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ReproLab FastAPI Backend

FastAPI wrapper connecting the React frontend to the Python reproducibility scoring engine.

Quick Start

Prerequisites

  • Python 3.10+
  • pip or uv

Setup

  1. Install dependencies:

    pip install -r requirements.txt

  2. Run server:

    python main.py

Architecture

api/
├── main.py              # FastAPI application
├── requirements.txt     # Python dependencies
└── __init__.py

API Endpoints

Health Check

GET /health

Returns: {"status": "ok"}

List Protocols

GET /protocols

Returns array of protocol objects:

[
  {
    "id": "proto_1",
    "name": "DNA Extraction",
    "description": "...",
    "materials": ["Taq polymerase (NEB M0273L)"],
    "methods": ["Step 1: ..."],
    "constraints": ["Temperature: 37°C"],
    "reproducibility_score": {
      "overall": 85,
      "metadata_completeness": 90,
      "reagent_traceability": 80,
      "step_granularity": 75
    }
  }
]

Get Protocol

GET /protocols/{protocol_id}

Returns single protocol object.

Create Protocol

POST /protocols
Content-Type: application/json

{
  "name": "DNA Extraction Protocol",
  "description": "Standard molecular extraction procedure",
  "materials": ["DNA extraction kit (Qiagen 69504)"],
  "methods": ["Add sample to lysis buffer", "Incubate at 95°C for 10 min"],
  "constraints": ["Store at -20°C"]
}

Returns: Created protocol with ID and score.

Update Protocol

PUT /protocols/{protocol_id}
Content-Type: application/json

{
  "name": "Updated Name",
  "description": "...",
  ...
}

Returns: Updated protocol with recalculated score.

Score Protocol (Without Saving)

POST /protocols/score
Content-Type: application/json

{
  "name": "Protocol Name",
  "description": "Description",
  "materials": ["..."],
  "methods": ["..."],
  "constraints": ["..."]
}

Returns:

{
  "overall": 75,
  "metadata_completeness": 80,
  "reagent_traceability": 70,
  "step_granularity": 65
}

Delete Protocol

DELETE /protocols/{protocol_id}

Returns: {"message": "Protocol deleted"}

Scoring Algorithm

Reproducibility score combines three components:

Component Weight Calculation
Metadata Completeness 45% Name + description + constraints present
Reagent Traceability 35% Materials with catalog numbers present
Step Granularity 20% Number and detail of method steps

Final Score = 45% × metadata + 35% × traceability + 20% × granularity

Range: 0-100

Integration with Python Backend

The API imports and uses the ReproLab Python package:

from reprolab import ReproLabPipeline
from reprolab.scoring import ReproducibilityScorer

scorer = ReproducibilityScorer()
score = scorer.score(protocol_dict, log_frame)

Scoring results are wrapped in HTTP responses with proper error handling.

CORS Configuration

Allowed origins (configured in main.py):

  • http://localhost:5173 (React dev server)
  • http://localhost:3000 (Alternative React dev port)

Error Handling

{
  "detail": "Protocol not found"
}

HTTP Status Codes:

  • 200 - Success
  • 404 - Not found
  • 422 - Validation error
  • 500 - Server error

Development

Interactive Docs

Run server and navigate to: http://localhost:8000/docs

Swagger UI provides interactive API testing.

Logging

Enable debug logging in main.py:

import logging
logging.basicConfig(level=logging.DEBUG)

Production Deployment

  1. Use production ASGI server (Gunicorn):

    pip install gunicorn
gunicorn -w 4 -k uvicorn.workers.UvicornWorker api.main:app

  2. Configure environment variables

  3. Set CORS origins to production domains

  4. Replace in-memory storage with Supabase

Data Storage

Currently uses in-memory dictionary (protocols_store).

Future: Migrate to Supabase PostgreSQL

  • User authentication via Supabase Auth
  • Protocol data in protocols table
  • Multi-tenant isolation
  • Audit trail logging

Testing

# Interactive API docs
curl http://localhost:8000/health

# Score a protocol
curl -X POST http://localhost:8000/protocols/score \
  -H "Content-Type: application/json" \
  -d '{"name": "Test", "materials": ["Item 1"], "methods": ["Step 1"], "constraints": []}'