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234 | 234 | "description: dict[str, str]={\n", |
235 | 235 | " \"title\": \"Income Prediction (Demo)\",\n", |
236 | 236 | " \"issuer\": \"XYZ Demo Solutions GmbH\",\n", |
| 237 | + " \"risk_class\": \"High Risk\",\n", |
| 238 | + " \"useCase\": \"Income Prediction\",\n", |
| 239 | + " \"role\": \"User\",\n", |
237 | 240 | " \"contact_intern\": \"Jon Doe\",\n", |
238 | 241 | " \"contact_extern\": \"Jane Doe\",\n", |
239 | 242 | " \"purpose\": \"The goal of this AI system is to estimate, based on demographic data, whether a person's income ...\",\n", |
|
251 | 254 | "cell_type": "code", |
252 | 255 | "source": [ |
253 | 256 | "from thetis import thetis\n", |
| 257 | + "from thetis import ThetisArgs\n", |
254 | 258 | "\n", |
255 | | - "\n", |
256 | | - "result = thetis(\n", |
| 259 | + "thetis_args = ThetisArgs(\n", |
257 | 260 | " config=\"demo_config_classification.yaml\",\n", |
258 | 261 | " description=description,\n", |
259 | 262 | " annotations=annotations,\n", |
260 | 263 | " predictions=predictions,\n", |
261 | 264 | " output_dir=\"./output\",\n", |
262 | 265 | " license_file_path=\"demo_license_classification.dat\"\n", |
263 | | - ")" |
| 266 | + ")\n", |
| 267 | + "\n", |
| 268 | + "result = thetis(thetis_args)" |
264 | 269 | ], |
265 | 270 | "outputs": [], |
266 | 271 | "execution_count": null |
267 | 272 | }, |
268 | 273 | { |
269 | 274 | "metadata": {}, |
270 | 275 | "cell_type": "code", |
271 | | - "outputs": [], |
272 | | - "execution_count": null, |
273 | 276 | "source": [ |
274 | 277 | "from IPython.display import IFrame\n", |
275 | 278 | "IFrame(\"./output/report.pdf\", width=800, height=1024)" |
276 | | - ] |
| 279 | + ], |
| 280 | + "outputs": [], |
| 281 | + "execution_count": null |
277 | 282 | } |
278 | 283 | ], |
279 | 284 | "metadata": { |
|
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