From 1fa41301c0f1da2ace42874fe4732b8e1a6fe24e 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?=
 =?UTF-8?q?=D0=BB=D0=B0=D1=85=D0=BC=D0=B5=D1=82=D0=BE=D0=B2?=
 <kirillkutlahmetov10@gmail.com>
Date: Tue, 26 May 2026 08:59:23 +0000
Subject: [PATCH] clear main.py

---
 main.py | 93 ---------------------------------------------------------
 1 file changed, 93 deletions(-)
 delete mode 100644 main.py

diff --git a/main.py b/main.py
deleted file mode 100644
index 0e58933..0000000
--- a/main.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""Task parser for raw assignment text.
-
-This module demonstrates how to convert a free‑form assignment description into a
-structured data object using LangChain and Pydantic.
-
-The main entry point is :func:`parse_task` which accepts a string and returns a
-validated :class:`TaskCard` instance.
-
-Example usage:
-
->>> from main import parse_task
->>> text = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
->>> card = parse_task(text)
->>> print(card.model_dump())
-{"title": "мини-отчёт по LangChain", "subject": "LangChain", "deadline_hint": "к пятнице", "deliverable_type": "отчёт", "grading_hints": ["полнота", "пример кода"]}
-"""
-
-from __future__ import annotations
-
-from typing import List
-
-from pydantic import BaseModel, Field
-from langchain_core.prompts import PromptTemplate
-from langchain_openai import ChatOpenAI
-from langchain_core.output_parsers import PydanticOutputParser
-
-
-class TaskCard(BaseModel):
-    """Structured representation of an assignment description."""
-
-    title: str = Field(..., description="Short title of the assignment")
-    subject: str = Field(..., description="Subject or topic of the assignment")
-    deadline_hint: str = Field(..., description="Free‑form deadline hint")
-    deliverable_type: str = Field(..., description="What is expected to be submitted")
-    grading_hints: List[str] = Field(..., description="List of grading criteria mentioned in the text")
-
-
-# Prompt template – we ask the model to output JSON that matches TaskCard.
-prompt_template = (
-    "You are an assistant that extracts structured information from a short assignment description."
-    " Return a JSON object with the following fields: title, subject, deadline_hint, deliverable_type, grading_hints."
-    " Do not add any extra keys or text."
-    " Example input: {input_text}\n"
-    " {format_instructions}"
-)
-
-parser = PydanticOutputParser(pydantic_object=TaskCard)
-prompt = PromptTemplate(
-    template=prompt_template,
-    input_variables=["input_text"],
-    partial_variables={"format_instructions": parser.get_format_instructions()},
-)
-
-# Use the default OpenAI model; the user can set OPENAI_API_KEY.
-llm = ChatOpenAI(temperature=0)
-
-# Chain: prompt -> LLM -> parser
-chain = prompt | llm | parser
-
-
-def parse_task(text: str) -> TaskCard:
-    """Parse raw assignment text into a :class:`TaskCard`.
-
-    Parameters
-    ----------
-    text:
-        Raw assignment description.
-
-    Returns
-    -------
-    TaskCard
-        Validated structured data.
-    """
-    return chain.invoke({"input_text": text})
-
-
-if __name__ == "__main__":
-    import sys
-
-    if len(sys.argv) < 2:
-        print("Usage: python main.py '<assignment text>'")
-        sys.exit(1)
-    raw = sys.argv[1]
-    card = parse_task(raw)
-    print("Parsed card:\n", card.model_dump(indent=2))
-    print("\nHuman‑readable summary:\n")
-    print(f"Title: {card.title}")
-    print(f"Subject: {card.subject}")
-    print(f"Deadline hint: {card.deadline_hint}")
-    print(f"Deliverable type: {card.deliverable_type}")
-    print("Grading hints:")
-    for hint in card.grading_hints:
-        print(f"- {hint}")