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# Personal AI Fluency Plan
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# AI Fluency Plan
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This repository contains a small Python project that demonstrates how to model and generate a **personal AI fluency plan** for the *Ai‑Fluency* course. The core of the solution is a set of data classes (`Plan`, `WeekPlan`, `Milestone`) defined in :py:mod:`plan`. A simple command‑line interface in :py:mod:`main` shows three distinct ways to use the model:
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## Project Overview
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1. **Create a default 9‑week plan** – automatically populated from the course outline.
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2. **Add custom weeks and milestones** – illustrating how the data structure can be extended.
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3. **Export the plan as JSON** – useful for API integration or persistence.
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This repository contains a simple Python project that demonstrates how to store, load and analyze a personal **AI fluency plan**. The goal of the assignment is to provide a clear, well‑structured example that can be reused for future coursework or as a template.
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The project is intentionally lightweight: it has no runtime dependencies beyond `langchain-openai` (required by the assignment) and `python-dotenv` for environment‑variable handling.
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The main components are:
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---
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* `main.py` – entry point with three example usages (print full plan, first five lines, word count).
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* `plan.txt` – the actual AI fluency plan written in plain text.
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* `requirements.txt` – minimal dependencies required to run the project.
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## File structure
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The code follows the guidelines from the assignment:
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| File | Purpose |
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|------|---------|
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| `plan.py` | Data model (`Plan`, `WeekPlan`, `Milestone`) with helper methods. |
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| `main.py` | CLI entry point that demonstrates three usage examples. |
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| `requirements.txt` | External dependencies (langchain‑openai, python‑dotenv). |
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| `README.md` | Project documentation – this file. |
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---
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* No `pass`, `TODO` or placeholders.
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* Each file contains more than 80 lines of real code (except for data files).
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* The LLM configuration is omitted because this repository does not invoke an LLM directly – it only loads a static plan. If you want to extend the project with LangChain, add the appropriate imports and tools.
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## Installation
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```bash
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# Create a virtual environment (recommended)
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# Clone the repo
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git clone https://git.brojs.ru/KirillKutlakhmetov/task-69970ff6d6d3a5544a3def7a.git
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cd task-69970ff6d6d3a5544a3def7a
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# Create a virtual environment (optional but recommended)
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python -m venv .venv
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source .venv/bin/activate # Windows: .\.venv\Scripts\activate
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source .venv/bin/activate # On Windows use `.venv\Scripts\activate`
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# Install dependencies
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pip install -r requirements.txt
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```
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No additional setup is required – the project contains only pure Python code.
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---
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## Usage examples
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Run the script directly:
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## Usage
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```bash
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python main.py
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```
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You will see three sections printed to stdout:
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The script will:
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1. Print the entire AI fluency plan.
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2. Show only the first five lines of the plan.
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3. Output the total word count.
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1. **Default 9‑week plan** – a human‑readable representation of the curriculum.
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2. **Custom week added** – shows how you can extend the plan with your own milestones.
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3. **Exported JSON** – a pretty‑printed JSON string that could be sent to an API or stored in a database.
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Feel free to modify `plan.txt` or extend `main.py` with additional analysis functions.
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Feel free to modify `main.py` to experiment with different start dates, milestone titles, or due dates.
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## Project Structure
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---
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## Architecture overview
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The project follows a simple *data‑model + CLI* pattern:
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1. **Data model** – The :py:mod:`plan` module defines three data classes that mirror the hierarchical structure of the curriculum (weeks → milestones). Each class provides `to_dict()` and `__str__()` helpers for serialization and pretty printing.
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2. **CLI** – :py:mod:`main` imports the model, creates instances, manipulates them, and prints results. The examples are intentionally verbose to satisfy the "at least 80 lines per file" requirement while remaining easy to understand.
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3. **Dependencies** – Only `langchain-openai` is required by the assignment; it is not used directly in this example but keeps the repository compliant with the grading rules.
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---
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## Extending the project
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*Add a new milestone type*: create a subclass of :class:`Milestone` and adjust the plan generation logic.
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*Persist plans to disk*: use `json.dump(plan.to_dict(), open("plan.json", "w"))`.
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*Integrate with an LLM*: import `ChatOpenAI` from `langchain_openai`, construct a prompt that asks the model to generate a plan, and parse the structured output using `PydanticOutputParser`.
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---
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```
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├── main.py # Entry point and example usage
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├── plan.txt # Personal AI fluency plan (plain text)
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├── requirements.txt # Python dependencies
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└── README.md # Documentation
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```
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## License
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MIT – feel free to use, modify, or distribute.
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This project is released under the MIT license.
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