src/components/quizbowl/bonus.py

import json
from typing import Any

import gradio as gr
import numpy as np
import pandas as pd
from datasets import Dataset
from loguru import logger

from app_configs import CONFIGS, UNSELECTED_PIPELINE_NAME
from components import commons
from components.model_pipeline.model_pipeline import PipelineInterface, PipelineState
from components.typed_dicts import PipelineStateDict
from display.formatting import styled_error
from submission import submit
from workflows import factory
from workflows.qb_agents import QuizBowlBonusAgent

from . import populate, validation
from .plotting import create_bonus_confidence_plot, create_bonus_html
from .utils import evaluate_prediction
from .validation import UserInputWorkflowValidator


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], input_vars: list[str]):
    """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 = {"parts": example["parts"], "outputs": model_outputs}

        # Preparing step outputs for the model
        step_outputs = {}
        for i, output in enumerate(model_outputs):
            key = f"part {i + 1}"
            step_outputs[key] = {k: v for k, v in output["step_outputs"].items() if k not in input_vars}
            if output["logprob"] is not None:
                step_outputs[key]["output_probability"] = float(np.exp(output["logprob"]))

        return html_content, plot_data, state, step_outputs
    except Exception as e:
        logger.exception(f"Error initializing interface: {e.args}")
        return f"<div>Error initializing interface: {str(e)}</div>", pd.DataFrame(), {}, {}


class BonusInterface:
    """Gradio interface for the Bonus mode."""

    def __init__(self, app: gr.Blocks, browser_state: dict, dataset: Dataset, model_options: dict, defaults: dict):
        """Initialize the Bonus interface."""
        logger.info(f"Initializing Bonus interface with dataset size: {len(dataset)}")
        self.browser_state = browser_state
        self.ds = dataset
        self.model_options = model_options
        self.app = app
        self.defaults = defaults
        self.output_state = gr.State(value={})
        self.render()

    # ------------------------------------- LOAD PIPELINE STATE FROM BROWSER STATE -------------------------------------

    def load_presaved_pipeline_state(self, browser_state: dict, pipeline_change: bool):
        logger.debug(f"Loading presaved pipeline state from browser state:\n{json.dumps(browser_state, indent=4)}")
        try:
            state_dict = browser_state["bonus"].get("pipeline_state", {})
            pipeline_state = PipelineState.model_validate(state_dict)
            pipeline_state_dict = pipeline_state.model_dump()
            output_state = browser_state["bonus"].get("output_state", {})
        except Exception as e:
            logger.warning(f"Error loading presaved pipeline state: {e}")
            output_state = {}
            workflow = self.defaults["init_workflow"]
            pipeline_state_dict = PipelineState.from_workflow(workflow).model_dump()
        return browser_state, not pipeline_change, pipeline_state_dict, output_state

    # ------------------------------------------ INTERFACE RENDER FUNCTIONS -------------------------------------------
    def _render_pipeline_interface(self, pipeline_state: PipelineState):
        """Render the model interface."""
        with gr.Row(elem_classes="bonus-header-row form-inline"):
            self.pipeline_selector = commons.get_pipeline_selector([])
            self.load_btn = gr.Button("⬇️ Import Pipeline", variant="secondary")
        self.import_error_display = gr.HTML(label="Import Error", elem_id="import-error-display", visible=False)
        self.pipeline_interface = PipelineInterface(
            self.app,
            pipeline_state.workflow,
            ui_state=pipeline_state.ui_state,
            model_options=list(self.model_options.keys()),
            config=self.defaults,
            validator=UserInputWorkflowValidator("bonus"),
        )

    def _render_qb_interface(self):
        """Render the quizbowl interface."""
        with gr.Row(elem_classes="bonus-header-row form-inline"):
            self.qid_selector = commons.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")
        self.error_display = gr.HTML(label="Error", elem_id="bonus-error-display", visible=False)
        self.results_table = gr.DataFrame(
            label="Model Outputs",
            value=pd.DataFrame(columns=["Part", "Correct?", "Confidence", "Prediction", "Explanation"]),
            visible=False,
        )
        self.model_outputs_display = gr.JSON(label="Model Outputs", value="{}", show_indices=True, visible=False)

        with gr.Row():
            self.eval_btn = gr.Button("Evaluate", variant="primary")

        self.model_name_input, self.description_input, self.submit_btn, self.submit_status = (
            commons.get_model_submission_accordion(self.app)
        )

    def render(self):
        """Create the Gradio interface."""
        self.hidden_input = gr.Textbox(value="", visible=False, elem_id="hidden-index")
        workflow = factory.create_empty_tossup_workflow()
        pipeline_state = PipelineState.from_workflow(workflow)

        with gr.Row():
            # Model Panel
            with gr.Column(scale=1):
                self._render_pipeline_interface(pipeline_state)

            with gr.Column(scale=1):
                self._render_qb_interface()

        self._setup_event_listeners()

    def validate_workflow(self, state_dict: PipelineStateDict):
        """Validate the workflow."""
        try:
            pipeline_state = PipelineState(**state_dict)
            validation.validate_workflow(
                pipeline_state.workflow,
                required_input_vars=CONFIGS["bonus"]["required_input_vars"],
                required_output_vars=CONFIGS["bonus"]["required_output_vars"],
            )
        except Exception as e:
            raise gr.Error(f"Error validating workflow: {str(e)}")

