Documentation-for-Main-Files.md

+This page contains documentation for the main files used to run MARCO
+
+<details><summary>agent.py</summary>
+<p>
+This is the main file used to run MARCO. It contains the specifications for MARCO’s UI, and is also where the functions used to optimize code are called.
+
+Lines 1-5 import the required packages and files. streamlit is the framework used to build MARCO’s frontend. agent_functions contains the functions that concern interacting with the LLM. asyncio is a Python library that allows for concurrent programming, allowing process_optimization() to wait for input from functions in agent_functions. base64 was used for encoding a background image and is no longer used in the file. time is used in the typewriter_effect_code function to simulate dynamic text output.
+
+Lines 8-83 set the formatting for MARCO’s webpage using the markdown() function from streamlit.
+
+Lines 85-92 define create_error_message(), which creates error messages as needed.
+
+Lines 95-102 define typewriter_effect_code(), which allows the text to output as if it is being typed one letter at a time, instead of all at once.
+
+Lines 105-106 initialize the session.
+
+Lines 109-114 control the layout of the main content area (i.e., the title, the textbox for the code, and the controls for selecting the LLM and number of techniques used)
+
+Lines 116-144 define the process_optimization(), which gets the optimization techniques/optimized code using functions in agent_functions.py and outputs them (along with the unoptimized code imputed by the user, for comparison purposes.
+
+Lines 146-150 govern what happens when the user presses the “Improve Code” button. If there is code in the textbox, process_optimization() is called to start the optimization process. Otherwise, a warning message is outputted asking the user to input code.
+</p>
+</details>
+
+<details><summary>agent_functions.py</summary>
+<p>
+This file contains the functions that require direct interaction with the LLM. These functions are called within process_optimization() in agent.py.
+
+Lines 1-7 import the required packages. The primary framework used is LangChain, allowing us to power MARCO with models developed by OpenAI. Most of the other imports deal with file access.
+
+Line 9 gets necessary variables from the .env environment file, such as the API keys used for accessing the model.
+
+Line 10 initializes llm, the variable that will be used the LLM.
+
+Lines 13-20 define the select_model() function, which takes the model selected by the user and uses it to set llm. This function also sets llm as a global variable.
+
+Lines 22-45  define the get_optimization_techniques() function. This function asks the model for a list of optimization techniques (with the exact number of techniques set by the user), and does some formatting before returning the list of techniques.
+
+Lines 47-58 define the get_subsets() function. This was previously used in formatting the list of optimization techniques, but is now unused.
+
+Lines 60-77 define the improve_code() function. This function asks the model to optimize the code using the previously generated optimization techniques, and returns the user’s imputed code optimized using each technique.
+</p>
+</details>
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