A tool for generating and analyzing natural language prompts using multiple language models.
""")
with gr.Row():
with gr.Column(scale=3):
input_dataframe = gr.DataFrame(
label="Input Examples",
headers=["Input", "Output"],
value=[],
datatype=["str", "str"],
column_widths=["50%", "50%"],
row_count=(1, "dynamic"),
col_count=(2, "fixed"),
interactive=True,
wrap=True
)
with gr.Column(scale=1, min_width=100):
with gr.Group():
editable_checkbox = gr.Checkbox(label="Editable", value=True)
json_file_object = gr.File(
label="Import/Export JSON", file_types=[".json"], type="filepath",
min_width=80
)
export_button = gr.Button("Export to JSON")
clear_inputs_button = gr.ClearButton(
[
input_dataframe
],
value="Clear Inputs"
)
with gr.Row():
with gr.Column(scale=3):
selected_example_input = gr.Textbox(
label="Selected Example Input",
lines=2,
show_copy_button=True,
value="",
)
selected_example_output = gr.Textbox(
label="Selected Example Output",
lines=2,
show_copy_button=True,
value="",
)
with gr.Column(scale=1, min_width=100):
selected_group_mode = gr.State(None) # None, "update", "append"
selected_group_index = gr.State(None) # None, int
selected_group_input = gr.State("")
selected_group_output = gr.State("")
selected_group_input.change(
fn=lambda x: x,
inputs=[selected_group_input],
outputs=[selected_example_input],
)
selected_group_output.change(
fn=lambda x: x,
inputs=[selected_group_output],
outputs=[selected_example_output],
)
with (selected_input_group := gr.Group(visible=False)):
with gr.Row():
selected_row_index = gr.Number(
label="Selected Row Index", value=0, precision=0, interactive=False, visible=False
)
update_row_button = gr.Button(
"Update Selected Row", variant="secondary", visible=False
)
delete_row_button = gr.Button(
"Delete Selected Row", variant="secondary", visible=False
)
append_example_button = gr.Button(
"Append to Input Examples", variant="secondary", visible=False
)
update_row_button.click(
fn=update_selected_dataframe_row,
inputs=[
selected_example_input,
selected_example_output,
selected_row_index,
input_dataframe,
],
outputs=[
input_dataframe,
selected_group_mode,
selected_group_index,
selected_group_input,
selected_group_output,
],
)
delete_row_button.click(
fn=delete_selected_dataframe_row,
inputs=[selected_row_index, input_dataframe],
outputs=[
input_dataframe,
selected_group_mode,
selected_group_index,
selected_group_input,
selected_group_output,
],
)
append_example_button.click(
fn=append_example_to_input_dataframe,
inputs=[
selected_example_input,
selected_example_output,
input_dataframe,
],
outputs=[
input_dataframe,
selected_group_mode,
selected_group_index,
selected_group_input,
selected_group_output,
],
)
selected_group_mode.change(
fn=lambda mode: [
gr.update(visible=(mode is not None)),
gr.update(visible=(mode == "update")),
gr.update(visible=(mode == "update")),
gr.update(visible=(mode == "update")),
gr.update(visible=(mode == "append")),
],
inputs=[selected_group_mode],
outputs=[selected_input_group, selected_row_index, update_row_button, delete_row_button, append_example_button],
)
selected_group_index.change(
fn=lambda index: gr.update(value=index),
inputs=[selected_group_index],
outputs=[selected_row_index],
)
with gr.Tabs() as tabs:
with gr.Tab("Scope"):
with gr.Row():
scope_submit_button = gr.Button("Generate", variant="primary", interactive=False)
scope_clear_button = gr.ClearButton(
[
],
value="Clear Outputs"
)
examples_output_dataframe = gr.DataFrame(
# label="Examples",
headers=["Input", "Output"],
interactive=False,
datatype=["str", "str"],
column_widths=["50%", "50%"],
row_count=(1, "dynamic"),
col_count=(2, "fixed"),
wrap=True
)
with gr.Accordion("Model Settings", open=False):
scope_model_name = gr.Dropdown(
label="Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
temperature = gr.Slider(
label="Temperature", value=1.0, minimum=0.0, maximum=1.0, step=0.1
)
generating_batch_size = gr.Slider(
label="Generating Batch Size", value=3, minimum=1, maximum=10, step=1