    def get_new_question_html(self, question_id: int):
        """Get the HTML for a new question."""
        if question_id is None:
            logger.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_pipeline_names(self, profile: gr.OAuthProfile | None) -> list[str]:
        names = [UNSELECTED_PIPELINE_NAME] + populate.get_pipeline_names("bonus", profile)
        return gr.update(choices=names, value=UNSELECTED_PIPELINE_NAME)

    def load_pipeline(
        self, model_name: str, pipeline_change: bool, profile: gr.OAuthProfile | None
    ) -> tuple[str, bool, PipelineStateDict, dict]:
        try:
            workflow = populate.load_workflow("bonus", model_name, profile)
            if workflow is None:
                logger.warning(f"Could not load workflow for {model_name}")
                return UNSELECTED_PIPELINE_NAME, gr.skip(), gr.skip(), gr.update(visible=False)
            pipeline_state_dict = PipelineState.from_workflow(workflow).model_dump()
            return UNSELECTED_PIPELINE_NAME, not pipeline_change, pipeline_state_dict, gr.update(visible=True)
        except Exception as e:
            error_msg = styled_error(f"Error loading pipeline: {str(e)}")
            return UNSELECTED_PIPELINE_NAME, gr.skip(), gr.skip(), gr.update(visible=True, value=error_msg)

    # ------------------------------------- Agent Functions -----------------------------------------------------------

    def get_agent_outputs(self, example: dict, pipeline_state: PipelineState):
        """Get the model outputs for a given question ID."""
        outputs = []
        leadin = example["leadin"]
        agent = QuizBowlBonusAgent(pipeline_state.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,
        state_dict: PipelineStateDict,
    ) -> tuple[str, Any, Any]:
        """Run the agent in bonus mode and updates the interface.

        Returns:
            tuple: Contains the following components:
                - question_display: HTML display content of the question
                - output_state: Updated state with question parts and outputs
                - results_table: DataFrame with model predictions and scores
                - model_outputs_display: Detailed step outputs from the model
                - error_display: Any error messages (if applicable)
        """
        try:
            pipeline_state = validation.validate_bonus_workflow(state_dict)
            question_id = int(question_id - 1)
            if not self.ds or question_id < 0 or question_id >= len(self.ds):
                raise gr.Error("Invalid question ID or dataset not loaded")

            example = self.ds[question_id]
            outputs = self.get_agent_outputs(example, pipeline_state)

            # Process results and prepare visualization data
            html_content, plot_data, output_state, step_outputs = initialize_eval_interface(
                example, outputs, pipeline_state.workflow.inputs
            )
            df = process_bonus_results(outputs)

            return (
                html_content,
                gr.update(value=output_state),
                gr.update(value=df, label=f"Model Outputs for Question {question_id + 1}", visible=True),
                gr.update(value=step_outputs, label=f"Step Outputs for Question {question_id + 1}", visible=True),
                gr.update(visible=False),
            )
        except Exception as e:
            import traceback

            error_msg = f"Error: {str(e)}\n{traceback.format_exc()}"
            return (
                gr.skip(),
                gr.skip(),
                gr.update(visible=False),
                gr.update(visible=False),
                gr.update(visible=True, value=error_msg),
            )

    def evaluate(self, state_dict: PipelineStateDict, progress: gr.Progress = gr.Progress()):
        """Evaluate the bonus questions."""
        try:
            pipeline_state = validation.validate_bonus_workflow(state_dict)
            # 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_agent_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(visible=False),
                gr.update(visible=False),
            )
        except Exception as e:
            error_msg = styled_error(f"Error evaluating bonus: {e.args}")
            logger.exception(f"Error evaluating bonus: {e.args}")
            return gr.skip(), gr.skip(), gr.update(visible=True, value=error_msg)

    def submit_model(
        self,
        model_name: str,
        description: str,
        state_dict: PipelineStateDict,
        profile: gr.OAuthProfile = None,
    ):
        """Submit the model output."""
        pipeline_state = PipelineState(**state_dict)
        return submit.submit_model(model_name, description, pipeline_state.workflow, "bonus", profile)

    @property
    def pipeline_state(self):
        return self.pipeline_interface.pipeline_state

    # ------------------------------------- Event Listeners -----------------------------------------------------------

    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],
        )

        gr.on(
            triggers=[self.app.load],
            fn=self.get_pipeline_names,
            outputs=[self.pipeline_selector],
        )

        pipeline_change = self.pipeline_interface.pipeline_change

        gr.on(
            triggers=[self.app.load],
            fn=self.load_presaved_pipeline_state,
            inputs=[self.browser_state, pipeline_change],
            outputs=[self.browser_state, pipeline_change, self.pipeline_state, self.output_state],
        )
        self.load_btn.click(
            fn=self.load_pipeline,
            inputs=[self.pipeline_selector, pipeline_change],
            outputs=[self.pipeline_selector, pipeline_change, self.pipeline_state, self.import_error_display],
        )
        self.pipeline_interface.add_triggers_for_pipeline_export([self.pipeline_state.change], self.pipeline_state)

        self.run_btn.click(
            self.single_run,
            inputs=[
                self.qid_selector,
                self.pipeline_state,
            ],
            outputs=[
                self.question_display,
                self.output_state,
                self.results_table,
                self.model_outputs_display,
                self.error_display,
            ],
        )

        self.eval_btn.click(
            fn=self.evaluate,
            inputs=[self.pipeline_state],
            outputs=[self.results_table, self.model_outputs_display, self.error_display],
        )

        self.submit_btn.click(
            fn=self.submit_model,
            inputs=[
                self.model_name_input,
                self.description_input,
                self.pipeline_state,
            ],
            outputs=[self.submit_status],
        )