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🐾 Cat-Dog Classification with PocketFlow-Tutorial-Codebase-Knowledge
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🐾 Cat-Dog Classification with PocketFlow-Tutorial-Codebase-Knowledge

2 min read·
python pocket-flow-tutorial gemini
Table of Contents

Published on: 2025-07-21

Originally built in 2022, I have Cat-Dog Classification TFLite Flutter project served as an experiment in mobile machine learning. This project was recently revisited to evaluate how PocketFlow-Tutorial-Codebase-Knowledge can assist in documenting and explaining the workflow more effectively.

📦 Why Use PocketFlow-Tutorial-Codebase-Knowledge

PocketFlow-Tutorial-Codebase-Knowledge is an AI-powered tool that automatically generates documentation, tutorials, and diagrams by analyzing any codebase. Manual documentation, especially for older projects, can be time-consuming. This tool was tested to determine its effectiveness in handling the Cat-Dog classifier and streamlining the documentation process.

🚀 How It Was Used

1️⃣ Cloning the Tool

Begin with the 🚀 Getting Started section at PocketFlow GitHub. An error related to the model was encountered, requiring an update in utils/call_llm.py:

# model = os.getenv("GEMINI_MODEL", "gemini-2.5-pro-exp-03-25")
model = os.getenv("GEMINI_MODEL", "gemini-2.0-flash")

Run the following command to verify model compatibility:

python utils/call_llm.py
Verify model

2️⃣ Running the Tool

To generate documentation, the repository path must be provided. The tool will scan and analyze the project codebase. Example:

python main.py --repo https://github.com/malebolgia/Cat-Dog-Classification-tensor-flow-lite-flutter

Generated documentation will appear in the output/ directory.

Generating docs

📊 Process Overview

A simple flow of how PocketFlow-Tutorial-Codebase-Knowledge works:

🐶🐱 Output Summary

PocketFlow-Tutorial-Codebase-Knowledge generated the following:

  • A project overview
  • Setup instructions (including Windows batch file)
  • Model creation details
  • Code structure explanations and diagrams

The generated content serves as a solid starting point and can be refined as needed to match the project’s narrative.

💡 Tips for Effective Use

  • Always review and customize the generated content.
  • Use Mermaid diagrams to clarify structure and flow.
  • Re-run the tool after major updates with your app to maintain up-to-date documentation.

🎉 Conclusion

PocketFlow-Tutorial-Codebase-Knowledge simplifies the task of documenting machine learning projects. Whether revisiting an older project or seeking to automate technical documentation, this tool provides an efficient and intelligent starting point.

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