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Refactor response handling in app.py and various tool modules to improve JSON parsing and error management. Introduce utility functions for cleaning and extracting JSON from text, ensuring consistent data handling across quiz, lesson, learning path, and interaction tools. Enhance robustness by accommodating multiple response formats, including strings and dictionaries, to streamline educational interactions.
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| """ | |
| Lesson generation tools for TutorX MCP. | |
| """ | |
| from typing import Dict, Any, List | |
| from mcp_server.mcp_instance import mcp | |
| from mcp_server.model.gemini_flash import GeminiFlash | |
| import json | |
| MODEL = GeminiFlash() | |
| def clean_json_trailing_commas(json_text: str) -> str: | |
| import re | |
| return re.sub(r',([ \t\r\n]*[}}\]])', r'\1', json_text) | |
| def extract_json_from_text(text: str): | |
| import re, json | |
| if not text or not isinstance(text, str): | |
| return None | |
| # Remove code fences | |
| text = re.sub(r'^\s*```(?:json)?\s*', '', text, flags=re.IGNORECASE) | |
| text = re.sub(r'\s*```\s*$', '', text, flags=re.IGNORECASE) | |
| text = text.strip() | |
| # Remove trailing commas | |
| cleaned = clean_json_trailing_commas(text) | |
| return json.loads(cleaned) | |
| async def generate_lesson_tool(topic: str, grade_level: int, duration_minutes: int) -> dict: | |
| """ | |
| Generate a lesson plan for the given topic, grade level, and duration, fully LLM-driven. | |
| Use Gemini to generate a JSON object with objectives, activities, materials, assessment, differentiation, and homework. | |
| """ | |
| prompt = ( | |
| f"Generate a detailed lesson plan as a JSON object for the topic '{topic}', grade {grade_level}, duration {duration_minutes} minutes. " | |
| f"Include fields: objectives (list), activities (list), materials (list), assessment (dict), differentiation (dict), and homework (dict)." | |
| ) | |
| llm_response = await MODEL.generate_text(prompt) | |
| try: | |
| data = extract_json_from_text(llm_response) | |
| except Exception: | |
| data = {"llm_raw": llm_response, "error": "Failed to parse LLM output as JSON"} | |
| return data | |