# Project: Raw Text → Flat Task Card

## 📖 Overview

This project turns a natural‑language description of an assignment into a **structured, machine‑readable card**.  
Given a single sentence or short paragraph from a teacher (e.g., “Write a report on LangChain”), the script uses OpenAI via LangChain to produce a validated `TaskCard` object containing:

| Field | Description |
|-------|-------------|
| `title` | Short title of the task |
| `subject` | Subject area (optional) |
| `deadline_hint` | Free‑form hint about when it’s due |
| `deliverable_type` | What to submit (report, code, presentation…) |
| `grading_hints` | List of grading criteria mentioned |

The output is printed as JSON and a concise summary.

---

## 🚀 Installation

```bash
# Clone the repo (or copy the file)
git clone https://github.com/your‑repo/task‑card.git
cd task-card

# Create virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\\Scripts\\activate

# Install dependencies
pip install langchain-core langchain-openai pydantic python-dotenv
```

> **Environment variables**  
> The script uses `OPENAI_API_KEY`. Create a `.env` file in the project root:

```dotenv
OPENAI_API_KEY=sk-...
```

or export it directly:

```bash
export OPENAI_API_KEY="sk-..."
```

---

## 📦 Usage

The main entry point is `solution.py`.

### 1. Run with an inline string

```bash
python solution.py "Write a short report on LangChain and its applications."
```

**Output**

```json
{
  "title": "Short Report on LangChain",
  "subject": null,
  "deadline_hint": "Submit by the end of the week.",
  "deliverable_type": "report",
  "grading_hints": [
    "Clarity of explanation",
    "Depth of examples"
  ]
}
```

### 2. Run with a file

Create `task.txt` containing your assignment description:

```text
Develop a Python script that uses LangChain to parse user input and produce a structured task card.
```

Run:

```bash
python solution.py -f task.txt
```

The same JSON will be printed.

### 3. Using the output programmatically

You can import `TaskCard` from `solution.py` in another Python script:

```python
from solution import TaskCard, parse_task_description

description = "Create a presentation on AI ethics."
card: TaskCard = parse_task_description(description)
print(card.title)          # -> "Presentation on AI Ethics"
```

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## 🛠️ How It Works

1. **Prompt** – A `PromptTemplate` instructs the model to output JSON matching the `TaskCard` schema.
2. **LLM** – `ChatOpenAI` (any OpenAI-compatible model) processes the prompt.
3. **Parser** – `PydanticOutputParser` validates and converts the raw text into a `TaskCard`.
4. **Result** – The script prints the JSON representation and a short human‑readable summary.

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## 📄 License

MIT © 2026

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