diff --git a/main.py b/main.py
new file mode 100644
index 0000000..5a82b59
--- /dev/null
+++ b/main.py
@@ -0,0 +1,51 @@
+"""
+Task: Convert raw task text to flat card using LangChain and Pydantic.
+"""
+
+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):
+    title: str = Field(..., description="Task title")
+    subject: str = Field(..., description="Subject or topic of the task")
+    deadline_hint: str = Field(..., description="Free‑form deadline hint")
+    deliverable_type: str = Field(..., description="What to submit: report, code, presentation, etc.")
+    grading_hints: list[str] = Field(..., description="List of grading hints mentioned in the text")
+
+# Prompt template
+prompt_template = """
+You are an assistant that extracts structured information from a natural language task description.
+Return the data in the following JSON format:
+{format_instructions}
+
+Task description: {task_text}
+"""
+
+parser = PydanticOutputParser(pydantic_object=TaskCard)
+prompt = PromptTemplate(
+    template=prompt_template,
+    input_variables=["task_text"],
+    partial_variables={"format_instructions": parser.get_format_instructions()},
+)
+
+llm = ChatOpenAI(temperature=0)
+chain = prompt | llm | parser
+
+if __name__ == "__main__":
+    import sys
+    if len(sys.argv) < 2:
+        print("Usage: python main.py '<task description>'")
+        sys.exit(1)
+    task_text = sys.argv[1]
+    result = chain.invoke({"task_text": task_text})
+    print("Parsed card:\n", result.model_dump(indent=2))
+    # Human readable summary
+    print("\nSummary:\n")
+    print(f"Title: {result.title}")
+    print(f"Subject: {result.subject}")
+    print(f"Deadline hint: {result.deadline_hint}")
+    print(f"Deliverable type: {result.deliverable_type}")
+    print(f"Grading hints: {', '.join(result.grading_hints)}")
+"""