Download scripts/quick_test.py from build-small-hackathon/CityQuest-AI: direct link, hf CLI and curl.
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https://huggingface.co/spaces/build-small-hackathon/CityQuest-AI/resolve/64cf79ddd182104117c0d364caa915cda170a7cf/scripts/quick_test.py
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hf download hf://spaces/build-small-hackathon/CityQuest-AI@64cf79ddd182104117c0d364caa915cda170a7cf/scripts/quick_test.py
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curl -L -o quick_test.py https://huggingface.co/spaces/build-small-hackathon/CityQuest-AI/resolve/64cf79ddd182104117c0d364caa915cda170a7cf/scripts/quick_test.py
653 Bytes
| """Quick test for diverse mock generation.""" | |
| import sys; sys.path.insert(0, '.') | |
| from app.services.generator import generate_game_mock | |
| from app.services.retrieval import load_games_dataset, normalize_game_record | |
| raw = load_games_dataset('app/data/games_dataset.json') | |
| norm = [normalize_game_record(r) for r in raw] | |
| cfg = { | |
| 'game_type': 'scavenger_hunt', 'city': 'Paris', 'area': 'Le Marais', | |
| 'duration_minutes': 60, 'num_players': 4, 'difficulty': 'medium', 'age_group': 'adults' | |
| } | |
| g = generate_game_mock(cfg, norm[:3]) | |
| print(g['title']) | |
| for t in g['tasks']: | |
| print(f" {t['task_id']}: {t['title']} | {t['points']}pts | {t['proof_type']}") |