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| 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("meta-llama/Meta-Llama-3.1-8B-Instruct") | |
| 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() | |
| ######################################## | |
| # from openai import OpenAI | |
| # import streamlit as st | |
| # import os | |
| # import sys | |
| # from dotenv import load_dotenv, dotenv_values | |
| # load_dotenv() | |
| # st.title("ChatGPT-like clone") | |
| # client = OpenAI(api_key=os.environ.get["OPENAI_API_KEY"]) | |
| # if "openai_model" not in st.session_state: | |
| # st.session_state["openai_model"] = "gpt-3.5-turbo" | |
| # if "messages" not in st.session_state: | |
| # st.session_state.messages = [] | |
| # for message in st.session_state.messages: | |
| # with st.chat_message(message["role"]): | |
| # st.markdown(message["content"]) | |
| # if prompt := st.chat_input("What is up?"): | |
| # st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # with st.chat_message("user"): | |
| # st.markdown(prompt) | |
| # with st.chat_message("assistant"): | |
| # stream = client.chat.completions.create( | |
| # model=st.session_state["openai_model"], | |
| # messages=[ | |
| # {"role": m["role"], "content": m["content"]} | |
| # for m in st.session_state.messages | |
| # ], | |
| # stream=True, | |
| # ) | |
| # response = st.write_stream(stream) | |
| # st.session_state.messages.append({"role": "assistant", "content": response}) |