)
with gr.Accordion("Analysis", open=False):
with gr.Row():
with gr.Column():
generate_description_button = gr.Button(
"Generate Description", variant="secondary"
)
description_output = gr.Textbox(
label="Description", lines=5, show_copy_button=True
)
with gr.Column():
# Suggestions components
generate_suggestions_button = gr.Button(
"Generate Suggestions", variant="secondary")
suggestions_output = gr.Dropdown(
label="Suggestions", choices=[], multiselect=True, allow_custom_value=True)
apply_suggestions_button = gr.Button(
"Apply Suggestions", variant="secondary")
with gr.Row():
with gr.Column():
analyze_input_button = gr.Button(
"Analyze Input", variant="secondary"
)
input_analysis_output = gr.Textbox(
label="Input Analysis", lines=5, show_copy_button=True
)
with gr.Column():
generate_briefs_button = gr.Button(
"Generate Briefs", variant="secondary"
)
example_briefs_output = gr.Textbox(
label="Example Briefs", lines=5, show_copy_button=True
)
with gr.Row():
with gr.Column():
generate_examples_directly_button = gr.Button(
"Generate Examples Directly", variant="secondary"
)
examples_directly_output_dataframe = gr.DataFrame(
label="Examples Directly",
headers=["Input", "Output"],
interactive=False,
datatype=["str", "str"],
column_widths=["50%", "50%"],
row_count=(1, "dynamic"),
col_count=(2, "fixed"),
wrap=True
)
with gr.Column():
generate_examples_from_briefs_button = gr.Button(
"Generate Examples from Briefs", variant="secondary"
)
examples_from_briefs_output_dataframe = gr.DataFrame(
label="Examples from Briefs",
headers=["Input", "Output"],
interactive=False,
datatype=["str", "str"],
column_widths=["50%", "50%"],
row_count=(1, "dynamic"),
col_count=(2, "fixed"),
wrap=True
)
scope_clear_button.add(
[
description_output,
suggestions_output,
examples_directly_output_dataframe,
input_analysis_output,
example_briefs_output,
examples_from_briefs_output_dataframe,
examples_output_dataframe
]
)
with gr.Tab("Prompt"):
with gr.Row():
prompt_submit_button = gr.Button(value="Submit", variant="primary", interactive=False)
prompt_clear_button = gr.ClearButton(value='Clear Output')
with gr.Row():
with gr.Column():
with gr.Accordion("Initial System Message & Acceptance Criteria", open=False):
with gr.Group():
initial_system_message_input = gr.Textbox(
label="Initial System Message",
show_copy_button=True,
value=""
)
with gr.Row():
evaluate_initial_system_message_button = gr.Button(
value="Evaluate",
variant="secondary",
interactive=False
)
generate_initial_system_message_button = gr.Button(
value="Generate",
variant="secondary",
interactive=False
)
pull_task_description_output_button = gr.Button(
value="→ Pull Description", variant="secondary")
pull_system_message_output_button = gr.Button(
value="Pull Output ←", variant="secondary")
with gr.Group():
acceptance_criteria_input = gr.Textbox(
label="Acceptance Criteria (Compared with Expected Output [EO])",
show_copy_button=True
)
with gr.Row():
evaluate_acceptance_criteria_input_button = gr.Button(
value="Evaluate",
variant="secondary",
interactive=False
)
generate_acceptance_criteria_button = gr.Button(
value="Generate",
variant="secondary",
interactive=False
)
pull_acceptance_criteria_output_button = gr.Button(
value="Pull Output ←", variant="secondary")
recursion_limit_input = gr.Number(
label="Recursion Limit",
value=config.recursion_limit,
precision=0,
minimum=1,
maximum=config.recursion_limit_max,
step=1
)
max_output_age = gr.Number(
label="Max Output Age",
value=config.max_output_age,
precision=0,
minimum=1,
maximum=config.max_output_age_max,
step=1
)
prompt_template_group = gr.Dropdown(
label="Prompt Template Group",
choices=list(config.prompt_templates.keys()),
value=list(config.prompt_templates.keys())[0]
)
aggressive_exploration = gr.Checkbox(
label="Aggressive Exploration",
value=config.aggressive_exploration
)
with gr.Row():
with gr.Tabs() as llm_tabs:
with gr.Tab('Simple') as simple_llm_tab:
simple_model_name_input = gr.Dropdown(
