Spaces:
Running
Running
File size: 4,048 Bytes
342e4c4 495d32d 342e4c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
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，正在重新调用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() |