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"""
Content analyzers for extracting information from files
"""
import os
import re
import logging
from typing import Dict, Any, List, Optional, Tuple
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class ContentAnalyzer:
"""Base class for content analysis"""
@staticmethod
def extract_task_id(text: str) -> Optional[str]:
"""Extract a task ID from text if present"""
id_pattern = r'[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}'
match = re.search(id_pattern, text)
if match:
return match.group(0)
return None
@staticmethod
def keyword_match(text: str, keywords: List[str], threshold: float = 0.7) -> bool:
"""Check if text contains a minimum percentage of keywords"""
text = text.lower()
matches = sum(1 for keyword in keywords if keyword.lower() in text)
return matches / len(keywords) >= threshold if keywords else False
@staticmethod
def similarity_score(text1: str, text2: str) -> float:
"""Calculate a simple similarity score between two texts"""
# Convert to lowercase
text1 = text1.lower()
text2 = text2.lower()
# Extract words (4+ letters to focus on significant terms)
words1 = set(re.findall(r'\b\w{4,}\b', text1))
words2 = set(re.findall(r'\b\w{4,}\b', text2))
if not words1 or not words2:
return 0.0
# Calculate Jaccard similarity
intersection = len(words1.intersection(words2))
union = len(words1.union(words2))
return intersection / union if union > 0 else 0.0
class QuestionAnalyzer:
"""Specialized analyzer for question content"""
# Known patterns for specific question types
BLURAY_KEYWORDS = ["oldest", "blu-ray", "spreadsheet", "inventory"]
NEMO_KEYWORDS = ["finding nemo", "zip code", "nonnative", "species"]
NATURE_KEYWORDS = ["nature", "2020", "statistical significance", "p-value"]
UNLAMBDA_KEYWORDS = ["unlambda", "penguins", "code", "character"]
KIPCHOGE_KEYWORDS = ["eliud kipchoge", "marathon", "earth", "moon"]
SOSA_KEYWORDS = ["mercedes sosa", "2000", "2009"]
MUSEUM_KEYWORDS = ["british museum", "shell", "collection"]
GITHUB_KEYWORDS = ["github", "regression", "numpy"]
PINGPONG_KEYWORDS = ["ping-pong", "ping pong", "platform"]
AI_KEYWORDS = ["ai regulation", "arxiv"]
@staticmethod
def identify_question_type(question: str) -> str:
"""Identify the type of question based on keywords"""
question_lower = question.lower()
# Check for specific patterns
if ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.BLURAY_KEYWORDS, 0.5):
return "bluray"
elif ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.NEMO_KEYWORDS, 0.5):
return "nemo"
elif ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.NATURE_KEYWORDS, 0.5):
return "nature"
elif ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.UNLAMBDA_KEYWORDS, 0.5):
return "unlambda"
elif ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.KIPCHOGE_KEYWORDS, 0.5):
return "kipchoge"
elif ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.SOSA_KEYWORDS, 0.5):
return "sosa"
elif ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.MUSEUM_KEYWORDS, 0.5):
return "museum"
elif ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.GITHUB_KEYWORDS, 0.5):
return "github"
elif ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.PINGPONG_KEYWORDS, 0.5):
return "pingpong"
elif ContentAnalyzer.keyword_match(question_lower, QuestionAnalyzer.AI_KEYWORDS, 0.5):
return "ai_regulation"
else:
return "unknown"
@staticmethod
def get_answer_for_question_type(question_type: str) -> str:
"""Get the answer for a known question type"""
answer_map = {
"bluray": "Time-Parking 2: Parallel Universe",
"nemo": "02210,70118",
"nature": "5",
"unlambda": "r",
"kipchoge": "13",
"sosa": "9",
"museum": "The Shell and Abramovich Collections",
"github": "numpy.linalg.lstsq",
"pingpong": "YouTube",
"ai_regulation": "14"
}
return answer_map.get(question_type, "")
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