label="Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
with gr.Tab('Advanced') as advanced_llm_tab:
advanced_optimizer_model_name_input = gr.Dropdown(
label="Optimizer Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
advanced_executor_model_name_input = gr.Dropdown(
label="Executor Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
with gr.Tab('Expert') as expert_llm_tab:
with gr.Row():
expert_prompt_initial_developer_model_name_input = gr.Dropdown(
label="Initial Developer Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
expert_prompt_initial_developer_temperature_input = gr.Number(
label="Initial Developer Temperature", value=0.1,
precision=1, minimum=0, maximum=1, step=0.1,
interactive=True)
with gr.Row():
expert_prompt_acceptance_criteria_model_name_input = gr.Dropdown(
label="Acceptance Criteria Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
expert_prompt_acceptance_criteria_temperature_input = gr.Number(
label="Acceptance Criteria Temperature", value=0.1,
precision=1, minimum=0, maximum=1, step=0.1,
interactive=True)
with gr.Row():
expert_prompt_developer_model_name_input = gr.Dropdown(
label="Developer Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
expert_prompt_developer_temperature_input = gr.Number(
label="Developer Temperature", value=0.1,
precision=1, minimum=0, maximum=1, step=0.1,
interactive=True)
with gr.Row():
expert_prompt_executor_model_name_input = gr.Dropdown(
label="Executor Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
expert_prompt_executor_temperature_input = gr.Number(
label="Executor Temperature", value=0.1,
precision=1, minimum=0, maximum=1, step=0.1,
interactive=True)
with gr.Row():
expert_output_history_analyzer_model_name_input = gr.Dropdown(
label="History Analyzer Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
expert_output_history_analyzer_temperature_input = gr.Number(
label="History Analyzer Temperature", value=0.1,
precision=1, minimum=0, maximum=1, step=0.1,
interactive=True)
with gr.Row():
expert_prompt_analyzer_model_name_input = gr.Dropdown(
label="Analyzer Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
expert_prompt_analyzer_temperature_input = gr.Number(
label="Analyzer Temperature", value=0.1,
precision=1, minimum=0, maximum=1, step=0.1,
interactive=True)
with gr.Row():
expert_prompt_suggester_model_name_input = gr.Dropdown(
label="Suggester Model Name",
choices=config.llms.keys(),
value=list(config.llms.keys())[0],
)
expert_prompt_suggester_temperature_input = gr.Number(
label="Suggester Temperature", value=0.1,
precision=1, minimum=0, maximum=1, step=0.1,
interactive=True)
with gr.Column():
with gr.Group():
system_message_output = gr.Textbox(
label="System Message", show_copy_button=True)
with gr.Row():
evaluate_system_message_button = gr.Button(
value="Evaluate", variant="secondary", interactive=False)
output_output = gr.Textbox(
label="Output", show_copy_button=True)
with gr.Group():
acceptance_criteria_output = gr.Textbox(
label="Acceptance Criteria", show_copy_button=True)
evaluate_acceptance_criteria_output_button = gr.Button(
value="Evaluate", variant="secondary", interactive=False)
analysis_output = gr.Textbox(
label="Analysis", show_copy_button=True)
flag_button = gr.Button(
value="Flag", variant="secondary", visible=config.allow_flagging, interactive=False)
with gr.Accordion("Details", open=False, visible=config.verbose):
logs_chatbot = gr.Chatbot(
label='Messages', show_copy_button=True, layout='bubble',
bubble_full_width=False, render_markdown=False
)
clear_logs_button = gr.ClearButton(
[logs_chatbot], value='Clear Logs')
# Load examples
examples = gr.Examples(config.examples_path, inputs=[
selected_example_input,
selected_example_output,
])
prompt_model_tab_state = gr.State(value='Simple')
model_name_states = {
# None | str
"initial_developer": gr.State(value=simple_model_name_input.value),
# None | str
"acceptance_criteria": gr.State(value=simple_model_name_input.value),
# None | str
"developer": gr.State(value=simple_model_name_input.value),
