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# main application file initializing the gradio based ui and calling other
# standard imports
import os
# external imports
from fastapi import FastAPI
import markdown
import gradio as gr
from uvicorn import run
from gradio_iframe import iFrame
# internal imports
from backend.controller import interference
from explanation.markup import color_codes
# global Variables and js/css
# creating FastAPI app and getting color codes
app = FastAPI()
coloring = color_codes()
# defining custom css and js for certain environments
css = """
.examples {text-align: start;}
.seperatedRow {border-top: 1rem solid;}",
"""
# custom js to force light mode in custom environments
if os.environ["HOSTING"].lower() != "spaces":
js = """
function () {
gradioURL = window.location.href
if (!gradioURL.endsWith('?__theme=light')) {
window.location.replace(gradioURL + '?__theme=light');
}
}
"""
else:
js = ""
# different functions to provide frontend abilities
# function to load markdown files
def load_md(path):
# CREDIT: official python-markdown documentation
## see https://python-markdown.github.io/reference/)
with open(path, "r", encoding="utf-8") as file:
text = file.read()
return markdown.markdown(text)
# function to display the system prompt info
def system_prompt_info(sys_prompt_txt):
if sys_prompt_txt == "":
sys_prompt_txt = """
You are a helpful, respectful and honest assistant.
Always answer as helpfully as possible, while being safe.
"""
# display the system prompt using the Gradio Info component
gr.Info(f"The system prompt was set to:\n {sys_prompt_txt}")
# function to display the xai info
def xai_info(xai_radio):
# display the xai method using the Gradio Info component
if xai_radio != "None":
gr.Info(f"The XAI was set to:\n {xai_radio}")
else:
gr.Info("No XAI method was selected.")
def model_info(model_radio):
# displays the selected model using the Gradio Info component
gr.Info(f"The following model was selected:\n {model_radio} ")
# ui interface based on Gradio Blocks
# see https://www.gradio.app/docs/interface
with gr.Blocks(
css=css,
js=js,
title="Thesis Webapp Showcase",
head="<head>",
) as ui:
# header row with markdown based text
with gr.Row():
# markdown component to display the header
gr.Markdown("""
# Thesis Demo - AI Chat Application with GODEL
Interpretability powered by shap and attention visualization,
### Select between tabs below for the different views.
""")
# ChatBot tab used to chat with the AI chatbot
with gr.Tab("AI ChatBot"):
with gr.Row():
# markdown component to display the header of the current tab
gr.Markdown("""
### ChatBot Demo
Chat with the AI ChatBot using the textbox below.
Manipulate the settings in the row above,
including the selection of the model,
the system prompt and the XAI method.
**See Explanations in the accordion above the chat.**
""")
# row with columns for the different settings
with gr.Row(equal_height=True):
with gr.Accordion("Application Settings", open=False):
# column that takes up 3/4 of the row
with gr.Column(scale=2):
# textbox to enter the system prompt
system_prompt = gr.Textbox(
label="System Prompt",
info="Set the models system prompt, dictating how it answers.",
# default system prompt is set to this in the backend
placeholder=(
"You are a helpful, respectful and honest assistant. Always"
" answer as helpfully as possible, while being safe."
),
)
# column that takes up 1/4 of the row
with gr.Column(scale=1):
# checkbox group to select the xai method
xai_selection = gr.Radio(
["None", "SHAP", "Attention"],
label="Interpretability Settings",
info=(
"Select a Interpretability Approach Implementation to use."
),
value="None",
interactive=True,
show_label=True,
)
# column that takes up 1/4 of the row
with gr.Column(scale=1):
# checkbox group to select the xai method
model_selection = gr.Radio(
["GODEL", "Mistral"],
label="Model Settings",
info="Select a Model to use.",
value="Mistral",
interactive=True,
show_label=True,
)
# calling info functions on inputs/submits for different settings
system_prompt.change(system_prompt_info, [system_prompt])
xai_selection.change(xai_info, [xai_selection])
model_selection.change(model_info, [model_selection])
# row with chatbot ui displaying "conversation" with the model
with gr.Row(equal_height=True):
# group to display components closely together
with gr.Group(elem_classes="border: 1px solid black;"):
# accordion to display the normalized input explanation
with gr.Accordion(label="Input Explanation", open=False):
gr.Markdown("""
The explanations are based on 10 buckets that range between the
lowest negative value (1 to 5) and the highest positive attribution value (6 to 10).
**The legend shows the color for each bucket.**
*HINT*: This works best in light mode.
""")
xai_text = gr.HighlightedText(
color_map=coloring,
label="Input Explanation",
show_legend=True,
show_label=False,
)
# out of the box chatbot component with avatar images
# see documentation: https://www.gradio.app/docs/chatbot
chatbot = gr.Chatbot(
layout="panel",
show_copy_button=True,
avatar_images=("./public/human.jpg", "./public/bot.jpg"),
)
# extendable components for extra knowledge
with gr.Accordion(label="Additional Knowledge", open=False):
gr.Markdown("""
*Hint:* Add extra knowledge to see GODEL work the best.
