", "content": message}) | |
| # response = "" | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = message.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| # """ | |
| # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| # """ | |
| # demo = gr.ChatInterface( | |
| # respond, | |
| # additional_inputs=[ | |
| # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| # gr.Slider( | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # value=0.95, | |
| # step=0.05, | |
| # label="Top-p (nucleus sampling)", | |
| # ), | |
| # ], | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| ##################################### | |
| # import gradio as gr | |
| # gr.load("models/meta-llama/Meta-Llama-3.1-70B-Instruct").launch() | |
| ######################################## | |
| # import streamlit as st | |
| # from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # # Load model directly | |
| # tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") | |
| # model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") | |
| # # Initialize chat history | |
| # if "chat_history" not in st.session_state: | |
| # st.session_state.chat_history = [] | |
| # # Display chat history | |
| # for chat in st.session_state.chat_history: | |
| # st.write(f"User: {chat['user']}") | |
| # st.write(f"Response: {chat['response']}") | |
| # # Get user input | |
| # user_input = st.text_input("Enter your message:") | |
| # # Generate response | |
| # if st.button("Send"): | |
| # inputs = tokenizer(user_input, return_tensors="pt") | |
| # outputs = model.generate(**inputs) | |
| # response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # st.session_state.chat_history.append({"user": user_input, "response": response}) | |
| # st.write(f"Response: {response}") |