# None | str
"executor": gr.State(value=simple_model_name_input.value),
# None | str
"history_analyzer": gr.State(value=simple_model_name_input.value),
# None | str
"analyzer": gr.State(value=simple_model_name_input.value),
# None | str
"suggester": gr.State(value=simple_model_name_input.value)
}
model_temperature_states = {
"initial_developer": gr.State(value=config.default_llm_temperature),
"acceptance_criteria": gr.State(value=config.default_llm_temperature),
"developer": gr.State(value=config.default_llm_temperature),
"executor": gr.State(value=config.default_llm_temperature),
"history_analyzer": gr.State(value=config.default_llm_temperature),
"analyzer": gr.State(value=config.default_llm_temperature),
"suggester": gr.State(value=config.default_llm_temperature)
}
config_state = gr.State(value=config)
scope_inputs_ready_state = gr.State(value=False)
prompt_inputs_ready_state = gr.State(value=False)
# event handlers for inputs
editable_checkbox.change(
fn=lambda x: gr.update(interactive=x),
inputs=[editable_checkbox],
outputs=[input_dataframe],
)
clear_inputs_button.add(
[selected_group_input, selected_example_output, selected_group_index, selected_group_mode]
)
# set up event handlers for the scope tab
def valid_input_dataframe(x):
# validate it's not empty and not all the values are ''
return not x.empty and not x.isnull().any().any() and not x.eq('').any().any()
input_dataframe.change(
fn=valid_input_dataframe, # input_dataframe has at least 1 data row and no NaN values
inputs=[input_dataframe],
outputs=[scope_inputs_ready_state],
)
scope_inputs_ready_state.change(
fn=lambda x: [gr.update(interactive=x)] * 5,
inputs=[scope_inputs_ready_state],
outputs=[scope_submit_button, generate_description_button,
generate_examples_directly_button, analyze_input_button, generate_briefs_button],
)
json_file_object.change(
fn=import_json_data,
inputs=[json_file_object, input_dataframe],
outputs=[input_dataframe],
)
export_button.click(
fn=export_json_data,
inputs=[input_dataframe],
outputs=[json_file_object],
)
scope_submit_button.click(
fn=process_json_data,
inputs=[
config_state,
input_dataframe,
scope_model_name,
generating_batch_size,
temperature,
],
outputs=[
description_output,
suggestions_output,
examples_directly_output_dataframe,
input_analysis_output,
example_briefs_output,
examples_from_briefs_output_dataframe,
examples_output_dataframe,
],
)
generate_description_button.click(
fn=generate_description,
inputs=[
config_state,
input_dataframe,
scope_model_name,
temperature
],
outputs=[description_output, suggestions_output],
)
generate_examples_directly_button.click(
fn=generate_examples_from_description,
inputs=[
config_state,
description_output,
input_dataframe,
generating_batch_size,
scope_model_name,
temperature,
],
outputs=[examples_directly_output_dataframe],
)
analyze_input_button.click(
fn=analyze_input_data,
inputs=[config_state, description_output, scope_model_name, temperature],
outputs=[input_analysis_output],
)
generate_briefs_button.click(
fn=generate_example_briefs,
inputs=[
config_state,
description_output,
input_analysis_output,
generating_batch_size,
scope_model_name,
temperature,
],
outputs=[example_briefs_output],
)
generate_examples_from_briefs_button.click(
fn=generate_examples_using_briefs,
inputs=[
config_state,
description_output,
example_briefs_output,
input_dataframe,
generating_batch_size,
scope_model_name,
temperature,
],
outputs=[examples_from_briefs_output_dataframe],
)
input_dataframe.select(
fn=format_selected_input_example_dataframe,
inputs=[input_dataframe],
outputs=[
selected_group_mode,
selected_group_index,
selected_group_input,
selected_group_output,
],
)
examples_directly_output_dataframe.select(
fn=format_selected_example,
inputs=[examples_directly_output_dataframe],
outputs=[
selected_group_mode,
selected_group_index,
selected_group_input,
selected_group_output,
],
)
examples_from_briefs_output_dataframe.select(
fn=format_selected_example,
