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import gradio as gr
import json, os, vertexai, wandb
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
credentials = os.environ["CREDENTIALS"]
project = os.environ["PROJECT"]
wandb_api_key = os.environ["WANDB_API_KEY"]
config = {
"model": "text-bison@001",
}
wandb.login(key = wandb_api_key)
wandb.init(project = "vertex-ai-llm", config = config)
config = wandb.config
credentials = json.loads(credentials)
from google.oauth2 import service_account
credentials = service_account.Credentials.from_service_account_info(credentials)
if credentials.expired:
credentials.refresh(Request())
vertexai.init(project = project,
location = "us-central1",
credentials = credentials)
from vertexai.language_models import TextGenerationModel
generation_model = TextGenerationModel.from_pretrained(config.model)
def invoke(prompt):
completion = generation_model.predict(prompt = prompt).text
wandb.log({"prompt": prompt, "completion": completion})
return completion
description = """<a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://cloud.google.com/vertex-ai?hl=en/'>Google Vertex AI</a> API
with Bison 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() |