diff --git a/main.py b/main.py
index 5c9415f..d350b9a 100644
--- a/main.py
+++ b/main.py
@@ -1,104 +1,25 @@
-"""Task 69dd4221f309a98be0006b2e – Structured task card parser.
+import os
+import json
+from parser import TaskParser
 
-The script accepts a single natural‑language description of a course task and
-returns a validated Pydantic model instance.  It demonstrates a one‑shot
-prompting pattern with a structured output parser.
-
-Usage:
-    python main.py "<task description>"
-
-The script prints the raw model dump and a short human‑readable summary.
-"""
-
-import sys
-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
-
-# ---------------------------------------------------------------------------
-# 1. Pydantic model
-# ---------------------------------------------------------------------------
-class TaskCard(BaseModel):
-    """Compact representation of a course task.
-
-    All fields are optional because the model may not be able to infer every
-    piece of information from a very short description.  The parser will
-    still return a valid instance – missing values will be ``None``.
-    """
-
-    title: str | None = Field(None, description="Short title of the task")
-    subject: str | None = Field(None, description="Subject or topic of the task")
-    deadline_hint: str | None = Field(
-        None, description="Free‑form hint about the deadline (e.g. 'к пятнице')"
-    )
-    deliverable_type: str | None = Field(
-        None, description="What is expected to be submitted (report, code, etc.)"
-    )
-    grading_hints: List[str] | None = Field(
-        None, description="List of hints mentioned about grading"
-    )
-
-# ---------------------------------------------------------------------------
-# 2. Prompt + chain
-# ---------------------------------------------------------------------------
-parser = PydanticOutputParser(pydantic_object=TaskCard)
-
-prompt_template = """You are a helper that extracts structured information from a
-course task description.  Return a JSON object that matches the following
-schema:
-
-{schema}
-
-The input description is:
-
-{description}
-
-Respond ONLY with the JSON object.  Do not add any extra text.
-"""
-
-prompt = PromptTemplate(
-    template=prompt_template,
-    input_variables=["description"],
-    partial_variables={"schema": parser.get_format_instructions()},
-)
-
-# LLM – use the default OpenAI OSS endpoint via environment variables
-llm = ChatOpenAI(model="openai/gpt-oss-20b:free", temperature=0.2)
-
-chain = prompt | llm | parser
-
-# ---------------------------------------------------------------------------
-# 3. Main entry point
-# ---------------------------------------------------------------------------
+# Example raw texts
+RAW_TEXTS = [
+    "Напишите мини-отчёт по LangChain до пятницы, 3-5 страниц, критерии: полнота и примеры кода",
+    "Сделать агента на LangGraph который умеет искать в интернете, сдать ссылку на github",
+    "Реализовать REST API на FastAPI с авторизацией JWT, покрыть тестами, дедлайн 1 июня",
+]
 
 def main() -> None:
-    if len(sys.argv) < 2:
-        print("Usage: python main.py '<task description>'")
-        sys.exit(1)
-
-    description = sys.argv[1]
-    result: TaskCard = chain.invoke({"description": description})
-
-    # Print raw model dump
-    print("\n--- Parsed model ---")
-    print(result.model_dump(indent=2))
-
-    # Human‑readable summary
-    print("\n--- Summary ---")
-    print(f"Title: {result.title or 'N/A'}")
-    print(f"Subject: {result.subject or 'N/A'}")
-    print(f"Deadline hint: {result.deadline_hint or 'N/A'}")
-    print(f"Deliverable: {result.deliverable_type or 'N/A'}")
-    if result.grading_hints:
-        print("Grading hints:")
-        for hint in result.grading_hints:
-            print(f"- {hint}")
-    else:
-        print("Grading hints: N/A")
-
+    parser = TaskParser()
+    cards = parser.batch_parse(RAW_TEXTS)
+    for idx, card in enumerate(cards, start=1):
+        print(f"\n=== Task {idx} ===")
+        print(card.to_markdown())
+        filename = f"task_{idx}.json"
+        parser.save_to_file(card, filename)
+        print(f"Saved to {filename}")
 
 if __name__ == "__main__":
     main()
+
+# End of main.py