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from llm.llm import LLM |
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from input.problem import problem_input |
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from input.test_middle_result import problem_str, selected_models, modeling_solution, task_descriptions |
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from agent.model_selection import ModelSelection |
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from agent.modeling import Modeling |
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from agent.task_decompse import TaskDecompose |
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from agent.task import Task |
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from utils.utils import write_json_file, write_markdown_file, json_to_markdown |
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if __name__ == "__main__": |
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llm = LLM('deepseek-chat') |
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paper = {'tasks': []} |
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problem_path = 'data/actor_data/input/problem/2024_C.json' |
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problem_str, problem = problem_input(problem_path, llm) |
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paper['problem_background'] = problem['background'] |
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paper['problem_requirement'] = problem['problem_requirement'] |
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ms = ModelSelection(llm) |
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selected_models = ms.select_models(problem_str) |
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print(selected_models) |
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print('---') |
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mm = Modeling(llm) |
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modeling_solution = mm.modeling(problem_str, selected_models) |
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print(modeling_solution) |
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print('---') |
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td = TaskDecompose(llm) |
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task_descriptions = td.decompose(problem_str, modeling_solution) |
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print(task_descriptions) |
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print('---') |
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task = Task(llm) |
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for task_description in task_descriptions[:]: |
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task_analysis = task.analysis(task_description) |
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task_modeling = task.modeling(task_description, task_analysis, problem['data_summary']) |
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task_result = task.result(task_description, task_analysis, task_modeling) |
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task_answer = task.answer(task_description, task_analysis, task_modeling, task_result) |
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paper['tasks'].append({ |
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'task_description': task_description, |
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'task_analysis': task_analysis, |
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'mathematical_modeling_process': task_modeling, |
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'result': task_result, |
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'answer': task_answer |
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}) |
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print(paper) |
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print(llm.get_total_usage()) |
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write_json_file('data/actor_data/output/paper4.json', paper) |
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write_markdown_file('data/actor_data/output/paper4.md', json_to_markdown(paper)) |
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