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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)
# print(problem_str)
# print('---')
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))
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