from typing import Optional, Tuple
from pathlib import Path
import gradio as gr
from fairsenseai.analysis.ai_safety_dashboard import display_ai_safety_dashboard
from fairsenseai.analysis.bias import (
analyze_image_for_bias,
analyze_images_batch,
analyze_text_csv,
analyze_text_for_bias,
)
from fairsenseai.analysis.risk_assessment import analyze_text_for_risks
from fairsenseai.runtime import get_runtime
[docs]
def start_server(
make_public_url: Optional[bool] = True,
allow_filesystem_access: Optional[bool] = True,
prevent_thread_lock: Optional[bool] = False,
launch_browser_on_startup: Optional[bool] = False,
) -> None:
"""
Starts the Gradio server with multiple tabs for text analysis, image analysis,
batch processing, AI governance insights, and an AI safety risks dashboard.
Parameters
----------
make_public_url
Whether to make the server publicly accessible.
allow_filesystem_access
Whether to allow filesystem access for file uploads, required to save results
prevent_thread_lock
Whether to prevent thread lock issues.
launch_browser_on_startup
Whether to launch the browser on server startup.
Returns
-------
None
Example
-------
>>> start_server()
"""
# Initialize the runtime
get_runtime(allow_filesystem_access=allow_filesystem_access)
script_dir = Path(__file__).resolve().parent
ui_dir = script_dir / "ui"
with open(ui_dir / "home.html") as home_file:
home = home_file.read()
with open(ui_dir / "footer.html") as footer_file:
footer = footer_file.read()
with open(ui_dir / "about.html") as page_file:
about = page_file.read()
demo = gr.Blocks()
with demo:
gr.HTML(home)
gr.HTML(footer)
with demo.route("Bias Identification", "/bias"):
gr.Markdown("# Bias Identification", elem_classes="page-title")
with gr.Tabs():
# --- Text Analysis Tab ---
with gr.TabItem("📄 Text Analysis"):
with gr.Row():
text_input = gr.Textbox(
lines=5,
placeholder="Enter text to analyze for bias",
label="Text Input",
)
# Summarizer toggle for text analysis
use_summarizer_checkbox_text = gr.Checkbox(
value=True,
label="Use Summarizer?",
)
analyze_button = gr.Button("Analyze")
# Examples
gr.Examples(
examples=[
"Some people say that women are not suitable for leadership roles.",
"Our hiring process is fair and unbiased, but we prefer male candidates "
"for their intellect level.",
],
inputs=text_input,
label="Try some examples",
)
highlighted_text = gr.HTML(label="Highlighted Text")
detailed_analysis = gr.HTML(label="Detailed Analysis")
analyze_button.click(
analyze_text_for_bias,
inputs=[text_input, use_summarizer_checkbox_text],
outputs=[highlighted_text, detailed_analysis],
show_progress=True,
)
# --- Image Analysis Tab ---
with gr.TabItem("🖼️ Image Analysis"):
with gr.Row():
image_input = gr.Image(type="pil", label="Upload Image")
# Summarizer toggle for image analysis
use_summarizer_checkbox_img = gr.Checkbox(
value=True,
label="Use Summarizer?",
)
analyze_image_button = gr.Button("Analyze")
# Example images
gr.Markdown(
"""
### Example Images
You can download the following images and upload them to test the analysis:
- [Example 1](https://media.top1000funds.com/wp-content/uploads/2019/12/iStock-525807555.jpg)
- [Example 2](https://ichef.bbci.co.uk/news/1536/cpsprodpb/BB60/production/_115786974_d6bbf591-ea18-46b9-821b-87b8f8f6006c.jpg)
"""
)
highlighted_caption = gr.HTML(label="Highlighted Text and Caption")
image_analysis = gr.HTML(label="Detailed Analysis")
analyze_image_button.click(
analyze_image_for_bias,
inputs=[image_input, use_summarizer_checkbox_img],
