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| import numpy as np | |
| import streamlit as st | |
| from openai import OpenAI | |
| import os | |
| from dotenv import load_dotenv | |
| # Load environment variables | |
| load_dotenv() | |
| # Initialize the Hugging Face client | |
| hf_api_key = os.getenv('HF_API_KEY') # Replace with your Hugging Face API key | |
| openai_api_key = os.getenv('OPENAI_API_KEY') # Replace with your OpenAI API key | |
| client = OpenAI( | |
| api_key=openai_api_key | |
| ) | |
| # Create supported models | |
| model_links = { | |
| "Meta-Llama-3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct", | |
| "Meta-Llama-3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct", | |
| "Meta-Llama-3.1-405B-Instruct-FP8": "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8", | |
| "Meta-Llama-3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct", | |
| "Mistral-Nemo-Instruct-2407": "mistralai/Mistral-Nemo-Instruct-2407", | |
| "Meta-Llama-3-70B-Instruct": "meta-llama/Meta-Llama-3-70B-Instruct", | |
| "Meta-Llama-3-8B-Instruct": "meta-llama/Meta-Llama-3-8B-Instruct", | |
| "C4ai-command-r-plus": "CohereForAI/c4ai-command-r-plus", | |
| "Aya-23-35B": "CohereForAI/aya-23-35B", | |
| "Zephyr-orpo-141b-A35b-v0.1": "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1", | |
| "Mixtral-8x7B-Instruct-v0.1": "mistralai/Mixtral-8x7B-Instruct-v0.1", | |
| "Codestral-22B-v0.1": "mistralai/Codestral-22B-v0.1", | |
| "Nous-Hermes-2-Mixtral-8x7B-DPO": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", | |
| "Yi-1.5-34B-Chat": "01-ai/Yi-1.5-34B-Chat", | |
| "Gemma-2-27b-it": "google/gemma-2-27b-it", | |
| "Meta-Llama-2-70B-Chat-HF": "meta-llama/Llama-2-70b-chat-hf", | |
| "Meta-Llama-2-7B-Chat-HF": "meta-llama/Llama-2-7b-chat-hf", | |
| "Meta-Llama-2-13B-Chat-HF": "meta-llama/Llama-2-13b-chat-hf", | |
| "Mistral-7B-Instruct-v0.1": "mistralai/Mistral-7B-Instruct-v0.1", | |
| "Mistral-7B-Instruct-v0.2": "mistralai/Mistral-7B-Instruct-v0.2", | |
| "Mistral-7B-Instruct-v0.3": "mistralai/Mistral-7B-Instruct-v0.3", | |
| "Falcon-7b-Instruct": "tiiuae/falcon-7b-instruct", | |
| "Starchat2-15b-v0.1": "HuggingFaceH4/starchat2-15b-v0.1", | |
| "Gemma-1.1-7b-it": "google/gemma-1.1-7b-it", | |
| "Gemma-1.1-2b-it": "google/gemma-1.1-2b-it", | |
| "Zephyr-7B-Beta": "HuggingFaceH4/zephyr-7b-beta", | |
| "Zephyr-7B-Alpha": "HuggingFaceH4/zephyr-7b-alpha", | |
| "Phi-3-mini-128k-instruct": "microsoft/Phi-3-mini-128k-instruct", | |
| "Phi-3-mini-4k-instruct": "microsoft/Phi-3-mini-4k-instruct", | |
| } | |
| # 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"] | |
| # Reset conversation | |
| def reset_conversation(): | |
| st.session_state.conversation = [] | |
| st.session_state.messages = [] | |
| # 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("*Generated content may be inaccurate or false.*") | |
| st.sidebar.markdown("\n[TypeGPT](https://typegpt.net).") | |
| # Initialize previous option and messages | |
| 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'TypeGPT.net - {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, | |
| stream=True, | |
| max_tokens=3000, | |
| ) | |
| response = st.write_stream(stream) | |
| except Exception as e: | |
| response = ("šµāš« Looks like someone unplugged something! " | |
| "Either the model space is being updated or something is down. " | |
| "Try again later. Here's a random pic of a š¶:") | |
| 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}") |