| # 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 ={ | |
| # "Mistral-7b":"mistralai/Mistral-7B-Instruct-v0.2", | |
| # "Mistral-8x7b":"mistralai/Mixtral-8x7B-Instruct-v0.1" | |
| # # "Gemma-7B":"google/gemma-7b-it", | |
| # # "Gemma-2B":"google/gemma-2b-it", | |
| # # "Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta", | |
| # } | |
| # #Pull info about the model to display | |
| # model_info ={ | |
| # "Mistral-7b": | |
| # {'description':"""The Mistral 7B 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-8x7b": | |
| # {'description':"""The Mistral 8x7B 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-8x7b/) team as has based on MOE arch.** \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 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.*") | |
| # # st.sidebar.markdown("\nLearn how to build this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).") | |
| # # st.sidebar.markdown("\nRun into issues? Try the [back-up](https://huggingface.co/spaces/ngebodh/SimpleChatbot-Backup).") | |
| # 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"): | |
| # 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) | |
| # st.session_state.messages.append({"role": "assistant", "content": response}) | |
| # from huggingface_hub import InferenceClient | |
| # import gradio as gr | |
| # client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2") | |
| # def format_prompt(message, history): | |
| # prompt = "<s>" | |
| # for user_prompt, bot_response in history: | |
| # prompt += f"[INST] {user_prompt} [/INST]" | |
| # prompt += f" {bot_response}</s> " | |
| # prompt += f"[INST] {message} [/INST]" | |
| # return prompt | |
| # def generate( | |
| # prompt, history, temperature=0.2, max_new_tokens=3000, top_p=0.95, repetition_penalty=1.0, | |
| # ): | |
| # temperature = float(temperature) | |
| # if temperature < 1e-2: | |
| # temperature = 1e-2 | |
| # top_p = float(top_p) | |
| # generate_kwargs = dict( | |
| # temperature=temperature, | |
| # max_new_tokens=max_new_tokens, | |
| # top_p=top_p, | |
| # repetition_penalty=repetition_penalty, | |
| # do_sample=True, | |
| # seed=42, | |
| # ) | |
| # formatted_prompt = format_prompt(prompt, history) | |
| # stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) | |
| # output = "" | |
| # for response in stream: | |
| # output += response.token.text | |
| # yield output | |
| # return output | |
| # mychatbot = gr.Chatbot( | |
| # avatar_images=["./user.png", "./bot.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,) | |
| # demo = gr.ChatInterface(fn=generate, | |
| # chatbot=mychatbot, | |
| # title="Mistral-Chat", | |
| # retry_btn=None, | |
| # undo_btn=None | |
| # ) | |
| # demo.queue().launch(show_api=False) | |
| import boto3 | |
| import json | |
| from botocore.exceptions import ClientError | |
| import os | |
| access_key_id = os.environ['aws_access_key_id'] | |
| secret_access_key = os.environ['aws_secret_access_key'] | |
| import gradio as gr | |
| bedrock = boto3.client(service_name='bedrock-runtime',region_name='us-east-1',aws_access_key_id=access_key_id,aws_secret_access_key=secret_access_key) | |
| def invoke_llama3_8b(user_message): | |
| try: | |
| # Set the model ID, e.g., Llama 3 8B Instruct. | |
| model_id = "meta.llama3-8b-instruct-v1:0" | |
| # Embed the message in Llama 3's prompt format. | |
| prompt = f""" | |
| <|begin_of_text|> | |
| <|start_header_id|>user<|end_header_id|> | |
| {user_message} | |
| <|eot_id|> | |
| <|start_header_id|>assistant<|end_header_id|> | |
| """ | |
| # Format the request payload using the model's native structure. | |
| request = { | |
| "prompt": prompt, | |
| # Optional inference parameters: | |
| "max_gen_len": 1024, | |
| "temperature": 0.6, | |
| "top_p": 0.9, | |
| } | |
| # Encode and send the request. | |
| response = bedrock.invoke_model(body=json.dumps(request), modelId=model_id) | |
| # Decode the native response body. | |
| model_response = json.loads(response["body"].read()) | |
| # Extract and print the generated text. | |
| response_text = model_response["generation"] | |
| return response_text | |
| except ClientError: | |
| print("Couldn't invoke llama3 8B") | |
| raise | |
| mychatbot = gr.Chatbot( | |
| avatar_images=["./user.png", "./bot.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,) | |
| demo = gr.ChatInterface(fn=invoke_llama3_8b, | |
| chatbot=mychatbot, | |
| title="llama3-Chat", | |
| retry_btn=None, | |
| undo_btn=None | |
| ) | |
| demo.queue().launch(show_api=False) | |