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