From f5a43e87dab20f61f0e5ce5ad53c95fd1350aa44 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9A=D0=B8=D1=80=D0=B8=D0=BB=D0=BB=20=D0=9A=D1=83=D1=82?=
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 <kirillkutlahmetov10@gmail.com>
Date: Wed, 27 May 2026 13:32:34 +0000
Subject: [PATCH] add main.py

---
 main.py | 111 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 111 insertions(+)
 create mode 100644 main.py

diff --git a/main.py b/main.py
new file mode 100644
index 0000000..ec2f237
--- /dev/null
+++ b/main.py
@@ -0,0 +1,111 @@
+"""
+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.
+
+Usage examples are provided in the ``__main__`` section – three different raw texts are parsed and printed.
+"""
+
+from __future__ import annotations
+
+import os
+from typing import List
+
+from langchain_openai import ChatOpenAI
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+from pydantic import BaseModel, Field
+
+# ---------------------------------------------------------------------------
+# 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.
+# ---------------------------------------------------------------------------
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
+    api_key=os.getenv("JOURNAL_MCP_PAT"),
+    temperature=0.0,
+)
+
+# ---------------------------------------------------------------------------
+# 4. Helper that runs the chain and returns a TaskCard.
+# ---------------------------------------------------------------------------
+async def parse_task(text: str) -> TaskCard:
+    """Parse *text* into a :class:`TaskCard` using LangChain.
+
+    The function is asynchronous because ``ChatOpenAI`` uses an async API.  It can be called from
+    synchronous code via ``asyncio.run``.
+    """
+
+    chain = prompt_template | llm | parser
+    result = await chain.ainvoke({"text": text})
+    return result
+
+# ---------------------------------------------------------------------------
+# 5. Demo – three example texts.
+# ---------------------------------------------------------------------------
+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())