diff --git a/main.py b/main.py
index ec2f237..a7147e1 100644
--- a/main.py
+++ b/main.py
@@ -1,9 +1,19 @@
 """
 Main entry point for the "сырой текст задания → плоская карточка" task.
 
-The script demonstrates how to convert a free‑form description of an assignment into a structured data object using LangChain and Pydantic.
+The script demonstrates how to:
+1. Define a Pydantic model that represents a parsed task card.
+2. Build a LangChain chain that takes an informal description and returns a validated
+   :class:`TaskCard` instance.
+3. Print the raw JSON, the ``model_dump`` representation and a human‑readable summary.
 
-Usage examples are provided in the ``__main__`` section – three different raw texts are parsed and printed.
+The example is intentionally self‑contained – it does not rely on any external files
+and can be executed with:
+
+```bash
+pip install -r requirements.txt
+python main.py
+```
 """
 
 from __future__ import annotations
@@ -11,59 +21,15 @@ from __future__ import annotations
 import os
 from typing import List
 
-from langchain_openai import ChatOpenAI
 from langchain_core.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
 from langchain_core.output_parsers import PydanticOutputParser
-from pydantic import BaseModel, Field
+
+# Import the model defined in models.py
+from models import TaskCard
 
 # ---------------------------------------------------------------------------
-# 1. Define the data model that represents a task card.
-# ---------------------------------------------------------------------------
-class TaskCard(BaseModel):
-    """Structured representation of an assignment description.
-
-    The fields are intentionally generic – they capture the most common pieces of information
-    that appear in the course tasks:
-
-    * ``title`` – short name of the task.
-    * ``subject`` – subject or topic area.
-    * ``deadline_hint`` – free‑form hint about when the task should be finished.
-    * ``deliverable_type`` – what is expected to be submitted (report, code, presentation…).
-    * ``grading_hints`` – list of items that influence grading.
-    """
-
-    title: str = Field(..., description="Short name of the task")
-    subject: str | None = Field(None, description="Subject or topic area")
-    deadline_hint: str | None = Field(
-        None,
-        description="Free‑form hint about when the task should be finished",
-    )
-    deliverable_type: str | None = Field(
-        None,
-        description="What is expected to be submitted (report, code, presentation…)",
-    )
-    grading_hints: List[str] = Field(
-        default_factory=list,
-        description="List of items that influence grading",
-    )
-
-# ---------------------------------------------------------------------------
-# 2. Build the prompt and parser.
-# ---------------------------------------------------------------------------
-parser = PydanticOutputParser(pydantic_object=TaskCard)
-
-prompt_template = PromptTemplate(
-    template=(
-        "You are an assistant that extracts structured information from a free‑form task description."
-        " Return only the data in JSON format that matches the following schema:\n{format_instructions}\n"
-        "Input: {text}"
-    ),
-    input_variables=["text"],
-    partial_variables={"format_instructions": parser.get_format_instructions()},
-)
-
-# ---------------------------------------------------------------------------
-# 3. Create the LLM instance.
+# 1. Configure LLM – use BroJS endpoint via environment variable
 # ---------------------------------------------------------------------------
 llm = ChatOpenAI(
     model="openai/gpt-oss-20b:free",
@@ -73,39 +39,44 @@ llm = ChatOpenAI(
 )
 
 # ---------------------------------------------------------------------------
-# 4. Helper that runs the chain and returns a TaskCard.
+# 2. Prepare parser and prompt template
 # ---------------------------------------------------------------------------
-async def parse_task(text: str) -> TaskCard:
-    """Parse *text* into a :class:`TaskCard` using LangChain.
+parser = PydanticOutputParser(pydantic_object=TaskCard)
 
-    The function is asynchronous because ``ChatOpenAI`` uses an async API.  It can be called from
-    synchronous code via ``asyncio.run``.
-    """
+prompt_template = PromptTemplate(
+    template="""
+You are an assistant that extracts structured information from a short informal task description.
+Return the data in the format specified by the following instructions.
 
-    chain = prompt_template | llm | parser
-    result = await chain.ainvoke({"text": text})
-    return result
+Input: {input_text}
+{format_instructions}
+""",
+    input_variables=["input_text"],
+    partial_variables={"format_instructions": parser.get_format_instructions()},
+)
+
+chain = prompt_template | llm | parser
 
 # ---------------------------------------------------------------------------
-# 5. Demo – three example texts.
+# 3. Example usage – three different informal descriptions
+# ---------------------------------------------------------------------------
+examples: List[str] = [
+    "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода.",
+    "На следующей неделе подготовьте презентацию о применении RAG в чат‑ботах. Требуется 10 слайдов, оценка – содержание и дизайн.",
+    "Разработайте скрипт на Python, который парсит CSV и выводит статистику. Срок: до конца месяца. Оценка: корректность кода и комментарии.",
+]
+
+for idx, text in enumerate(examples, 1):
+    print(f"\nExample {idx}:\n{text}\n")
+    result = chain.invoke({"input_text": text})
+    # ``result`` is already a TaskCard instance because of the parser.
+    print("Parsed object (model_dump):", result.model_dump())
+    print("Human‑readable summary:\n", str(result))
+
+# ---------------------------------------------------------------------------
+# 4. If run as script, execute examples
 # ---------------------------------------------------------------------------
 if __name__ == "__main__":
-    import asyncio
-
-    examples = [
-        "Сдайте к пятнице мини‑отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода.",
-        "На следующей неделе подготовьте презентацию о Qdrant. Должно быть 10 слайдов, включать примеры кода. Оценка по содержанию и дизайну.",
-        "Разработайте скрипт на Python, который генерирует случайный пароль длиной 12 символов. Сдача – код в репозитории. Оценка: корректность и безопасность.",
-    ]
-
-    async def demo():
-        for i, txt in enumerate(examples, start=1):
-            card = await parse_task(txt)
-            print(f"\nExample {i}:")
-            print("Raw text:")
-            print(txt)
-            print("\nParsed card: ")
-            # Pretty‑print the model using ``model_dump`` – it returns a dict.
-            print(card.model_dump(indent=2, sort_keys=False))
-
-    asyncio.run(demo())
+    # The loop above already demonstrates the functionality.
+    pass
+"
\ No newline at end of file