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63 lines
1.5 KiB
Python
63 lines
1.5 KiB
Python
#https://www.analyticsvidhya.com/blog/2020/09/precision-recall-machine-learning/
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import os
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from data_importer import GherkinData
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from html_gui import setup_ui
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from nicegui import ui
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from standalone_rimay import results, start_gherkin_translation
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import sys
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client = None
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# Read all gherkin data
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# Score systeem.
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# Log all outputs
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# improve prompt techniques.
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n = len(sys.argv)
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print("Total arguments passed:", n)
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# all gherkin data.
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# Gherkin acceptance criteria inspriation comes from:
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# Open_Source_Projects_GitHub_US_AC_Analysis.xlsx
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# https://zenodo.org/records/6460854
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gherkin_acceptance_criteria = GherkinData() #GherkinData
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all_acceptance_criteria = gherkin_acceptance_criteria.get_all_acceptance_criteria()
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gui = str(sys.argv[1])
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if gui == "standalone":
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range_start = int(sys.argv[2]) or 0
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range_end = int(sys.argv[3]) or 20
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techniek = sys.argv[4] or "Few-shot-learning"
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temp = sys.argv[5] or 0.2
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print(gherkin_acceptance_criteria)
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print(len(all_acceptance_criteria))
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# Standalone?
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for scenario in all_acceptance_criteria[range_start:range_end]:
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scenario_name = scenario["scenario_name"]
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content_acceptance_criteria = scenario["simplified"]
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print(scenario_name+"\n")
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start_gherkin_translation(scenario_name, content_acceptance_criteria, techniek, temp)
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elif gui == "results":
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techniek = str(sys.argv[2]) or "Few-shot-learning"
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data_directory = f"output_dataset/{techniek}/"
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results(data_directory)
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else:
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setup_ui()
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ui.run()
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