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from aws_llm import llm_response
import json
from .prompts import MCQ_SYSTEM_PROMPT , MCQ_USER_PROMPT , LEARN_SYSTEM_PROMPT,LEARN_USER_PROMPT,PLAYLIST_MCQ_SYSTEM_PROMPT,PLAYLIST_MCQ_USER_PROMPT
def generate_mcqs(material, num_questions=5):
"""Generate MCQs using the LLM."""
try:
user_prompt = MCQ_USER_PROMPT.format(material=material, num_questions=num_questions)
system_prompt = MCQ_SYSTEM_PROMPT.format(num_questions=num_questions)
response, cost = llm_response(system_prompt, user_prompt)
# Parse JSON response
questions = json.loads(response)
return questions, cost
except Exception as e:
print(f"⚠️ Error generating MCQs: {e}")
# Return a fallback question if generation fails
return [
{
"question": "What is the main topic of this content?",
"options": [
"Unable to determine",
"Content unavailable",
"Error in processing",
"Question generation failed"
],
"answer": "Error in processing"
}
], 0
def generate_learning_outcomes(material):
"""Generate 'What You'll Learn' points using the LLM."""
try:
user_prompt = LEARN_USER_PROMPT.format(material=material)
response, cost = llm_response(LEARN_SYSTEM_PROMPT, user_prompt)
# Parse JSON response
learning_outcomes = json.loads(response)
return learning_outcomes, cost
except Exception as e:
print(f"⚠️ Error generating learning outcomes: {e}")
return ["Understand key concepts covered in this content"], 0
def generate_playlist_mcqs(playlist_title, video_summaries, num_questions=10):
"""Generate overall playlist MCQs using the LLM."""
try:
# Create a condensed summary of all videos for context
summaries_text = "\n\n".join([f"Video: {summary['title']}\nSummary: {summary['description']}"
for summary in video_summaries[:20]]) # Limit to avoid token limits
user_prompt = PLAYLIST_MCQ_USER_PROMPT.format(
playlist_title=playlist_title,
video_summaries=summaries_text,
num_questions=num_questions
)
system_prompt = PLAYLIST_MCQ_SYSTEM_PROMPT.format(num_questions=num_questions)
response, cost = llm_response(system_prompt, user_prompt)
# Parse JSON response
questions = json.loads(response)
return questions, cost
except Exception as e:
print(f"⚠️ Error generating playlist MCQs: {e}")
# Return a fallback question if generation fails
return [
{
"question": "What is the main theme of this playlist?",
"options": [
"Unable to determine",
"Content unavailable",
"Error in processing",
"Question generation failed"
],
"answer": "Error in processing"
}
], 0
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