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# 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)
prompt = """
<|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,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
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=bedrock_runtime)
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(qa_fn)
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)
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