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import gradio as gr
import numpy as np
from loguru import logger
from app_configs import AVAILABLE_MODELS, UNSELECTED_VAR_NAME
from components import commons
from display.formatting import tiny_styled_warning
from workflows.structs import Buzzer, TossupWorkflow
from .model_pipeline import PipelineInterface, PipelineState, PipelineUIState
def toggleable_slider(
value, minimum, maximum, step, toggle_value=False, label=None, info=None, min_width=200, scale=1
):
with gr.Column(elem_classes="toggleable", min_width=min_width, scale=scale):
show_label = label is not None
checkbox = gr.Checkbox(label=label, value=toggle_value, container=False, info=info, show_label=show_label)
slider = gr.Slider(
minimum=minimum,
maximum=maximum,
value=value,
step=step,
label="",
interactive=True,
show_label=False,
container=False,
)
checkbox.change(fn=lambda x: gr.update(interactive=x), inputs=[checkbox], outputs=[slider])
return checkbox, slider
class TossupPipelineState(PipelineState):
workflow: TossupWorkflow
class TossupPipelineInterface(PipelineInterface):
def __init__(
self,
workflow: TossupWorkflow,
ui_state: PipelineUIState | None = None,
model_options: list[str] = None,
simple: bool = False,
show_pipeline_selector: bool = False,
defaults: dict = {},
):
super().__init__(workflow, ui_state, model_options, simple, show_pipeline_selector)
self.defaults = defaults
def update_buzzer(
self,
state: TossupPipelineState,
confidence_threshold: float,
method: str,
tokens_prob: float | None,
):
"""Update the buzzer."""
prob_threshold = float(tokens_prob) if tokens_prob and tokens_prob > 0 else None
state.workflow.buzzer = state.workflow.buzzer.model_copy(
update={
"method": method,
"confidence_threshold": confidence_threshold,
"prob_threshold": prob_threshold,
}
)
Buzzer.model_validate(state.workflow.buzzer)
return state
def update_prob_slider(self, state: TossupPipelineState, answer_var: str, tokens_prob: float | None):
"""Update the probability slider based on the answer variable."""
if answer_var == UNSELECTED_VAR_NAME:
return (
state,
gr.update(interactive=True),
gr.update(value="AND", interactive=True),
gr.update(visible=False),
)
step_id = answer_var.split(".")[0]
model_name = state.workflow.steps[step_id].get_full_model_name()
model_config = AVAILABLE_MODELS[model_name]
is_model_with_logprobs = model_config.get("logprobs", False)
buzzer = state.workflow.buzzer
tokens_prob_threshold = tokens_prob if is_model_with_logprobs else None
method = buzzer.method if is_model_with_logprobs else "AND"
state = self.update_buzzer(
state,
confidence_threshold=buzzer.confidence_threshold,
method=method,
tokens_prob=tokens_prob_threshold,
)
return (
state,
gr.update(interactive=is_model_with_logprobs),
gr.update(value=method, interactive=is_model_with_logprobs),
gr.update(
value=tiny_styled_warning(
f"{model_name} does not support `logprobs`. The probability slider will be disabled."
),
visible=not is_model_with_logprobs,
),
)
def _render_output_panel(self, available_variables: list[str], pipeline_state: TossupPipelineState):
dropdowns = {}
variable_options = [UNSELECTED_VAR_NAME] + [v for v in available_variables if v not in self.input_variables]
with gr.Column(elem_classes="step-accordion control-panel"):
commons.get_panel_header(
header="Final output variables mapping:",
)
with gr.Row(elem_classes="output-fields-row"):
for output_field in self.required_output_variables:
value = pipeline_state.workflow.outputs.get(output_field, UNSELECTED_VAR_NAME)
dropdown = gr.Dropdown(
label=output_field,
value=value,
choices=variable_options,
interactive=True,
elem_classes="output-field-variable",
# show_label=False,
)
dropdown.change(
self.sm.update_output_variables,
inputs=[self.pipeline_state, gr.State(output_field), dropdown],
outputs=[self.pipeline_state],
)
dropdowns[output_field] = dropdown
commons.get_panel_header(
header="Buzzer settings:",
subheader="Set your thresholds for confidence and output tokens probability.",
)
with gr.Row(elem_classes="control-panel"):
self.confidence_slider = gr.Slider(
minimum=0.0,
maximum=1.0,
value=self.defaults.get("confidence_threshold", 0.85),
step=0.01,
label="Confidence",
elem_classes="slider-container",
)
self.buzzer_method_dropdown = gr.Dropdown(
choices=["AND", "OR"],
value=self.defaults.get("buzzer_method", "AND"),
label="Method",
interactive=True,
min_width=80,
scale=0,
)
self.prob_slider = gr.Slider(
value=self.defaults.get("logits_prob", 0.0),
label="Probability",
minimum=0.0,
maximum=1.0,
step=0.001,
elem_classes="slider-container",
)
self.buzzer_warning_display = gr.HTML(visible=False)
def update_choices(available_variables):
"""Update the choices for the dropdowns"""
return [
gr.update(choices=available_variables, value=None, selected=None) for dropdown in dropdowns.values()
]
self.variables_state.change(
update_choices,
inputs=[self.variables_state],
outputs=list(dropdowns.values()),
)
gr.on(
triggers=[
self.confidence_slider.input,
self.buzzer_method_dropdown.input,
self.prob_slider.input,
],
fn=self.update_buzzer,
inputs=[
self.pipeline_state,
self.confidence_slider,
self.buzzer_method_dropdown,
self.prob_slider,
],
outputs=[self.pipeline_state],
)
# TODO: Do Add model step change triggers as well. (Model name change triggers)
answer_dropdown = dropdowns["answer"]
if answer_dropdown is not None:
gr.on(
triggers=[answer_dropdown.input, self.model_selection_state.change],
fn=self.update_prob_slider,
inputs=[self.pipeline_state, answer_dropdown, self.prob_slider],
outputs=[
self.pipeline_state,
self.prob_slider,
self.buzzer_method_dropdown,
self.buzzer_warning_display,
],
)
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