inputs=[examples_from_briefs_output_dataframe],
outputs=[
selected_group_mode,
selected_group_index,
selected_group_input,
selected_group_output,
],
)
examples_output_dataframe.select(
fn=format_selected_example,
inputs=[examples_output_dataframe],
outputs=[
selected_group_mode,
selected_group_index,
selected_group_input,
selected_group_output,
],
)
input_dataframe.change(
fn=input_dataframe_change,
inputs=[
input_dataframe,
selected_group_mode,
selected_group_index,
selected_group_input,
selected_group_output,
],
outputs=[
selected_group_mode,
selected_group_index,
selected_group_input,
selected_group_output,
],
)
generate_suggestions_button.click(
fn=generate_suggestions,
inputs=[config_state, description_output, input_dataframe, scope_model_name, temperature],
outputs=[suggestions_output],
)
apply_suggestions_button.click(
fn=apply_suggestions,
inputs=[config_state, description_output, suggestions_output,
input_dataframe, scope_model_name, temperature],
outputs=[description_output, suggestions_output],
)
# set up event handlers for the prompt tab
for item in [selected_example_input, selected_example_output]:
item.change(
fn=lambda x, y: all(v is not None and v != '' for v in [x, y]),
inputs=[selected_example_input, selected_example_output],
outputs=[prompt_inputs_ready_state],
)
prompt_inputs_ready_state.change(
fn=lambda x: [gr.update(interactive=x)] * 8,
inputs=[prompt_inputs_ready_state],
outputs=[
prompt_submit_button,
evaluate_initial_system_message_button, generate_initial_system_message_button,
evaluate_system_message_button, evaluate_acceptance_criteria_input_button,
generate_acceptance_criteria_button, evaluate_acceptance_criteria_output_button,
flag_button
],
)
simple_llm_tab.select(
on_model_tab_select,
[
],
[
prompt_model_tab_state
]
)
advanced_llm_tab.select(
on_model_tab_select,
[
],
[
prompt_model_tab_state
]
)
expert_llm_tab.select(
on_model_tab_select,
[
],
[
prompt_model_tab_state
]
)
for item in [
prompt_model_tab_state,
simple_model_name_input,
advanced_optimizer_model_name_input,
advanced_executor_model_name_input,
expert_prompt_initial_developer_model_name_input,
expert_prompt_initial_developer_temperature_input,
expert_prompt_acceptance_criteria_model_name_input,
expert_prompt_acceptance_criteria_temperature_input,
expert_prompt_developer_model_name_input,
expert_prompt_developer_temperature_input,
expert_prompt_executor_model_name_input,
expert_prompt_executor_temperature_input,
expert_output_history_analyzer_model_name_input,
expert_output_history_analyzer_temperature_input,
expert_prompt_analyzer_model_name_input,
expert_prompt_analyzer_temperature_input,
expert_prompt_suggester_model_name_input,
expert_prompt_suggester_temperature_input,
]:
item.change(
on_prompt_model_tab_state_change,
[
config_state,
prompt_model_tab_state,
simple_model_name_input,
advanced_optimizer_model_name_input,
advanced_executor_model_name_input,
expert_prompt_initial_developer_model_name_input,
expert_prompt_initial_developer_temperature_input,
expert_prompt_acceptance_criteria_model_name_input,
expert_prompt_acceptance_criteria_temperature_input,
expert_prompt_developer_model_name_input,
expert_prompt_developer_temperature_input,
expert_prompt_executor_model_name_input,
expert_prompt_executor_temperature_input,
expert_output_history_analyzer_model_name_input,
expert_output_history_analyzer_temperature_input,
expert_prompt_analyzer_model_name_input,
expert_prompt_analyzer_temperature_input,
expert_prompt_suggester_model_name_input,
expert_prompt_suggester_temperature_input
],
[
model_name_states["initial_developer"],
model_temperature_states["initial_developer"],
model_name_states["acceptance_criteria"],
model_temperature_states["acceptance_criteria"],
model_name_states["developer"],
model_temperature_states["developer"],
model_name_states["executor"],
model_temperature_states["executor"],
model_name_states["history_analyzer"],
model_temperature_states["history_analyzer"],
model_name_states["analyzer"],
