Using ChatGpt in Google Colab | Learn python with chatgpt and Google Colab

Learning Python often turns into a loop of trial-and-error—until errors pile up and the “why” behind them stays unclear. This walkthrough shows a practical way to pair ChatGPT with Google Colab so coding help happens inside the notebook: a browser extension adds ChatGPT controls to Colab, then ChatGPT can (1) explain working code, (2) diagnose and fix bugs, and (3) refactor code into a cleaner, more maintainable form.

The process starts with installing a “ChatGPT for Google Colab” extension from the Chrome Web Store. After that, the user opens Google Colab, signs in, and creates a new notebook. The notebook then displays a ChatGPT button, enabling prompts to be sent from within the Colab environment. A key operational detail follows: ChatGPT requires an active login at chat.openai.com. If that session is closed, the Colab integration reports a “session expired” error, so the ChatGPT tab must remain available while working.

To demonstrate the workflow, the notebook runs a factorial program for a non-negative integer. The code initializes a number (example given: 5), defines factorial accumulation starting from 1, checks whether the input is negative, zero, or positive, and uses a loop to multiply values from 1 through n. When the “Explain” task is selected, ChatGPT returns a markdown-formatted breakdown: factorial is described as the product of all positive integers up to n; the conditional logic is mapped to negative/zero/positive cases; and the loop’s role is clarified through the example that 5! becomes 5×4×3×2×1 = 120. ChatGPT also points out how the commented input line can be uncommented to accept user input rather than relying on a hardcoded value.

Next comes error handling. The factorial code is intentionally broken by removing the definition of the number variable and introducing an indentation mistake. Using the “Fixed” (diagnose and fix) task, ChatGPT identifies two issues: a missing input/initialization for the variable num (so the program has no value to compute with) and incorrect indentation inside Python’s if blocks (Python requires indentation to define code blocks). ChatGPT provides an updated version that restores the input mechanism (either by uncommenting the input line or initializing num) and corrects indentation. Running the repaired code with an example input of 5 produces the expected output: factorial of 5 is 120.

Finally, the notebook tests additional ChatGPT tasks: “Summarize” generates a markdown summary of the code’s purpose and structure, while “Refactor” rewrites the program for better readability and maintainability. In the refactored version, factorial computation is moved into a dedicated compute factorial function, and the main flow is organized into a main function. Instead of printing errors for negative inputs, compute factorial returns a value (described as None for negative numbers), allowing the caller to handle error conditions more cleanly. The walkthrough ends by using an “Add” task to insert descriptive comments explaining each code section’s purpose and importance, reinforcing the idea that ChatGPT can improve both correctness and clarity while learning Python in Colab.