add main.py

main.py

@@ -1,5 +1,4 @@
 from pydantic import BaseModel, Field
-from typing import List
 from langchain_core.prompts import PromptTemplate
 from langchain_openai import ChatOpenAI
 from langchain_core.output_parsers import PydanticOutputParser
@@ -7,40 +6,31 @@ from langchain_core.output_parsers import PydanticOutputParser
 class TaskCard(BaseModel):
     title: str = Field(..., description="Title of the assignment")
     subject: str = Field(..., description="Subject or topic of the assignment")
-    deadline_hint: str = Field(..., description="Free‑form deadline hint")
+    deadline_hint: str = Field(..., description="Free‑form hint about deadline")
-    deliverable_type: str = Field(..., description="What to submit: report, code, presentation, etc.")
+    deliverable_type: str = Field(..., description="What is to be submitted (report, code, presentation, etc.)")
-    grading_hints: List[str] = Field(..., description="List of grading hints mentioned in the text")
+    grading_hints: list[str] = Field(..., description="List of hints mentioned about grading")
 
 parser = PydanticOutputParser(pydantic_object=TaskCard)
 
-prompt_template = (
-    "You are an assistant that extracts structured information from a short assignment description. "
-    "Return the data in the following JSON format exactly as required by the parser. "
-    "Do not add any extra keys or text.\n"
-    "{format_instructions}\n"
-    "Input: {input_text}\n"
-    "Output:"
-)
-
 prompt = PromptTemplate(
-    template=prompt_template,
+    template="""
+You are an assistant that extracts structured information from a short assignment description.
+Return the data in the following JSON format exactly:
+{format_instructions}
+
+Input: {input_text}
+""",
     input_variables=["input_text"],
     partial_variables={"format_instructions": parser.get_format_instructions()},
 )
 
 llm = ChatOpenAI(temperature=0)
-
 chain = prompt | llm | parser
 
 if __name__ == "__main__":
-    # Example input
+    sample = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
-    input_text = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
+    result = chain.invoke({"input_text": sample})
-    result = chain.invoke({"input_text": input_text})
     print("Parsed object:")
     print(result.model_dump())
-    print("\nHuman readable summary:\n")
+    print("\nHuman readable summary:")
-    print(f"Title: {result.title}")
+    print(f"Title: {result.title}\nSubject: {result.subject}\nDeadline: {result.deadline_hint}\nDeliverable: {result.deliverable_type}\nGrading hints: {', '.join(result.grading_hints)}")
-    print(f"Subject: {result.subject}")
-    print(f"Deadline: {result.deadline_hint}")
-    print(f"Deliverable: {result.deliverable_type}")
-    print(f"Grading hints: {', '.join(result.grading_hints)}")