# 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 gradio as gr | |
import boto3 | |
import os | |
from langchain.llms import Bedrock | |
from langchain.chains import ConversationChain | |
#from langchain.memory import ConversationBufferWindowMemory | |
from langchain.prompts.prompt import PromptTemplate | |
access_key_id = os.environ['aws_access_key_id'] | |
secret_access_key = os.environ['aws_secret_access_key'] | |
client = 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) | |
template = """ | |
<|begin_of_text|> | |
{history} | |
<|start_header_id|>user<|end_header_id|> | |
{input} | |
<|eot_id|> | |
<|start_header_id|>assistant<|end_header_id|> | |
""" | |
prompt_temp = PromptTemplate(input_variables=["history", "input"], template=template) | |
def generate( | |
prompt_temp, temperature=0.2, max_gen_len=1024, top_p=0.95, | |
): | |
generate_kwargs = dict( | |
temperature=temperature, | |
max_gen_len=max_gen_len, | |
top_p=top_p) | |
llm = Bedrock(model_id="meta.llama3-8b-instruct-v1:0",model_kwargs=generate_kwargs,client=client) | |
conversation = ConversationChain( | |
prompt=prompt_temp, | |
llm=llm, | |
verbose=True, | |
memory= ConversationBufferMemory(ai_prefix="AI Assistant") | |
) | |
chat_history = [] | |
#result =conversation.predict(input="Hi there!") | |
result = conversation({"input": message, "history":chat_history }) | |
chat_history.append((message, result['response'])) | |
return result['response'] | |
demo=gr.ChatInterface(generate) | |
demo.queue().launch(show_api=False) | |
# import gradio as gr | |
# 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'] | |
# 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) | |