from llm.llm import LLM from prompt.constants import modeling_methods from input.problem import problem_input # from input.test_middle_result import problem_str, problem_analysis, selected_models, modeling_solution, modeling_solution, task_descriptions from agent.problem_analysis import ProblemAnalysis from agent.method_ranking import MethodRanking from agent.problem_modeling import ProblemModeling from agent.task_decompse import TaskDecompose from agent.task import Task from agent.create_charts import Chart from agent.coordinator import Coordinator from utils.utils import read_json_file, write_json_file, write_text_file, json_to_markdown from prompt.template import TASK_ANALYSIS_APPEND_PROMPT, TASK_FORMULAS_APPEND_PROMPT, TASK_MODELING_APPEND_PROMPT # from utils.convert_format import markdown_to_latex import os from datetime import datetime import shutil import time def run_batch(problem_path, config, name, dataset_path, output_path): # Initialize LLM llm = LLM(config['model_name']) # Get problem input problem_str, problem = problem_input(problem_path, llm) problem_type = os.path.splitext(os.path.basename(problem_path))[0].split('_')[-1] # Initialize paper dictionary paper = {'tasks': []} paper['problem_background'] = problem['background'] paper['problem_requirement'] = problem['problem_requirement'] # Problem analysis pa = ProblemAnalysis(llm) problem_analysis = pa.analysis(problem_str, round=config['problem_analysis_round']) paper['problem_analysis'] = problem_analysis modeling_methods = "" # High level probelm understanding modeling pm = ProblemModeling(llm) modeling_solution = pm.modeling(problem_str, problem_analysis, modeling_methods, round=config['problem_modeling_round']) # Task decomposition td = TaskDecompose(llm) task_descriptions = td.decompose_and_refine(problem_str, problem_analysis, modeling_solution, problem_type, config['tasknum']) # Analyze dependency with_code = len(problem['dataset_path']) > 0 coordinator = Coordinator(llm) order = coordinator.analyze_dependencies(problem_str, problem_analysis, modeling_solution, task_descriptions, with_code) order = [int(i) for i in order] if with_code: shutil.copytree(dataset_path, os.path.join(output_path,'code'), dirs_exist_ok=True) # Process tasks task = Task(llm) mr = MethodRanking(llm) chart = Chart(llm) for id in order: task_dependency = [int(i) for i in coordinator.DAG[str(id)]] dependent_file_prompt = "" if len(task_dependency) > 0: dependency_prompt = f"""\ This task is Task {id}, which depends on the following tasks: {task_dependency}. The dependencies for this task are analyzed as follows: {coordinator.task_dependency_analysis[id - 1]} """ for task_id in task_dependency: dependency_prompt += f"""\ --- # The Description of Task {task_id}: {coordinator.memory[str(task_id)]['task_description']} # The modeling method for Task {task_id}: {coordinator.memory[str(task_id)]['mathematical_modeling_process']} """ if with_code: dependency_prompt += f"""\ # The structure of code for Task {task_id}: {coordinator.code_memory[str(task_id)]} # The result for Task {task_id}: {coordinator.memory[str(task_id)]['solution_interpretation']} --- """ dependent_file_prompt += f"""\ # The files generated by code for Task {task_id}: {coordinator.code_memory[str(task_id)]} """ coordinator.code_memory[str(task_id)]['file_outputs'] else: dependency_prompt += f"""\ # The result for Task {task_id}: {coordinator.memory[str(task_id)]['solution_interpretation']} --- """ task_analysis_prompt = dependency_prompt + TASK_ANALYSIS_APPEND_PROMPT task_formulas_prompt = dependency_prompt + TASK_FORMULAS_APPEND_PROMPT task_modeling_prompt = dependency_prompt + TASK_MODELING_APPEND_PROMPT else: task_analysis_prompt = "" task_formulas_prompt = "" task_modeling_prompt = "" code_template = open(os.path.join('data/actor_data/input/code_template','main{}.py'.format(id))).read() save_path = os.path.join(output_path,'code/main{}.py'.format(id)) work_dir = os.path.join(output_path,'code') script_name = 'main{}.py'.format(id) task_description = task_descriptions[id - 1] task_analysis = task.analysis(task_analysis_prompt, task_description) description_and_analysis = f'## Task Description\n{task_description}\n\n## Task Analysis\n{task_analysis}' top_modeling_methods = mr.top_methods(description_and_analysis, top_k=config['top_method_num']) task_formulas = task.formulas(task_formulas_prompt, problem['data_description'], task_description, task_analysis, top_modeling_methods, round=config['task_formulas_round']) task_modeling = task.modeling(task_modeling_prompt, problem['data_description'], task_description, task_analysis, task_formulas) if with_code: task_code, is_pass, execution_result = task.coding(problem['dataset_path'], problem['data_description'], problem['variable_description'], task_description, task_analysis, task_formulas, task_modeling, dependent_file_prompt, code_template, script_name, work_dir) code_structure = task.extract_code_structure(id, task_code, save_path) task_result = task.result(task_description, task_analysis, task_formulas, task_modeling, execution_result) task_answer = task.answer(task_description, task_analysis, task_formulas, task_modeling, task_result) task_dict = { 'task_description': task_description, 'task_analysis': task_analysis, 'preliminary_formulas': task_formulas, 'mathematical_modeling_process': task_modeling, 'task_code': task_code, 'is_pass': is_pass, 'execution_result': execution_result, 'solution_interpretation': task_result, 'subtask_outcome_analysis': task_answer } coordinator.code_memory[str(id)] = code_structure else: task_result = task.result(task_description, task_analysis, task_formulas, task_modeling) task_answer = task.answer(task_description, task_analysis, task_formulas, task_modeling, task_result) task_dict = { 'task_description': task_description, 'task_analysis': task_analysis, 'preliminary_formulas': task_formulas, 'mathematical_modeling_process': task_modeling, 'solution_interpretation': task_result, 'subtask_outcome_analysis': task_answer } coordinator.memory[str(id)] = task_dict charts = chart.create_charts(str(task_dict), config['chart_num']) task_dict['charts'] = charts paper['tasks'].append(task_dict) save_paper(paper, name, output_path) print(paper) print('Usage:', llm.get_total_usage()) write_json_file(f'{output_path}/usage/{name}.json', llm.get_total_usage()) return paper def save_paper(paper, name, path): write_json_file(f'{path}/json/{name}.json', paper) markdown_str = json_to_markdown(paper) write_text_file(f'{path}/markdown/{name}.md', markdown_str) # write_text_file(f'data/actor_data/output/latex/{name}.tex', markdown_to_latex(markdown_str)) def mkdir(path): os.mkdir(path) os.mkdir(path + '/json') os.mkdir(path + '/markdown') os.mkdir(path + '/latex') os.mkdir(path + '/code') os.mkdir(path + '/usage') if __name__ == "__main__": import glob file_name_list = [] for year in range(2025, 2026): if year == 2025: letters = "CDEF" else: letters = "ABCDEF" for letter in letters: file_name_list.append(f'data/actor_data/input/problem/{year}_{letter}*') files = [] for pattern in file_name_list: files.extend(glob.glob(pattern)) config_list = [{ 'top_method_num': 6, 'problem_analysis_round': 1, 'problem_modeling_round': 1, 'task_formulas_round': 1, 'tasknum': 4, 'chart_num': 3, 'model_name': 'gpt-4o', "method_name": "MM-Agent-gpt-4o-v3-probelm-modleing" # 'model_name': 'chatgpt-4o-latest' }] for i, config in enumerate(config_list, start=1): for file in files: try: name = file.split('/')[-1].split('.')[0] dataset_path = os.path.join('data/actor_data/input/dataset', file.split('/')[-1].split('.')[0]) output_dir = 'data/actor_data/exps/{}'.format(config["method_name"]) if not os.path.exists(output_dir): os.makedirs(output_dir) output_path = os.path.join(output_dir, name + '_{}'.format(datetime.now().strftime('%Y%m%d-%H%M%S'))) if not os.path.exists(output_path): mkdir(output_path) print(f'Processing {file}..., config: {config}') start = time.time() paper = run_batch(problem_path=file, config=config, name=name, dataset_path=dataset_path, output_path=output_path) end = time.time() with open(output_path + '/usage/runtime.txt', 'w') as f: f.write("{:.2f}s".format(end - start)) # save_paper(paper, name) except Exception as e: raise print(f'Error: {e}') continue