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import json
import logging
from typing import Any
import gradio as gr
import pandas as pd
from datasets import Dataset
from components.model_pipeline.model_pipeline import PipelineInterface, PipelineState
from submission import submit
from workflows.qb.multi_step_agent import MultiStepBonusAgent
from workflows.qb.simple_agent import SimpleBonusAgent
from workflows.structs import ModelStep, Workflow
from .commons import get_qid_selector
from .plotting import (
create_bonus_confidence_plot,
create_bonus_html,
create_scatter_pyplot,
update_tossup_plot,
)
from .utils import evaluate_prediction
def process_bonus_results(results: list[dict]) -> pd.DataFrame:
"""Process results from bonus mode and prepare visualization data."""
return pd.DataFrame(
[
{
"Part": f"Part {r['part_number']}",
"Correct?": "✅" if r["score"] == 1 else "❌",
"Confidence": r["confidence"],
"Prediction": r["answer"],
"Explanation": r["explanation"],
}
for r in results
]
)
def initialize_eval_interface(example: dict, model_outputs: list[dict]):
"""Initialize the interface with example text."""
try:
html_content = create_bonus_html(example["leadin"], example["parts"])
# Create confidence plot data
plot_data = create_bonus_confidence_plot(example["parts"], model_outputs)
# Store state
state = json.dumps({"parts": example["parts"], "outputs": model_outputs})
return html_content, plot_data, state
except Exception as e:
logging.error(f"Error initializing interface: {e}", exc_info=True)
return f"<div>Error initializing interface: {str(e)}</div>", pd.DataFrame(), "{}"
def validate_workflow(workflow: Workflow):
"""Validate that a workflow is properly configured for the bonus task."""
if not workflow.steps:
raise ValueError("Workflow must have at least one step")
# Ensure all steps are properly configured
for step_id, step in workflow.steps.items():
validate_model_step(step)
# Check that the workflow has the correct structure
input_vars = set(workflow.inputs)
if "leadin" not in input_vars or "part" not in input_vars:
raise ValueError("Workflow must have 'leadin' and 'part' as inputs")
output_vars = set(workflow.outputs)
if not all(var in output_vars for var in ["answer", "confidence", "explanation"]):
raise ValueError("Workflow must produce 'answer', 'confidence', and 'explanation' as outputs")
def validate_model_step(model_step: ModelStep):
"""Validate that a model step is properly configured for the bonus task."""
# Check required fields
if not model_step.model or not model_step.provider:
raise ValueError("Model step must have both model and provider specified")
if model_step.call_type != "llm":
raise ValueError("Model step must have call_type 'llm'")
# Validate temperature for LLM steps
if model_step.temperature is None:
raise ValueError("Temperature must be specified for LLM model steps")
if not (0.0 <= model_step.temperature <= 1.0):
raise ValueError(f"Temperature must be between 0.0 and 1.0, got {model_step.temperature}")
# Validate input fields
input_field_names = {field.name for field in model_step.input_fields}
if "leadin" not in input_field_names or "part" not in input_field_names:
raise ValueError("Model step must have 'leadin' and 'part' input fields")
# Validate output fields
output_field_names = {field.name for field in model_step.output_fields}
required_outputs = {"answer", "confidence", "explanation"}
if not all(out in output_field_names for out in required_outputs):
raise ValueError("Model step must have all required output fields: answer, confidence, explanation")
# Validate confidence output field is of type float
for field in model_step.output_fields:
if field.name == "confidence" and field.type != "float":
raise ValueError("The 'confidence' output field must be of type 'float'")
class BonusInterface:
"""Gradio interface for the Bonus mode."""
def __init__(self, app: gr.Blocks, dataset: Dataset, model_options: dict, defaults: dict):
"""Initialize the Bonus interface."""
logging.info(f"Initializing Bonus interface with dataset size: {len(dataset)}")
self.ds = dataset
self.model_options = model_options
self.app = app
self.defaults = defaults
self.output_state = gr.State(value="{}")
self.render()
def _render_model_interface(self, workflow: Workflow, simple: bool = True):
"""Render the model interface."""
self.pipeline_interface = PipelineInterface(
workflow,
simple=simple,
model_options=list(self.model_options.keys()),
)
def _render_qb_interface(self):
"""Render the quizbowl interface."""
with gr.Row(elem_classes="bonus-header-row form-inline"):
self.qid_selector = get_qid_selector(len(self.ds))
self.run_btn = gr.Button("Run on Bonus Question", variant="secondary")
self.question_display = gr.HTML(label="Question", elem_id="bonus-question-display")
with gr.Row():
self.confidence_plot = gr.Plot(
label="Part Confidence",
format="webp",
)
self.results_table = gr.DataFrame(
label="Model Outputs",
value=pd.DataFrame(columns=["Part", "Correct?", "Confidence", "Prediction", "Explanation"]),
)
with gr.Row():
self.eval_btn = gr.Button("Evaluate", variant="primary")
with gr.Accordion("Model Submission", elem_classes="model-submission-accordion", open=True):
with gr.Row():
self.model_name_input = gr.Textbox(label="Model Name")
self.description_input = gr.Textbox(label="Description")
with gr.Row():
gr.LoginButton()
self.submit_btn = gr.Button("Submit", variant="primary")
self.submit_status = gr.HTML(label="Submission Status")
def render(self):
"""Create the Gradio interface."""
