add main.py

main.py

@@ -1,51 +1,37 @@
-"""
+import os
-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
+from langchain_core.messages import HumanMessage
+from langchain_output_parsers import PydanticOutputParser
+from pydantic import BaseModel, Field
 
 class TaskCard(BaseModel):
-    title: str = Field(..., description="Task title")
+    title: str = Field(..., description="Title of the task")
-    subject: str = Field(..., description="Subject or topic of the task")
+    subject: str = Field(..., description="Subject area")
-    deadline_hint: str = Field(..., description="Free‑form deadline hint")
+    deadline_hint: str | None = Field(None, description="Hint about deadline")
-    deliverable_type: str = Field(..., description="What to submit: report, code, presentation, etc.")
+    deliverable_type: str = Field(..., description="What to submit (report, code, etc.)")
-    grading_hints: list[str] = Field(..., description="List of grading hints mentioned in the text")
+    grading_hints: list[str] = Field(default_factory=list, description="Hints for grading")
 
-# Prompt template
+llm = ChatOpenAI(
-prompt_template = """
+    model="openai/gpt-oss-20b:free",
-You are an assistant that extracts structured information from a natural language task description.
+    base_url="https://openrouter.ai/api/v1",
-Return the data in the following JSON format:
+    api_key=os.getenv("OPENAI_API_KEY"),
-{format_instructions}
+    temperature=0.0,
-
-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()},
 )
+parser = PydanticOutputParser(pydantic_object=TaskCard)
+prompt_template = """
+You are a task summarizer.
+Given the following informal description of an assignment, produce a JSON object matching TaskCard model.
 
-llm = ChatOpenAI(temperature=0)
+Description: {description}
-chain = prompt | llm | parser
+
+{format_instructions}
+"""
+prompt = prompt_template | llm | parser
+async def main():
+    description = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
+    result = await prompt.ainvoke({"description": description})
+    print(result["messages"][-1].content)
 
 if __name__ == "__main__":
-    import sys
+    import asyncio
-    if len(sys.argv) < 2:
+    asyncio.run(main())
-        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)}")
-"""