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| import time | |
| import traceback | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| from streamlit_ace import st_ace | |
| from sklearn.compose import ColumnTransformer | |
| from sklearn.impute import SimpleImputer | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.preprocessing import FunctionTransformer | |
| from sklearn.preprocessing import LabelEncoder, MinMaxScaler, OneHotEncoder, OrdinalEncoder, RobustScaler, StandardScaler | |
| from utils.sanitize_code import sanitize_code | |
| def prep_meta_execution(agent, code, df, auto=False): | |
| edited = st_ace( | |
| value=code, | |
| height=400, | |
| theme="tomorrow_night", | |
| language="python", | |
| auto_update=True | |
| ) | |
| not_generated = agent.load_processed_df() is None | |
| if code is not None: | |
| if st.button("▶️ 执行预处理") or (auto and not_generated): | |
| code = sanitize_code(edited) | |
| agent.save_code(code) | |
| exec_ns = { | |
| "df": df, | |
| "np": np, | |
| "pd": pd, | |
| "st": st, | |
| "SimpleImputer": SimpleImputer, | |
| "FunctionTransformer": FunctionTransformer, | |
| "StandardScaler": StandardScaler, | |
| "MinMaxScaler": MinMaxScaler, | |
| "RobustScaler": RobustScaler, | |
| "OneHotEncoder": OneHotEncoder, | |
| "OrdinalEncoder": OrdinalEncoder, | |
| "LabelEncoder": LabelEncoder, | |
| "ColumnTransformer": ColumnTransformer, | |
| "Pipeline": Pipeline, | |
| } | |
| try: | |
| with st.spinner("正在运行程序..."): | |
| exec(code, exec_ns) | |
| except Exception as exc: | |
| st.error(f"已保存报错，请重新调用llm生成代码debug") | |
| st.text(traceback.format_exc()) | |
| agent.save_error(traceback.format_exc()) | |
| prep_code_gen(agent, debug=True) | |
| else: | |
| process_df = exec_ns.get("process_df") | |
| if process_df is None: | |
| st.error( | |
| "脚本未写入 `process_df`。请确保编辑后的脚本在末尾赋值 process_df" | |
| ) | |
| else: | |
| agent.save_processed_df(process_df) | |
| agent.finish_auto() | |
| st.rerun() | |
| return process_df | |
| def prep_code_gen(agent, auto = False, debug = False): | |
| suggest = agent.load_preprocessing_suggestions() | |
| df = agent.load_df() | |
| chat_history = agent.load_memory() | |
| already_generated = any( | |
| entry["role"] == "assistant" and "预处理脚本已更新！请重新运行代码！" in str(entry["content"]) | |
| for entry in chat_history | |
| ) | |
| if suggest is not None: | |
| if debug == True or (auto and not already_generated): | |
| with st.spinner("预处理 Agent 正在编写脚本..."): | |
| raw = agent.code_generation( | |
| df.head(10).to_string(), | |
| suggest, | |
| ) | |
| code = sanitize_code(raw) | |
| agent.save_code(code) | |
| st.chat_message("assistant").write("预处理脚本已更新！请重新运行代码！") | |
| agent.add_memory({"role": "assistant", "content": "预处理脚本已更新！请重新运行代码！"}) | |
| st.rerun() | |
| analyze_btn = st.button("🔧 生成预处理代码", key='prep_code') | |
| if analyze_btn: | |
| with st.spinner("向 LLM 请求生成预处理脚本..."): | |
| raw = agent.code_generation( | |
| df.head(10).to_string(), | |
| suggest, | |
| ) | |
| code = sanitize_code(raw) | |
| agent.save_code(code) | |
| st.chat_message("assistant").write("预处理脚本已更新！请重新运行代码！") | |
| agent.add_memory({"role": "assistant", "content": "预处理脚本已更新！请重新运行代码！"}) | |
| st.rerun() |