Results Visualizer Component

Relevant source files

The following files were used as context for generating this wiki page:

• README.md
• frontend/src/components/Visualizer/ResultsView.tsx
• frontend/src/api/evaluations.ts
• backend/app/models/evaluation.py
• backend/app/api/v1/endpoints/results.py

The Results Visualizer Component renders performance metrics and evaluation data generated by the platform's testing suites. It provides a graphical interface for developers to analyze the success rates, latency, and specific failure modes of evaluated models.

This component acts as the primary interface for interpreting raw evaluation JSON data. It transforms backend database records into interactive charts and tables, allowing for side-by-side comparisons of different evaluation runs.

Sources: README.md:1-5, frontend/src/components/Visualizer/ResultsView.tsx:10-25

Component Architecture

The visualizer follows a client-server pattern where the frontend requests evaluation records via a REST API. The backend retrieves these records from the database and returns a structured payload containing metrics and logs.

The diagram shows the data flow from the database to the visual representation in the UI.
Sources: frontend/src/api/evaluations.ts:5-15, backend/app/api/v1/endpoints/results.py:20-45

Data Models and Types

The visualizer consumes the EvaluationResult model. This model stores the quantitative outputs of an evaluation run, including accuracy scores and individual test case results.

Evaluation Object Structure

Field	Type	Description
id	UUID	Unique identifier for the evaluation run
model_name	String	The name of the model being evaluated
status	Enum	Current state: PENDING, RUNNING, COMPLETED, FAILED
metrics	JSONB	Dictionary containing accuracy, precision, and recall
created_at	Timestamp	Time when the evaluation started

Sources: backend/app/models/evaluation.py:12-30

Implementation Details

API Integration

The frontend utilizes an asynchronous fetcher to retrieve results. It targets the /api/v1/results/{id} endpoint to get specific run details.

// frontend/src/api/evaluations.ts:18
export const fetchEvaluationResults = async (id: string) => {
  const response = await fetch(`/api/v1/results/${id}`);
  if (!response.ok) throw new Error('Failed to fetch results');
  return response.json();
};

Sources: frontend/src/api/evaluations.ts:18-23

Rendering Logic

The ResultsView component processes the metrics object to generate bar charts. It filters out incomplete test cases before rendering to ensure data integrity.

1. The component initializes with a loading state.
2. It calls the fetchEvaluationResults function inside a useEffect hook.
3. If the data contains an error field, it renders an error boundary.
4. It maps the results array to chart labels and datasets.

Sources: frontend/src/components/Visualizer/ResultsView.tsx:40-65

Sequence of Operations

The following sequence diagram illustrates the lifecycle of a visualization request from user interaction to data display.

Summary

The Results Visualizer Component centralizes evaluation data processing by fetching model performance metrics and presenting them through a React-based interface. It ensures that complex evaluation outputs are transformed into actionable insights via structured API calls and dynamic charting components.

Sources: README.md:1-10