dnr. 2017-03134 financed by the Swedish research council led by Petra Herzfeld Olssonat Stockholm University. The authors would like to thank her and other participants, Niklas\nBruun and Erik Sj√∂din for their helpful comments on earlier drafts. A much shorter article titled\n‚ÄòRisk Assessment and COVID -19: Systems at work (or not) in England and Sweden‚Äô is published\nin the (2021) Comparative Labour and Social Security Review /\n Revue de droit compar√© du\ntravail et de la s√©curit√© sociale.\n1 Public Health England, Disparities in the risk and outcomes of COVID-19 (2 June 2020 -\nhttps://assets.publishing.service.gov.uk/government/uploads/ system /uploads/attachment_data/file\n/890258/disparities_review.pdf.\n2 Nisreen A. Alwan, ‚ÄòTrack COVID- 19 sickness, not just positive tests and deaths‚Äô ( 2020)\n584.7820 Nature  170- 171; Elisabeth Mahase, ‚ÄòCovid-19: What do we know about ‚Äúlong covid‚Äù?‚Äô\n(2020) BMJ  370.\n3 Sarah Dryhurst, Claudia R. Schneider, John Kerr, Alexandra LJ Freeman, Gabriel Recchia,\nAnne Marthe Van Der Bles, David Spiegelhalter, and Sander van der Linden, ‚ÄòRisk perceptionsof COVID-19 around the world‚Äô (2020) 23(7- 8) Journal of Risk Research  994; W√§ndi Bruine de\nBruin, and Daniel Bennett, ‚ÄòRelationships between initial COVID -19 risk perceptions and\nprotective health behaviors: A national survey‚Äô (2020) 59(2) American Journal of Preventive\nMedicine  157; and Simon Deakin and Gaofeng Meng, ‚ÄòThe Governance of Covid- 19:\nAnthropogenic Risk, Evolutionary Learning, and the Future of the Social State‚Äô (2020)49(4) Industrial Law Journal  539."'# Simple Queriesagent.run("What was the US GDP in 2019?")            > 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# Convert the 'DATE' column to datetime\nus_gdp['DATE'] = pd.to_datetime(us_gdp['DATE'])\n\n# Filter the data for the year
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 ->: dnr. 2017-03134 financed by the Swedish research council led by Petra Herzfeld Olssonat Stockholm University. The authors would like to thank her and other participants, Niklas\nBruun and Erik Sj√∂din for their helpful comments on earlier drafts. A much shorter article titled\n‚ÄòRisk Assessment and COVID -19: Systems at work (or not) in England and Sweden‚Äô is published\nin the (2021) Comparative Labour and Social Security Review /\n Revue de droit compar√© du\ntravail et de la s√©curit√© sociale.\n1 Public Health England, Disparities in the risk and outcomes of COVID-19 (2 June 2020 -\nhttps://assets.publishing.service.gov.uk/government/uploads/ system /uploads/attachment_data/file\n/890258/disparities_review.pdf.\n2 Nisreen A. Alwan, ‚ÄòTrack COVID- 19 sickness, not just positive tests and deaths‚Äô ( 2020)\n584.7820 Nature  170- 171; Elisabeth Mahase, ‚ÄòCovid-19: What do we know about ‚Äúlong covid‚Äù?‚Äô\n(2020) BMJ  370.\n3 Sarah Dryhurst, Claudia R. Schneider, John Kerr, Alexandra LJ Freeman, Gabriel Recchia,\nAnne Marthe Van Der Bles, David Spiegelhalter, and Sander van der Linden, ‚ÄòRisk perceptionsof COVID-19 around the world‚Äô (2020) 23(7- 8) Journal of Risk Research  994; W√§ndi Bruine de\nBruin, and Daniel Bennett, ‚ÄòRelationships between initial COVID -19 risk perceptions and\nprotective health behaviors: A national survey‚Äô (2020) 59(2) American Journal of Preventive\nMedicine  157; and Simon Deakin and Gaofeng Meng, ‚ÄòThe Governance of Covid- 19:\nAnthropogenic Risk, Evolutionary Learning, and the Future of the Social State‚Äô (2020)49(4) Industrial Law Journal  539."'# Simple Queriesagent.run("What was the US GDP in 2019?")            > 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# Convert the 'DATE' column to datetime\nus_gdp['DATE'] = pd.to_datetime(us_gdp['DATE'])\n\n# Filter the data for the year