# 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() ######################################## 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})