import fluxcore
from fluxcore.ai import SketchToCircuit

# Initialize the client
client = fluxcore.Client(api_key="your-api-key")

# Create sketch converter
converter = SketchToCircuit(client)

# Option 1: Upload an image file
result = converter.from_image("sketch.jpg")

# Option 2: Capture from camera
result = converter.from_camera()

# Option 3: Use base64 encoded image
with open("sketch.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()
result = converter.from_base64(image_data)

# Access the generated circuit
circuit = result.circuit
print(f"Recognized {len(circuit.components)} components")
print(f"Found {len(circuit.connections)} connections")

# View confidence scores
for component in circuit.components:
    print(f"{component.type}: {component.confidence:.2%}")

# Export to various formats
circuit.export_gds("output.gds")
circuit.export_json("output.json")

# Open in editor
circuit.open_in_editor()

# Upload sketch and convert to circuit
curl -X POST https://api.fluxcore.io/v1/ai/sketch-to-circuit \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F "image=@sketch.jpg" \
  -F "options={
    \"target_wavelength\": 1550,
    \"material_system\": \"silicon\",
    \"min_feature_size\": 100,
    \"optimize_routing\": true
  }"

# Response
{
  "circuit_id": "cir_abc123",
  "components": [
    {
      "id": "ring_1",
      "type": "ring_resonator",
      "confidence": 0.97,
      "parameters": {
        "radius": 10.0,
        "gap": 0.2,
        "width": 0.45
      },
      "position": {"x": 100, "y": 200}
    }
  ],
  "connections": [...],
  "validation": {
    "drc_passed": true,
    "warnings": []
  }
}