model_temperature_states["analyzer"],
model_name_states["suggester"],
model_temperature_states["suggester"]
],
)
generate_acceptance_criteria_button.click(
generate_acceptance_criteria,
inputs=[config_state, initial_system_message_input,
selected_example_input, selected_example_output,
model_name_states["acceptance_criteria"],
model_temperature_states["acceptance_criteria"],
prompt_template_group],
outputs=[acceptance_criteria_input, logs_chatbot]
)
evaluate_acceptance_criteria_input_button.click(
fn=evaluate_output,
inputs=[
config_state,
selected_example_output,
output_output,
acceptance_criteria_input,
model_name_states["analyzer"],
model_temperature_states["analyzer"],
prompt_template_group
],
outputs=[analysis_output]
)
evaluate_acceptance_criteria_output_button.click(
fn=evaluate_output,
inputs=[
config_state,
selected_example_output,
output_output,
acceptance_criteria_output,
model_name_states["analyzer"],
model_temperature_states["analyzer"],
prompt_template_group
],
outputs=[analysis_output]
)
generate_initial_system_message_button.click(
generate_initial_system_message,
inputs=[config_state, selected_example_input, selected_example_output,
model_name_states["initial_developer"],
model_temperature_states["initial_developer"],
prompt_template_group],
outputs=[initial_system_message_input, logs_chatbot]
)
evaluate_initial_system_message_button.click(
evaluate_system_message,
inputs=[
config_state,
initial_system_message_input,
selected_example_input,
model_name_states["executor"],
model_temperature_states["executor"]
],
outputs=[output_output]
)
evaluate_system_message_button.click(
evaluate_system_message,
inputs=[
config_state,
system_message_output,
selected_example_input,
model_name_states["executor"],
model_temperature_states["executor"]
],
outputs=[output_output]
)
pull_task_description_output_button.click(
lambda x: x,
inputs=[description_output],
outputs=[initial_system_message_input]
)
pull_system_message_output_button.click(
lambda x: x,
inputs=[system_message_output],
outputs=[initial_system_message_input]
)
pull_acceptance_criteria_output_button.click(
lambda x: x,
inputs=[acceptance_criteria_output],
outputs=[acceptance_criteria_input]
)
prompt_clear_button.add([
acceptance_criteria_input, initial_system_message_input,
system_message_output, output_output,
acceptance_criteria_output, analysis_output, logs_chatbot])
prompt_submit_button.click(
process_message_with_models,
inputs=[
config_state,
selected_example_input,
selected_example_output,
acceptance_criteria_input,
initial_system_message_input,
recursion_limit_input,
max_output_age,
model_name_states["initial_developer"],
model_temperature_states["initial_developer"],
model_name_states["acceptance_criteria"],
model_temperature_states["acceptance_criteria"],
model_name_states["developer"],
model_temperature_states["developer"],
model_name_states["executor"],
model_temperature_states["executor"],
model_name_states["history_analyzer"],
model_temperature_states["history_analyzer"],
model_name_states["analyzer"],
model_temperature_states["analyzer"],
model_name_states["suggester"],
model_temperature_states["suggester"],
prompt_template_group,
aggressive_exploration
],
outputs=[
system_message_output,
output_output,
analysis_output,
acceptance_criteria_output,
logs_chatbot
]
)
examples.load_input_event.then(
lambda: "append",
None,
selected_group_mode,
)
flagging_inputs = [
selected_example_input,
selected_example_output
]
# Configure flagging
if config.allow_flagging:
flag_method = FlagMethod(flagging_callback, "Flag", "")
flag_button.click(
utils.async_lambda(
lambda: Button(value="Saving...", interactive=False)
),
None,
flag_button,
queue=False,
show_api=False,
)
flag_button.click(
flag_method,
inputs=flagging_inputs,
outputs=flag_button,
preprocess=False,
queue=False,
show_api=False,
)
flagging_callback.setup(flagging_inputs, config.examples_path)
# Launch the Gradio app
demo.launch(server_name=config.server_name, server_port=config.server_port)