Filter the data for the year 2019\nus_gdp_2019 = us_gdp[us_gdp['DATE'].dt.year == 2019]\n\n# Print the GDP for 2019\nprint(us_gdp_2019['GDP'].values)"}`            {'stdout': '[21104.133 21384.775 21694.282 21902.39 ]\n', 'stderr': '', 'fileLinks': [], 'exitCode': 0}The US GDP for each quarter in 2019 was as follows:        - Q1: 21104.133 billion dollars    - Q2: 21384.775 billion dollars    - Q3: 21694.282 billion dollars    - Q4: 21902.39 billion dollars        > Finished chain.    'The US GDP for each quarter in 2019 was as follows:\n\n- Q1: 21104.133 billion dollars\n- Q2: 21384.775 billion dollars\n- Q3: 21694.282 billion dollars\n- Q4: 21902.39 billion dollars'# Calculationsagent.run("What would the GDP be in 2030 if the latest GDP number grew by 50%?")            > Entering new AgentExecutor chain...        Invoking: `bearly_interpreter` with `{'python_code': "import pandas as pd\n\n# Load the data\nus_gdp = pd.read_csv('US_GDP.csv')\n\n# Get the latest GDP\nlatest_gdp = us_gdp['GDP'].iloc[-1]\n\n# Calculate the GDP in 2030 if the latest GDP number grew by 50%\ngdp_2030 = latest_gdp * 1.5\nprint(gdp_2030)"}`            {'stdout': '40594.518\n', 'stderr': '', 'fileLinks': [], 'exitCode': 0}If the latest GDP number grew by 50%, the GDP in 2030 would be approximately 40,594.518 billion dollars.        > Finished chain.    'If the latest GDP number grew by 50%, the GDP in 2030 would be approximately 40,594.518 billion dollars.'# Chart outputagent.run("Create a nice and labeled chart of the GDP growth over time")            > Entering new AgentExecutor chain...    Could not parse tool input: {'name': 'bearly_interpreter', 'arguments': '{\n  "python_code": "\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Load the data\ndf = pd.read_csv(\'US_GDP.csv\')\n\n# Convert the \'DATE\' column to datetime format\ndf[\'DATE\'] = pd.to_datetime(df[\'DATE\'])\n\n# Plot the data\nplt.figure(figsize=(10,6))\nplt.plot(df[\'DATE\'], df[\'GDP\'], label=\'US
Bearly Code Interpreter allows for remote execution of code. This makes it perfect for a code sandbox for agents, to allow for safe implementation of things like Code Interpreter
Bearly Code Interpreter allows for remote execution of code. This makes it perfect for a code sandbox for agents, to allow for safe implementation of things like Code Interpreter ->: Filter the data for the year 2019\nus_gdp_2019 = us_gdp[us_gdp['DATE'].dt.year == 2019]\n\n# Print the GDP for 2019\nprint(us_gdp_2019['GDP'].values)"}`            {'stdout': '[21104.133 21384.775 21694.282 21902.39 ]\n', 'stderr': '', 'fileLinks': [], 'exitCode': 0}The US GDP for each quarter in 2019 was as follows:        - Q1: 21104.133 billion dollars    - Q2: 21384.775 billion dollars    - Q3: 21694.282 billion dollars    - Q4: 21902.39 billion dollars        > Finished chain.    'The US GDP for each quarter in 2019 was as follows:\n\n- Q1: 21104.133 billion dollars\n- Q2: 21384.775 billion dollars\n- Q3: 21694.282 billion dollars\n- Q4: 21902.39 billion dollars'# Calculationsagent.run("What would the GDP be in 2030 if the latest GDP number grew by 50%?")            > Entering new AgentExecutor chain...        Invoking: `bearly_interpreter` with `{'python_code': "import pandas as pd\n\n# Load the data\nus_gdp = pd.read_csv('US_GDP.csv')\n\n# Get the latest GDP\nlatest_gdp = us_gdp['GDP'].iloc[-1]\n\n# Calculate the GDP in 2030 if the latest GDP number grew by 50%\ngdp_2030 = latest_gdp * 1.5\nprint(gdp_2030)"}`            {'stdout': '40594.518\n', 'stderr': '', 'fileLinks': [], 'exitCode': 0}If the latest GDP number grew by 50%, the GDP in 2030 would be approximately 40,594.518 billion dollars.        > Finished chain.    'If the latest GDP number grew by 50%, the GDP in 2030 would be approximately 40,594.518 billion dollars.'# Chart outputagent.run("Create a nice and labeled chart of the GDP growth over time")            > Entering new AgentExecutor chain...    Could not parse tool input: {'name': 'bearly_interpreter', 'arguments': '{\n  "python_code": "\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Load the data\ndf = pd.read_csv(\'US_GDP.csv\')\n\n# Convert the \'DATE\' column to datetime format\ndf[\'DATE\'] = pd.to_datetime(df[\'DATE\'])\n\n# Plot the data\nplt.figure(figsize=(10,6))\nplt.plot(df[\'DATE\'], df[\'GDP\'], label=\'US