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import gradio as gr
from datetime import date
from avidtools.datamodels.report import Report
from avidtools.datamodels.components import *
from avidtools.datamodels.enums import *
# def generate_report():
def generate_report(classof,type,risk_domain,sep,lifecycle):
# def generate_report(scraped_input, selections):
report = Report()
# report.affects = Affects(
# developer = [],
# deployer = ['Hugging Face'],
# artifacts = [Artifact(
# type = ArtifactTypeEnum.model,
# name = model_id
# )]
# )
report.problemtype = Problemtype(
# classof = clas,
classof = classof,
type = type,
description = LangValue(
lang = 'eng',
value = scraped_input['title']
)
)
report.references = [
Reference(
label = scraped_input['description'],
url = scraped_input['url']
)
]
report.description = LangValue(
lang = 'eng',
value = scraped_input['description']
)
report.impact = Impact(
avid = AvidTaxonomy(
risk_domain = risk_domain,
sep_view = sep,
lifecycle_view = lifecycle,
taxonomy_version = '0.2'
)
)
report.reported_date = date.today()
return report.dict()
scraped_input = {
"title": "### title",
"description": "description",
"url": "https://link.to.arxiv.paper"
}
# selections = {
# "classof": ClassEnum.llm,
# "type": TypeEnum.detection,
# "avid": {
# "risk_domain": ["Security"],
# "sep": [SepEnum.E0101],
# "lifecycle": [LifecycleEnum.L05]
# }
# }
demo = gr.Blocks(theme=gr.themes.Soft())
# demo = gr.Blocks(theme='gradio/darkdefault')
with demo:
gr.Markdown("# Report AI Vulnerability Research")
gr.Markdown("""
As language models become more prevalent in day-to-day technology, it's important to develop methods to \
investigate their biases and limitations. To this end, researchers are developing metrics like \
BOLD, HONEST, and WinoBias that calculate scores which represent their tendency to generate "unfair" text across \
different collections of prompts. With the widgets below, you can choose a model and a metric to run your own \
evaluations.
Generating these scores is only half the battle, though! What do you do with these numbers once you've evaluated \
a model? [AVID](https://avidml.org)'s data model makes it easy to collect and communicate your findings with \
structured reports.
""")
with gr.Row():
with gr.Column(scale=2):
gr.Markdown("""
## Step 1: \n\
Select a model and a method of detection.
""")
with gr.Box():
title = gr.Markdown(scraped_input['title'])
description = gr.Markdown(scraped_input['description'])
with gr.Column(scale=3):
gr.Markdown("""## Step 2: \
Categorize your report.""")
classof = gr.Radio(label="Class", choices=[ce.value for ce in ClassEnum])
type = gr.Radio(label="Type", choices=[te.value for te in TypeEnum])
risk_domain = gr.CheckboxGroup(label="Risk Domain", choices=['Security','Ethics','Performance'])
sep = gr.CheckboxGroup(label="Effect Categories", choices=[se.value for se in SepEnum])
lifecycle = gr.CheckboxGroup(label="Lifecycle Categories", choices=[le.value for le in LifecycleEnum])
with gr.Column(scale=5):
gr.Markdown("""
## Step 3: \n\
Generate a report that you can submit to AVID.
The title and abstract get auto-populated from Step 1. The taxonomy categories populate from your selections in Step 2.
""")
report_button = gr.Button("Generate Report")
report_json = gr.Json(label="AVID Report")
report_button.click(
fn=generate_report,
inputs=[classof,type,risk_domain,sep,lifecycle],
outputs=[report_json]
)
demo.launch()
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