src/components/quizbowl/bonus.py

import json
import logging
from typing import Any

import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datasets import Dataset

from components.model_pipeline.model_pipeline import PipelineInterface, PipelineState
from submission import submit
from workflows import factory
from workflows.qb.simple_agent import SimpleBonusAgent
from workflows.structs import ModelStep, Workflow

from .plotting import (
    create_pyplot,
    create_scatter_pyplot,
    evaluate_buzz,
    update_plot,
)


def evaluate_bonus_part(prediction: str, clean_answers: list[str]) -> float:
    """Evaluate a single bonus part."""
    return evaluate_buzz(prediction, clean_answers)


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:
        # Create HTML for leadin and parts
        leadin_html = f"<div class='leadin'>{example['leadin']}</div>"
        parts_html = []
        for i, part in enumerate(example["parts"]):
            parts_html.append(f"<div class='part'><b>Part {i + 1}:</b> {part['part']}</div>")

        html_content = f"{leadin_html}<div class='parts-container'>{''.join(parts_html)}</div>"

        # 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 create_bonus_confidence_plot(parts: list[dict], model_outputs: list[dict]):
    """Create confidence plot for bonus parts."""
    plt.style.use("ggplot")
    fig = plt.figure(figsize=(10, 6))
    ax = fig.add_subplot(111)

    # Plot confidence for each part
    x = range(1, len(parts) + 1)
    confidences = [output["confidence"] for output in model_outputs]
    scores = [output["score"] for output in model_outputs]

    # Plot confidence bars
    bars = ax.bar(x, confidences, color="#4698cf")

    # Color bars based on correctness
    for i, score in enumerate(scores):
        bars[i].set_color("green" if score == 1 else "red")

    ax.set_title("Part Confidence")
    ax.set_xlabel("Part Number")
    ax.set_ylabel("Confidence")
    ax.set_xticks(x)
    ax.set_xticklabels([f"Part {i}" for i in x])

    return fig


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()),
        )
        with gr.Row():
            self.run_btn = gr.Button("Run Bonus", variant="primary")

    def _render_qb_interface(self):
        """Render the quizbowl interface."""
        with gr.Row():
            self.qid_selector = gr.Number(
                label="Question ID", value=1, precision=0, minimum=1, maximum=len(self.ds), show_label=True, scale=0
            )
            self.answer_display = gr.Textbox(
                label="Answers", elem_id="answer-display", elem_classes="answer-box", interactive=False, scale=1
            )
            self.clean_answer_display = gr.Textbox(
                label="Acceptable Answers",
                elem_id="answer-display-2",
                elem_classes="answer-box",
                interactive=False,
                scale=2,
            )

        self.question_display = gr.HTML(label="Question", elem_id="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")

        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")
            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."""
        example = self.ds[question_id - 1]
        leadin = example["leadin"]
        parts = example["parts"]

        # Create HTML for leadin and parts
        leadin_html = f"<div class='leadin'>{leadin}</div>"
        parts_html = []
        for i, part in enumerate(parts):
            parts_html.append(f"<div class='part'>{part['part']}</div>")

        parts_html_str = "<br>".join(parts_html)

        html_content = (
            f"<div class='token-container'>{leadin_html}<div class='parts-container'><br>{parts_html_str}</div></div>"
        )

        # Format answers
        primary_answers = [f"{i + 1}. {part['answer_primary']}" for i, part in enumerate(parts)]
        clean_answers = []
        for i, part in enumerate(parts):
            part_answers = [a for a in part["clean_answers"] if len(a.split()) <= 6]
            clean_answers.append(f"{i + 1}. {', '.join(part_answers)}")

        return html_content, "\n".join(primary_answers), "\n".join(clean_answers)

    def get_model_outputs(self, example: dict, pipeline_state: PipelineState):
        """Get the model outputs for a given question ID."""
        outputs = []
        leadin = example["leadin"]

        for i, part in enumerate(example["parts"]):
            agent = SimpleBonusAgent(workflow=pipeline_state.workflow)
            # 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_bonus_part(part_output["answer"], part["clean_answers"])

            outputs.append(part_output)

        return outputs

    def run_bonus(
        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, None, None

    def evaluate_bonus(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.answer_display, self.clean_answer_display],
        )
        self.run_btn.click(
            self.pipeline_interface.validate_workflow,
            inputs=[self.pipeline_interface.pipeline_state],
            outputs=[self.pipeline_interface.pipeline_state],
        ).success(
            self.run_bonus,
            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_bonus,
            inputs=[self.pipeline_interface.pipeline_state],
            outputs=[self.results_table, self.confidence_plot],
        )

        self.submit_btn.click(
            fn=self.submit_model_output,
            inputs=[
                self.model_name_input,
                self.description_input,
                self.pipeline_interface.pipeline_state,
            ],
            outputs=[self.submit_status],
        )
        self.hidden_input.change(
            fn=update_plot,
            inputs=[self.hidden_input, self.output_state],
            outputs=[self.confidence_plot],
        )