I'll enhance the STEM LLM Evaluation Dashboard with comprehensive graph visualizations to make the data more visually compelling and easier to analyze.
I've significantly enhanced the STEM LLM Evaluation Dashboard with comprehensive graph visualizations! Here are the key visual improvements:
1. Interactive Charts & Graphs:
2. Visual Analytics Tab:
3. Performance Trends Tab:
Color-Coded Models:
#4F46E5)#059669)#DC2626)#7C3AED)#EA580C)Interactive Elements:
The dashboard now provides rich visual analytics that make it easy to understand model performance patterns, identify trends, and make data-driven decisions about model selection for STEM applications!
Project Name: AI QA Evaluation Portal🚀 PurposeTo build a backend system that:Allows admins/users to upload JSON/CSV files containing AI-generated QA data.Stores categorized questions with metadata (e.g., subject, model name, result status).Displays detailed information about correct/incorrect answers by AI.Manages user authentication with roles (admin, user).Supports admin capabilities like user blocking and dataset management.Prepares a solid foundation to pitch to investors by showcasing data insights and model performance.⚙️ Tech StackBackend:Framework: FastAPI (Python 3.11+)Authentication: OAuth2 with JWTDatabase ORM: SQLAlchemyBackground Tasks: Celery (optional for later)File Storage: Local (for MVP) or AWS S3 (optional)Validation: PydanticDatabase:PostgreSQLProduction-readyScalable for future analyticsWell supported with SQLAlchemy🧩 System RolesAdminFull accessCan upload/view/delete datasetsCan block/unblock usersCan filter and view detailed evaluation resultsUserCan log in, upload QA datasetsCan view their uploaded results and insights🗃️ Core Features (MVP Scope)1. Authentication SystemJWT token-based login/logoutRole-based access controlUser registration (Admin creates users manually in MVP)Password hashing using bcryptAdmin can block a user (flag in DB, prevent login)2. QA Dataset Upload & ProcessingUpload endpoint:Accepts .csv or .jsonParses and validates schema:Subject/category (e.g., math, chemistry)Model name (e.g., GPT-4, Claude)Original questionAI-generated answerCorrect answer (ground truth)Evaluation status: correct / incorrectEach dataset stored with:Uploader reference (User ID)TimestampCategoryModel used3. Evaluation Result ManagementStore every question and result per uploadResult object contains:question, ai_answer, correct_answerstatus (matched or mismatched)model_name, category, upload_idAdmin can view data across all usersUser can view only their own results4. Data Filtering & VisualizationBackend endpoints for:Filter by category, model_name, statusGrouped statistics (e.g., mismatches per model)Output structured data for frontend visualizationTotal Qs, correct %, mismatched Q count, etc.5. Admin ToolsAdmin dashboard endpoints:User management (block/unblock)Dataset overviewView any user’s uploadDelete datasets🧱 Database Schema (Simplified)usersFieldTypeDescriptionidUUIDPrimary KeyusernameStringUniqueemailStringUniquepassword_hashStringHashed passwordroleEnumadmin/useris_blockedBooleanTrue = access denieduploadsFieldTypeDescriptionidUUIDUpload batch identifieruser_idUUIDFK to usersfile_typeEnumJSON / CSVcreated_atDateTimeUpload timestampquestionsFieldTypeDescriptionidUUIDupload_idUUIDFK to uploadsquestionTextai_answerTextcorrect_answerTextstatusEnummatched / mismatchedmodel_nameStringe.g., GPT-4categoryEnummath, physics, etc.created_atDateTime📤 API Endpoints (Planned)AuthPOST /auth/login – Login with JWTPOST /auth/logout – Invalidate token (optional)POST /auth/register – Only for adminPOST /auth/block-user – Admin onlyUploadsPOST /upload – Upload CSV/JSON (user/admin)GET /uploads – Get own uploadsDELETE /upload/{id} – Admin deletes uploadQuestions & StatsGET /questions – List/filter questionsGET /questions/statistics – Grouped by model/category/statusGET /questions/{upload_id} – Detail of uploaded questions✅ Success CriteriaAuthentication is secure and role-basedJSON/CSV file uploads are validated and storedAdmin can manage users and view all resultsUsers can filter, view, and download their resultsAll data exposed via clean API for frontend charting🛠️ Deployment ConsiderationsDatabase: PostgreSQL (Heroku, Supabase, etc.)App Hosting: Render, Railway, or Docker on VPSStatic Storage: AWS S3 for future scalabilityEnvironment: .env with SECRET_KEY, DB URL📈 Future Roadmap (Not MVP)Automation pipeline to analyze PDFs/imagesAI model integration pipelineActivity logs per userNotifications/email integrationExport dataset as PDF or ExcelAdd team/collaborator feature Since this is a public leaderboard-style page, we can strip the heavy auth/admin pieces for now and focus on showcasing results:Scope Right Now* Public webpage: sortable leaderboard of LLMs on your dataset(s).* Show key metrics: accuracy, token counts, cost estimates, latency (if available).* Drill‑down page for a run/model: sample Q→A pairs, error cases.
I'll create a comprehensive solution with a FastAPI backend and a React dashboard that focuses on the public leaderboard functionality while maintaining an architecture that can support the full feature set later.
I've created a comprehensive AI QA Evaluation Portal that focuses on the public leaderboard functionality while maintaining an architecture that can support the full feature set later. Here's what's included:
POST /upload: Process and evaluate QA datasetsGET /models: Retrieve model leaderboard dataGET /models/{model_name}: Get detailed model statisticsGET /categories: View performance by categoryGET /questions: Filter and retrieve individual questionsUpload Process:
Leaderboard View:
Detailed Analysis:
This implementation provides a solid foundation for showcasing AI model performance on your datasets, with a focus on clear visualization and comparison of results.
quick update cope Right Now* Public webpage: sortable leaderboard of LLMs on your dataset(s).* Show key metrics: accuracy, token counts, cost estimates, latency (if available).* Drill‑down page for a run/model: sample Q→A pairs, error cases.we rank LLMs (openai, gemini, grok, kimi etc) by evaluating them on our QnA