Knowledge doesn't work with Mistral and will be ignored.
""")
# textbox to enter the knowledge
knowledge_input = gr.Textbox(
value="",
label="Knowledge",
max_lines=5,
info="Add additional context knowledge.",
show_label=True,
)
user_prompt = gr.Textbox(
label="Input Message",
max_lines=5,
info="""
Ask the ChatBot a question.
""",
show_label=True,
)
# row with columns for buttons to submit and clear content
with gr.Row(elem_classes=""):
with gr.Column():
# out of the box clear button which clearn the given components (see
# see: https://www.gradio.app/docs/clearbutton)
clear_btn = gr.ClearButton([user_prompt, chatbot])
with gr.Column():
# submit button that calls the backend functions on click
submit_btn = gr.Button("Submit", variant="primary")
# row with content examples that get autofilled on click
with gr.Row(elem_classes="examples"):
with gr.Accordion("Mistral Model Examples", open=False):
# examples util component
# see: https://www.gradio.app/docs/examples
gr.Examples(
label="Example Questions",
examples=[
["Does money buy happiness?", "None", "", "Mistral", ""],
["Does money buy happiness?", "SHAP", "", "Mistral", ""],
["Does money buy happiness?", "Attention", "", "Mistral", ""],
[
"Does money buy happiness?",
"None",
(
"Respond from the perspective of billionaire heir"
" living his best life with his father's money."
),
"Mistral",
"",
],
[
"Does money buy happiness?",
"SHAP",
(
"Respond from the perspective of billionaire heir"
" living his best life with his father's money."
),
"Mistral",
"",
],
[
"Does money buy happiness?",
"Attention",
(
"Respond from the perspective of billionaire heir"
" living his best life with his father's money."
),
"Mistral",
"",
],
],
inputs=[
user_prompt,
xai_selection,
system_prompt,
model_selection,
knowledge_input,
],
)
with gr.Accordion("GODEL Model Examples", open=False):
# examples util component
# see: https://www.gradio.app/docs/examples
gr.Examples(
label="Example Questions",
examples=[
[
"Does money buy happiness?",
"SHAP",
(
"Some studies have found a correlation between income"
" and happiness, but this relationship often has"
" diminishing returns. From a psychological standpoint,"
" it's not just having money, but how it is used that"
" influences happiness."
),
"",
"GODEL",
],
[
"Does money buy happiness?",
"Attention",
(
"Some studies have found a correlation between income"
" and happiness, but this relationship often has"
" diminishing returns. From a psychological standpoint,"
" it's not just having money, but how it is used that"
" influences happiness."
),
"",
"GODEL",
],
[
"Does money buy happiness?",
"Attention",
"",
"",
"GODEL",
],
],
inputs=[
user_prompt,
xai_selection,
knowledge_input,
system_prompt,
model_selection,
],
)
# explanations tab used to provide explanations for a specific conversation
with gr.Tab("Explanations"):
# row with markdown component to display the header of the current tab
with gr.Row():
gr.Markdown("""
### Get Explanations for Conversations
Get additional explanations for the last conversation you had with the AI ChatBot.
Depending on the selected XAI method, different explanations are available.
""")
# row that displays the generated explanation of the model (if applicable)
with gr.Row():
# wraps the explanation html to display it statically
xai_interactive = iFrame(
label="Interactive Explanation",
value=(
'<div style="text-align: center; font-family:arial;"><h4>No Graphic'
" to Display (Yet)</h4></div>"
),
show_label=True,
height="400px",
)
with gr.Row():
with gr.Accordion("Explanation Plot", open=False):
xai_plot = gr.Plot(
label="Input Sequence Attribution Plot", show_label=True
)
# functions to trigger the controller
## takes information for the chat and the xai selection
## returns prompt, history and xai data
## see backend/controller.py for more information
submit_btn.click(
interference,
[
user_prompt,
chatbot,
knowledge_input,
system_prompt,
xai_selection,
model_selection,
],
[user_prompt, chatbot, xai_interactive, xai_text, xai_plot],
)
# function triggered by the enter key
user_prompt.submit(
interference,
[
user_prompt,
chatbot,
knowledge_input,
system_prompt,
xai_selection,
model_selection,
],
[user_prompt, chatbot, xai_interactive, xai_text, xai_plot],
)
# final row to show legal information
## - credits, data protection and link to the License
with gr.Tab(label="About"):
# load about.md markdown
gr.Markdown(value=load_md("public/about.md"))
with gr.Accordion(label="Credits, Data Protection, License"):
# load credits and data protection markdown
gr.Markdown(value=load_md("public/credits_dataprotection_license.md"))
# mount function for fastAPI Application
app = gr.mount_gradio_app(app, ui, path="/")
# launch function to launch the application
if __name__ == "__main__":
# use standard gradio launch option for hgf spaces
if os.environ["HOSTING"].lower() == "spaces":
# set password to deny public access
ui.launch(auth=(os.environ["USER"], os.environ["PW"]))
# otherwise run the application on port 8080 in reload mode
## for local development, uses Docker for Prod deployment
run("main:app", port=8080, reload=True)
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