From 6ad99abb13040f807316cbb962a1547dd3cda50d Mon Sep 17 00:00:00 2001
From: balabanovan530 <175+balabanovan530@noreply.localhost>
Date: Thu, 28 May 2026 13:37:48 +0000
Subject: [PATCH] Add README.md

---
 README.md | 60 +------------------------------------------------------
 1 file changed, 1 insertion(+), 59 deletions(-)

diff --git a/README.md b/README.md
index c996149..7284513 100644
--- a/README.md
+++ b/README.md
@@ -1,59 +1 @@
-# Task Card Processor
-
-This project extracts a structured task card from a raw user text using a single LLM call and a Pydantic parser.
-
-## Installation
-
-```bash
-# Create a virtual environment (recommended)
-python -m venv .venv
-source .venv/bin/activate   # On Windows use .venv\Scripts\activate
-
-# Install dependencies
-pip install -r requirements.txt
-```
-
-## Configuration
-
-The script uses OpenAI API.  Set the following environment variables before running:
-
-- `OPENAI_API_KEY` – your OpenAI API key.
-- `OPENAI_API_BASE` (optional) – override the default OpenAI base URL if you use a local LLM.
-
-## Usage
-
-You can pipe raw text into the script or provide it as a command‑line argument.
-
-```bash
-# From stdin
-cat raw_text.txt | python agent.py
-
-# As an argument
-python agent.py "Generate a task card for a math homework assignment."
-```
-
-The script prints a validated JSON object followed by a short summary.
-
-## Example Output
-
-```json
---- Task Card JSON ---
-{
-  "title": "Math Homework",
-  "subject": "Algebra",
-  "deadline_hint": "Next Friday",
-  "deliverable_type": "Problem set",
-  "grading_hints": "Include solutions and explanations"
-}
-
---- Summary ---
-Title: Math Homework
-Subject: Algebra
-Deadline: Next Friday
-Deliverable: Problem set
-Grading hints: Include solutions and explanations
-```
-
-## License
-
-MIT License.
+<content>
\ No newline at end of file