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import gradio as gr
import boto3, json, os, wandb
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
aws_access_key_id = os.environ["AWS_ACCESS_KEY_ID"]
aws_secret_access_key = os.environ["AWS_SECRET_ACCESS_KEY"]
wandb_api_key = os.environ["WANDB_API_KEY"]
config = {
"max_tokens_to_sample": 300,
"model": "anthropic.claude-v2",
"temperature": 0,
"top_k": 250,
"top_p": 0.999,
}
wandb.login(key = wandb_api_key)
wandb.init(project = "bedrock-txt", config = config)
config = wandb.config
bedrock_runtime = boto3.client(
aws_access_key_id = aws_access_key_id,
aws_secret_access_key = aws_secret_access_key,
service_name = "bedrock-runtime",
region_name = "us-west-2"
)
def invoke(prompt):
body = json.dumps({"prompt": "\n\nHuman: " + prompt + "\n\nAssistant: ",
"max_tokens_to_sample": config.max_tokens_to_sample,
"temperature": config.temperature,
"top_k": config.top_k,
"top_p": config.top_p,
"stop_sequences": ["\n\nHuman: "]
})
modelId = config.model
accept = "application/json"
contentType = "application/json"
response = bedrock_runtime.invoke_model(body = body, modelId = modelId, accept = accept, contentType = contentType)
response_body = json.loads(response.get("body").read())
completion = response_body["completion"]
wandb.log({"prompt": prompt, "completion": completion})
return completion
description = """<a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://aws.amazon.com/bedrock/'>Amazon Bedrock</a> API
with <a href='https://www.anthropic.com/'>Anthropic</a> Claude 2 foundation model.
Model performance evaluation via <a href='https://wandb.ai/bstraehle'>Weights & Biases</a>."""
gr.close_all()
demo = gr.Interface(fn=invoke,
inputs = [gr.Textbox(label = "Prompt", lines = 1)],
outputs = [gr.Textbox(label = "Completion", lines = 1)],
title = "Generative AI - LLM",
description = description)
demo.launch() |