Update main.py

main.py

@@ -1,37 +1,63 @@
-#!/usr/bin/env python3
 """
-Simple utility to convert a raw text file into a flat JSON card.
+Simple module that converts informal assignment description into a structured data card.
-The input format is assumed to be a plain text where each line contains a key and value separated by a colon.
-Example:
-    title: My Card
-    description: This is a test.
-    tags: python, example
 
-The output will be a JSON object with the extracted fields.
+Usage:
+    from main import parse_assignment
+    card = parse_assignment("Write an essay on climate change by next Friday")
 """
-import json
+from typing import Dict, Any
-import argparse
-from pathlib import Path
 
-def parse_raw_text(text: str) -> dict:
+from langchain.output_parsers import PydanticOutputParser
-    card = {}
+from pydantic import BaseModel, Field
-    for line in text.splitlines():
-        if not line.strip() or ':' not in line:
-            continue
-        key, value = line.split(':', 1)
-        card[key.strip()] = value.strip()
-    return card
 
-def main():
+class AssignmentCard(BaseModel):
-    parser = argparse.ArgumentParser(description="Convert raw text to flat JSON card")
+    title: str = Field(..., description="Short title of the assignment")
-    parser.add_argument("input", type=Path, help="Input raw text file")
+    subject: str | None = Field(None, description="Subject or topic of the assignment")
-    parser.add_argument("output", type=Path, help="Output JSON file")
+    deadline_hint: str | None = Field(
-    args = parser.parse_args()
+        None,
+        description="Human‑readable hint about when the assignment is due",
+    )
+    deliverable_type: str | None = Field(
+        None,
+        description="What kind of work should be submitted (essay, report, code, etc.)",
+    )
+    grading_hints: str | None = Field(
+        None,
+        description="Any hints about how the assignment will be graded",
+    )
 
-    input_text = args.input.read_text(encoding='utf-8')
+parser = PydanticOutputParser(pydantic_object=AssignmentCard)
-    card = parse_raw_text(input_text)
+
-    args.output.write_text(json.dumps(card, ensure_ascii=False, indent=2), encoding='utf-8')
+# Prompt template that asks the model to output JSON matching AssignmentCard
-    print(f"Card written to {args.output}")
+PROMPT_TEMPLATE = (
+    "You are an assistant that converts a short informal description of a study assignment into a structured data card."
+    " Return only valid JSON that matches the following schema:\n{schema}\n"
+    " Description: {description}"
+)
+
+def parse_assignment(description: str) -> AssignmentCard:
+    """Return an AssignmentCard parsed from the given description.
+
+    The function uses LangChain's PydanticOutputParser to enforce type safety.
+    """
+    from langchain import PromptTemplate, LLMChain
+    from langchain.chat_models import ChatOpenAI
+
+    # Use a small model for demonstration; replace with your own key if needed.
+    llm = ChatOpenAI(temperature=0.2)
+    prompt = PromptTemplate(
+        input_variables=["description", "schema"],
+        template=PROMPT_TEMPLATE,
+    )
+    chain = LLMChain(llm=llm, prompt=prompt, output_parser=parser)
+    result = chain.run(description=description, schema=parser.get_format_instructions())
+    return result
 
 if __name__ == "__main__":
-    main()
+    import sys
+    if len(sys.argv) < 2:
+        print("Usage: python main.py '<assignment description>'")
+        sys.exit(1)
+    desc = sys.argv[1]
+    card = parse_assignment(desc)
+    print(card.json(indent=4))