self.hidden_input = gr.Textbox(value="", visible=False, elem_id="hidden-index")
workflow = self.defaults["init_workflow"]
with gr.Row():
# Model Panel
with gr.Column(scale=1):
self._render_model_interface(workflow, simple=self.defaults["simple_workflow"])
with gr.Column(scale=1):
self._render_qb_interface()
self._setup_event_listeners()
def get_new_question_html(self, question_id: int):
"""Get the HTML for a new question."""
if question_id is None:
logging.error("Question ID is None. Setting to 1")
question_id = 1
try:
question_id = int(question_id) - 1
if not self.ds or question_id < 0 or question_id >= len(self.ds):
return "Invalid question ID or dataset not loaded"
example = self.ds[question_id]
leadin = example["leadin"]
parts = example["parts"]
return create_bonus_html(leadin, parts)
except Exception as e:
return f"Error loading question: {str(e)}"
def get_model_outputs(self, example: dict, pipeline_state: PipelineState):
"""Get the model outputs for a given question ID."""
outputs = []
leadin = example["leadin"]
workflow = pipeline_state.workflow
if len(workflow.steps) > 1:
agent = MultiStepBonusAgent(workflow)
else:
agent = SimpleBonusAgent(workflow)
for i, part in enumerate(example["parts"]):
# Run model for each part
part_output = agent.run(leadin, part["part"])
# Add part number and evaluate score
part_output["part_number"] = i + 1
part_output["score"] = evaluate_prediction(part_output["answer"], part["clean_answers"])
outputs.append(part_output)
return outputs
def single_run(
self,
question_id: int,
pipeline_state: PipelineState,
) -> tuple[str, Any, Any]:
"""Run the agent in bonus mode."""
try:
# Validate inputs
question_id = int(question_id - 1)
if not self.ds or question_id < 0 or question_id >= len(self.ds):
return "Invalid question ID or dataset not loaded", None, None
example = self.ds[question_id]
outputs = self.get_model_outputs(example, pipeline_state)
# Process results and prepare visualization data
html_content, plot_data, output_state = initialize_eval_interface(example, outputs)
df = process_bonus_results(outputs)
return (
html_content,
gr.update(value=plot_data, label=f"Part Confidence on Question {question_id + 1}"),
gr.update(value=output_state),
gr.update(value=df, label=f"Model Outputs for Question {question_id + 1}"),
)
except Exception as e:
import traceback
error_msg = f"Error: {str(e)}\n{traceback.format_exc()}"
return error_msg, gr.skip(), gr.skip(), gr.skip()
def evaluate(self, pipeline_state: PipelineState, progress: gr.Progress = gr.Progress()):
"""Evaluate the bonus questions."""
try:
# Validate inputs
if not self.ds or not self.ds.num_rows:
return "No dataset loaded", None, None
total_correct = 0
total_parts = 0
part_scores = []
part_numbers = []
for example in progress.tqdm(self.ds, desc="Evaluating bonus questions"):
model_outputs = self.get_model_outputs(example, pipeline_state)
for output in model_outputs:
total_parts += 1
if output["score"] == 1:
total_correct += 1
part_scores.append(output["score"])
part_numbers.append(output["part_number"])
accuracy = total_correct / total_parts
df = pd.DataFrame(
[
{
"Part Accuracy": f"{accuracy:.2%}",
"Total Score": f"{total_correct}/{total_parts}",
"Questions Evaluated": len(self.ds),
}
]
)
plot_data = create_scatter_pyplot(part_numbers, part_scores)
return (
gr.update(value=df, label="Scores on Sample Set"),
gr.update(value=plot_data, label="Part Scores on Sample Set"),
)
except Exception:
import traceback
logging.error(f"Error evaluating bonus: {traceback.format_exc()}")
return "Error evaluating bonus", None, None
def submit_model(
self, model_name: str, description: str, pipeline_state: PipelineState, profile: gr.OAuthProfile = None
):
"""Submit the model output."""
return submit.submit_model(model_name, description, pipeline_state.workflow, "bonus", profile)
def _setup_event_listeners(self):
# Initialize with the default question (ID 0)
gr.on(
triggers=[self.app.load, self.qid_selector.change],
fn=self.get_new_question_html,
inputs=[self.qid_selector],
outputs=[self.question_display],
)
self.run_btn.click(
self.pipeline_interface.validate_workflow,
inputs=[self.pipeline_interface.pipeline_state],
outputs=[self.pipeline_interface.pipeline_state],
).success(
self.single_run,
inputs=[
self.qid_selector,
self.pipeline_interface.pipeline_state,
],
outputs=[
self.question_display,
self.confidence_plot,
self.output_state,
self.results_table,
],
)
self.eval_btn.click(
fn=self.evaluate,
inputs=[self.pipeline_interface.pipeline_state],
outputs=[self.results_table, self.confidence_plot],
)
self.submit_btn.click(
fn=self.submit_model,
inputs=[
self.model_name_input,
self.description_input,
self.pipeline_interface.pipeline_state,
],
outputs=[self.submit_status],
)
self.hidden_input.change(
fn=update_tossup_plot,
inputs=[self.hidden_input, self.output_state],
outputs=[self.confidence_plot],
)
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