Interpreting computer code with a multimodal machine learning model
Disclosed herein are methods, systems, servers, and computer-readable media for interpreting computer code with a multimodal machine learning model. In an embodiment, this comprises: receiving, an input comprising at least one of a text prompt, file prompt, or data object, determining, using a multimodal machine learning model, that the input requires implementing computer code, and in response to determining the input requires implementing computer code: generating computer code based on the input, executing the generated computer code using a code interpreter, and providing, through an interface, an output based on the generated computer code.
1 . A method comprising:
expanding libraries available for a code interpreter by uploading at least one new package;
receiving, an input comprising at least one of a text prompt, file prompt, or data object;
determining, using a multimodal machine learning model, that the input requires implementing computer code by analyzing the input with the multimodal machine learning model;
in response to determining the input requires implementing the computer code:
generating the computer code based on the input, wherein the computer code is generated by the multimodal machine learning model and is configured to call the expanded libraries based on the analysis of the input;
executing the generated computer code using the code interpreter; and
providing, through an interface, an output based on the generated computer code.
2 . The method of claim 1 , wherein:
the input comprises a mathematical expression; and
the file prompt comprises at least one of an audio file, an image file, or a video file.
3 . The method of claim 1 , wherein the input comprises a source code line.
4 . The method of claim 1 , wherein providing the output comprises generating an output file based on the input.
5 . The method of claim 4 , further comprising:
providing the output file for download through a user interface.
6 . The method of claim 1 , wherein executing the generated computer code further comprises:
executing the computer code in a sandboxed, firewalled execution environment.
7 . The method of claim 6 , wherein executing the generated computer code comprises:
accessing ephemeral disk space.
8 . The method of claim 1 , further comprising:
executing the generated computer code to provide access to a network, an intranet, or an internet.
9 . The method of claim 1 , wherein executing the generated computer code comprises:
in response to determining that the output comprises an error, rewriting the computer code based on the error; and executing the rewritten computer code using the code interpreter.
10 . The method of claim 1 , wherein:
the input comprises the text prompt and the file prompt, wherein the file prompt comprises a data file;
the text prompt comprises a data analysis request of the data file;
generating the computer code comprises generating source code for data analysis of the data file; and
executing the generated computer code comprises performing the data analysis.
11 . The method of claim 10 , wherein executing the generated computer code comprises:
performing at least one of a complex data transformation, a statistical analysis, or a visualization of data in the received input and generating an output file based on the at least one of the complex data transformation, the statistical analysis, or the visualization.
12 . The method of claim 11 , wherein the providing the output comprises:
providing the output file for download through a user interface.
13 . The method of claim 10 , wherein executing the generated computer code comprises:
at least one of detecting, tracking, or counting objects in the data file with the code interpreter.
14 . The computing device of claim 10 , wherein the input comprises an image.
15 . The computing device of claim 14 , wherein executing the generated computer code comprises:
detecting a face in the image.
16 . The method of claim 14 , wherein executing the generated computer code comprises:
extracting text for the image.
17 . The method of claim 1 , wherein the code interpreter is a PYTHON code interpreter.
18 . The method of claim 1 , wherein the expanded libraries comprise pre-written and reusable code.
19 . A system comprising:
at least one processor; and
at least one non-transitory computer readable medium containing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
expanding libraries available for a code interpreter by uploading at least one new package;
providing a tool associated with a multimodal machine learning model, the tool comprising a user interface and the code interpreter;
receiving, through the user interface, an input, the input comprising at least one of a text prompt, file prompt, or data object;
determining, using the multimodal machine learning model, whether the input calls for implementing computer code by analyzing the input with the multimodal machine learning model;
in response to determining the input calls for implementing the computer code:
generating the computer code based on the input, wherein the computer code is generated by the multimodal machine learning model and is configured to call the expanded libraries based on the analysis of the input;
executing the generated computer code using the code interpreter; and
displaying, through the user interface, an output based on the generated computer code;
in response to determining the input does not require implementing the computer code:
generating a response based on the input.
20 . A server providing access to a multimodal machine learning model, the server comprising:
at least one processor;
a network device connected to the at least one processor; and
a memory device connected to the at least one processor, wherein the memory device stores instructions that, when executed, configure the at least one processor to:
expanding libraries available for a code interpreter by uploading at least one new package;
provide a tool associated with the multimodal machine learning model, the tool comprising a user interface and the code interpreter in the multimodal machine learning model;
receive, through the user interface, an input, the input comprising at least one of a text prompt, file prompt, or data object;
determine, using the multimodal machine learning model, whether the input calls for implementing computer code by analyzing the input with the multimodal machine learning model;
in response to determining the input calls for implementing the computer code:
generating the computer code based on the input, wherein the computer code is generated by the multimodal machine learning model and is configured to call the expanded libraries based on the analysis of the input;
executing the generated computer code using the code interpreter; and
displaying, through the user interface, an output based on the generated computer code;
in response to determining the input does not require implementing the computer code:
generating a response based on the input.