seandavi

datasets:
  - name: brfss
    title: The brfss dataset
    description: |
      a very long description which can be 
      in [markdown](https://markdown.org).
      - list item
      - list item 2
    processor: readr::read_csv
  - name: svi

seandavi

"""This script scrapes the state cancer profiles website
and saves the results as csv files.

The website is a bit of a mess, so this script is a bit of a mess.
It requires scraping the select options from the website
and then iterating over all the possible combinations of
select options to get the data.

The script is designed to be run from the command line
with the following command:

seandavi

See: https://cloud.google.com/vertex-ai/docs/generative-ai/chat/test-chat-prompts

You'll need:

1. A project id
2. An installed and working gcloud command-line tool
3. A service account that has been granted access to vertex AI
4. A json key file associated with the service account
5. Set up local environment to use the json key for authentication

Tasks 1, 3, and 4 can be performed using the console OR using gcloud by a user authorized to create resources. Note that the

seandavi

from python:3.11

RUN pip install --upgrade pip
RUN pip install omicidx
RUN pip install fsspec gcsfs s3fs

COPY abc.py .

seandavi

library(httr2)
ChatGPT <- function(prompt='Tell me about Bioconductor') {
  base_request = httr2::request('https://openai-playground-asdf.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2023-03-15-preview')
  res = base_request |>
    httr2::req_headers('api-key'=Sys.getenv('API_KEY')) |>
    httr2::req_body_json(list(
        messages=
          list(
            list(role="system",content="You are a helpful AI assistant"),
            list(role="user", content=prompt)

seandavi

digraph ChatGPT {
    node [shape=box]
    subgraph cluster_0 {
        label="Microsoft Asure"
        style=dashed
        ChatGPT 
    }
    subgraph cluster_1 {
        label="On Prem or Cloud"
        style="dashed"

seandavi

library(alabaster)
library(AnnotationHub)
library(jsonlite)
ah = AnnotationHub()

STAGE_DIR = '/tmp/ah_staging'
dir.create(STAGE_DIR, showWarnings = FALSE)

write_error = function(dirname, e) {
  jsonlite::write_json(list(message=jsonlite::unbox(e$message)), file.path(dirname,'error.json'))

seandavi

Meetings

Summary from zoom transcript

You are an AI assistant that summarizes meeting notes generated by Zoom. For the meeting notes provided as input, provide these items if possible from the content:

• meeting date, time, and title
• meeting attendees as a bulleted list
• executive summary (3-5 sentences)

seandavi

# Place the cursor inside "graph" to get some refactoring options

digraph G {

	fontname="Helvetica,Arial,sans-serif"
	node [fontname="Helvetica,Arial,sans-serif"]
	edge [fontname="Helvetica,Arial,sans-serif"]
    edge[color="#00000050"]

	subgraph cluster_0 {

seandavi

I would like you to describe the UCSC genome browser table called "cytoBand". The first few lines of the table are here:      
  
chrom	chromStart	chromEnd	name	gieStain      
chr1	0	2300000	p36.33	gneg      
chr1	2300000	5300000	p36.32	gpos25      
chr1	5300000	7100000	p36.31	gneg      
chr1	7100000	9100000	p36.23	gpos25      
chr1	9100000	12500000	p36.22	gneg      
chr1	12500000	15900000	p36.21	gpos50      
chr1	15900000	20100000	p36.13	gneg