outputs=[highlighted_caption, image_analysis],
show_progress=True,
)
# --- Batch Text CSV Analysis Tab ---
with gr.TabItem("📂 Batch Text CSV Analysis"):
with gr.Row():
csv_input = gr.File(
label="Upload Text CSV (with 'text' column)",
file_types=[".csv"],
)
# Summarizer toggle for batch text CSV
use_summarizer_checkbox_text_csv = gr.Checkbox(
value=True,
label="Use Summarizer?",
)
analyze_csv_button = gr.Button("Analyze CSV")
csv_results = gr.HTML(label="CSV Analysis Results")
analyze_csv_button.click(
analyze_text_csv,
inputs=[csv_input, use_summarizer_checkbox_text_csv],
outputs=csv_results,
show_progress=True,
)
# --- Batch Image Analysis Tab ---
with gr.TabItem("🗂️ Batch Image Analysis"):
with gr.Row():
images_input = gr.File(
label="Upload Images (multiple allowed)",
file_types=["image"],
type="filepath",
file_count="multiple",
)
# Summarizer toggle for batch image
use_summarizer_checkbox_img_batch = gr.Checkbox(
value=True,
label="Use Summarizer?",
)
analyze_images_button = gr.Button("Analyze Images")
images_results = gr.HTML(label="Image Batch Analysis Results")
analyze_images_button.click(
analyze_images_batch,
inputs=[images_input, use_summarizer_checkbox_img_batch],
outputs=images_results,
show_progress=True,
)
gr.HTML(footer)
with demo.route("Risk Management", "/risk"):
gr.Markdown("# Risk Management", elem_classes="page-title")
with gr.Tabs():
with gr.TabItem("📄 Risk Identification and Mitigation"):
with gr.Row():
text_input = gr.Textbox(
lines=5,
placeholder="Enter text to analyze for bias",
label="Text Input",
scale=4,
)
analyze_button = gr.Button("Analyze Risks", scale=1)
# Examples
gr.Examples(
examples=[
"Our team is creating a healthcare chatbot that analyzes patient symptoms using electronic "
"health records. It provides early diagnoses, treatment advice, and can access sensitive "
"patient information. We must handle data security, privacy, and potential misdiagnoses.",
"We’re building an AI-powered facial recognition tool to improve workplace security. "
"It will monitor employee entrances, verify identities in real-time, and store face embeddings."
" The system must comply with privacy regulations and handle sensitive biometrics.",
],
inputs=text_input,
label="Try some examples",
)
csv_output_file = gr.File(
label="Risks and Outcomes Traceability Matrix"
)
highlighted_text = gr.HTML(label="Highlighted Text")
analyze_button.click(
analyze_text_for_risks,
inputs=text_input,
outputs=[highlighted_text, csv_output_file],
show_progress=True,
)
gr.Markdown(
"""
**Useful References:**
- [MIT AI Risk Repository](https://airisk.mit.edu/)
- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)
"""
)
# --- AI Safety Risks Dashboard ---
with gr.TabItem("📊 AI Safety Risks Dashboard"):
fig_bar, fig_pie, fig_scatter, df = display_ai_safety_dashboard()
gr.Markdown("### Percentage Distribution of AI Safety Risks")
gr.Plot(fig_bar)
# If you'd like to show the pie chart, you can uncomment:
# gr.Markdown("### Proportion of Risk Categories")
# gr.Plot(fig_pie)
gr.Markdown("### Severity vs. Likelihood of AI Risks")
gr.Plot(fig_scatter)
gr.Markdown("### AI Safety Risks Data")
gr.Dataframe(df)
gr.HTML(footer)
with demo.route("About FairSense-AI", "/about"):
gr.Markdown("# About FairSense-AI", elem_classes="page-title")
gr.HTML(value=about)
gr.HTML(footer)
demo.queue().launch(
share=make_public_url,
prevent_thread_lock=prevent_thread_lock,
inbrowser=launch_browser_on_startup,
)
if __name__ == "__main__":
start_server()