question_handler.py

import re
from sklearn.metrics.pairwise import cosine_similarity

def find_top_question(query, metadata, embeddings):
    query_embedding = model.encode(query, convert_to_tensor=True).cpu().numpy().reshape(1, -1)
    similarities = cosine_similarity(query_embedding, embeddings).flatten()
    top_index = similarities.argsort()[-1]
    top_result = metadata.iloc[top_index].copy()
    top_result['similarity_score'] = similarities[top_index]
    return top_result

def generate_detailed_prompt(question_metadata):
    return (
        f"Transform this LeetCode question into a real-world interview scenario.\n\n"
        f"**Company**: {question_metadata['company']}\n"
        f"**Question Name**: {question_metadata['questionName']}\n"
        f"**Difficulty Level**: {question_metadata['difficulty level']}\n"
        f"**Tags**: {question_metadata['Tags']}\n"
        f"**Content**: {question_metadata['Content']}\n"
        f"\nPlease create a real-world interview question based on this information. "
        f"Include sections for problem description, code template, sample input, and expected output."
    )