📊 Smart Finance Assistant – Student Project Template

Welcome to your project repository for the ISYS2001 Final Programming Project. This repo provides a starting point for building your Smart Finance Assistant.

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📖 Project Overview

In this project, you will design and implement a Smart Finance Assistant using:

• Python (Google Colab)
• hands-on-ai (chat, RAG, agent tools)
• Gradio (to create a simple app interface)

Your Assistant should include:

• Chat: a finance-oriented personality bot
• RAG: retrieval from CSV or other documents
• Agent Tool: one custom tool (e.g., budget calculator, currency converter)
• Gradio UI: a simple interface tying everything together
• Tests: a Testing Section in your notebook

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📂 Suggested Repo Layout

You may adapt this structure or create your own. Clarity and organisation are graded in the rubric.

/README.md            ← this file
/assignment.pdf       ← official assignment specification
/starter_notebook.ipynb  ← scaffold notebook with six-step method
/example_diary.md     ← sample Developer’s Diary entries
/data/                ← your CSVs or sample datasets
/tests/               ← your test scripts or asserts
/ai-conversations/    ← weekly AI Evidence Packages (screenshots, notes)
/docs/                ← pseudocode, design notes, planning docs

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🚀 Getting Started

1. Open the starter_notebook.ipynb in Google Colab.
2. Follow the six-step methodology:
   1. Understand the problem
   2. Identify inputs and outputs
   3. Work the problem by hand
   4. Write pseudocode
   5. Convert to Python
   6. Test with a variety of data
3. Add at least one meaningful GitHub commit per week (Weeks 8–12).
4. Document AI use in your Developer’s Diary (/ai-conversations/ folder or a markdown file).

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✅ Submission Requirements

• Colab Notebook with full project implementation
• GitHub repository with:
  • Notebook, README, and Developer’s Diary
  • Weekly AI Evidence Packages (Weeks 8–12)
  • Meaningful commit history
• Developer’s Diary entries that include:
  • Artifact: screenshot or snippet of AI use
  • Context: your goal
  • Reflection: what worked, what didn’t, what you learned

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📊 Assessment Criteria (Summary)

• Functionality – chatbot, RAG, tool, and UI integrated (30%)
• Testing & Debugging – clear tests, meaningful edge cases (20%)
• AI Collaboration & Progress – AI evidence + weekly commits (20%)
• Business Relevance – meaningful finance problem (15%)
• Clarity & Reflection – repo organisation, README, diary (15%)

For the full rubric, see assignment.pdf.

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📚 Resources

• hands-on-ai Package: GitHub Repository
• Documentation: DeepWiki Guide
• For AI Assistants: Share this LLM context file with ChatGPT/Claude/Copilot for better code suggestions

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💡 Tips

• Keep your commits small and descriptive.
• Use AI as a coding partner, not a crutch.
• Remember: undocumented AI use = misconduct.

Good luck, and have fun building your Finance Assistant! 🎉

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📜 License

The template code in this repository is licensed under the MIT License. See the LICENSE file for more details.

You are free to license your own work (your project code) under any license you choose.