add support for open-interpreter.

tests/metagpt/actions/test_clone_function.py

@@ -0,0 +1,54 @@
+import pytest
+
+from metagpt.actions.clone_function import CloneFunction, run_fucntion_code
+
+
+source_code = """
+def user_indicator():
+    import pandas as pd
+    import ta
+
+    # 读取股票数据
+    stock_data = pd.read_csv('./tests/data/baba_stock.csv')
+    stock_data.head()
+    # 计算简单移动平均线
+    stock_data['SMA'] = ta.trend.sma_indicator(stock_data['Close'], window=6)
+    stock_data[['Date', 'Close', 'SMA']].head()
+    # 计算布林带
+    stock_data['bb_upper'], stock_data['bb_middle'], stock_data['bb_lower'] = ta.volatility.bollinger_hband_indicator(stock_data['Close'], window=20), ta.volatility.bollinger_mavg(stock_data['Close'], window=20), ta.volatility.bollinger_lband_indicator(stock_data['Close'], window=20)
+    stock_data[['Date', 'Close', 'bb_upper', 'bb_middle', 'bb_lower']].head()
+"""
+
+template_code = """
+def stock_indicator(stock_path: str, indicators=['Simple Moving Average', 'BollingerBands', 'MACD]) -> pd.DataFrame:
+    import pandas as pd
+    # here is your code.
+"""
+
+
+def get_expected_res():
+    import pandas as pd
+    import ta
+
+    # 读取股票数据
+    stock_data = pd.read_csv('./tests/data/baba_stock.csv')
+    stock_data.head()
+    # 计算简单移动平均线
+    stock_data['SMA'] = ta.trend.sma_indicator(stock_data['Close'], window=6)
+    stock_data[['Date', 'Close', 'SMA']].head()
+    # 计算布林带
+    stock_data['bb_upper'], stock_data['bb_middle'], stock_data['bb_lower'] = ta.volatility.bollinger_hband_indicator(stock_data['Close'], window=20), ta.volatility.bollinger_mavg(stock_data['Close'], window=20), ta.volatility.bollinger_lband_indicator(stock_data['Close'], window=20)
+    stock_data[['Date', 'Close', 'bb_upper', 'bb_middle', 'bb_lower']].head()
+    return stock_data
+
+
+@pytest.mark.asyncio
+async def test_clone_function():
+    clone = CloneFunction()
+    code = await clone.run(template_code, source_code)
+    assert 'def ' in code
+    stock_path = './tests/data/baba_stock.csv'
+    df, msg = run_fucntion_code(code, 'stock_indicator', stock_path)
+    assert not msg
+    expected_df = get_expected_res()
+    assert df.equals(expected_df)

tests/metagpt/tools/test_code_interpreter.py

@@ -0,0 +1,42 @@
+import pytest
+import pandas as pd
+from pathlib import Path
+
+from tests.data import sales_desc, store_desc
+from metagpt.tools.code_interpreter import OpenCodeInterpreter, OpenInterpreterDecorator
+from metagpt.actions import Action
+from metagpt.logs import logger
+
+
+logger.add('./tests/data/test_ci.log')
+stock = "./tests/data/baba_stock.csv"
+
+
+# TODO: 需要一种表格数据格式，能够支持schame管理的，标注字段类型和字段含义。
+class CreateStockIndicators(Action):
+    @OpenInterpreterDecorator(save_code=True, code_file_path="./tests/data/stock_indicators.py")
+    async def run(self, stock_path: str, indicators=['Simple Moving Average', 'BollingerBands']) -> pd.DataFrame:
+        """对stock_path中的股票数据, 使用pandas和ta计算indicators中的技术指标, 返回带有技术指标的股票数据，不需要去除空值, 不需要安装任何包；
+            指标生成对应的三列: SMA, BB_upper, BB_lower
+        """
+        ...
+
+
+@pytest.mark.asyncio
+async def test_actions():
+    # 计算指标
+    indicators = ['Simple Moving Average', 'BollingerBands']
+    stocker = CreateStockIndicators()
+    df, msg = await stocker.run(stock, indicators=indicators)
+    assert isinstance(df, pd.DataFrame)
+    assert 'Close' in df.columns
+    assert 'Date' in df.columns
+    # 将df保存为文件，将文件路径传入到下一个action
+    df_path = './tests/data/stock_indicators.csv'
+    df.to_csv(df_path)
+    assert Path(df_path).is_file()
+    # 可视化指标结果
+    figure_path = './tests/data/figure_ci.png'
+    ci_ploter = OpenCodeInterpreter()
+    ci_ploter.chat(f"使用seaborn对{df_path}中与股票布林带有关的数据列的Date, Close, SMA, BB_upper（布林带上界）, BB_lower（布林带下界）进行可视化, 可视化图片保存在{figure_path}中。不需要任何指标计算，把Date列转换为日期类型。要求图片优美，BB_upper, BB_lower之间使用合适的颜色填充。")
+    assert Path(figure_path).is_file()