сырой текст задания → плоская карточка: README.md

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-# task-69dd4221-syroy-tekst-zadaniya-plos
+# Project: Raw Text → Flat Task Card

-Решения домашних заданий
+## 📖 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"
+```
+
+---
+
+## 🛠️ 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.
+
+---
+
+## 📄 License
+
+MIT © 2026
+
+---