import streamlit as st from huggingface_hub import InferenceClient import os import sys st.title("ChatGPT-like Chatbot") base_url="https://api-inference.huggingface.co/models/" API_KEY = os.environ.get('HUGGINGFACE_API_KEY') headers = {"Authorization":"Bearer "+API_KEY} model_links ={ "Mistral-7B":base_url+"mistralai/Mistral-7B-Instruct-v0.2", "Mistral-22B":base_url+"mistral-community/Mixtral-8x22B-v0.1", # "Gemma-2B":base_url+"google/gemma-2b-it", # "Zephyr-7B-β":base_url+"HuggingFaceH4/zephyr-7b-beta", # "Llama-2":"meta-llama/Llama-2-7b-chat-hf" } #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'}, "Mistral-22B": {'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-22b/) team as has over **22 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'}, } def format_promt(message, custom_instructions=None): prompt = "" if custom_instructions: prompt += f"[INST] {custom_instructions} [/INST]" prompt += f"[INST] {message} [/INST]" return prompt def reset_conversation(): ''' Resets Conversation ''' st.session_state.conversation = [] st.session_state.messages = [] return None 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.*") st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).") 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}') # 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"): custom_instruction = "Act like a Human in conversation" # 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}) formated_text = format_promt(prompt, custom_instruction) # Display assistant response in chat message container with st.chat_message("assistant"): client = InferenceClient( model=model_links[selected_model], headers=headers) output = client.text_generation( formated_text, temperature=temp_values,#0.5 max_new_tokens=3000, stream=True ) response = st.write_stream(output) st.session_state.messages.append({"role": "assistant", "content": response})