Urban Planning Lecture Notes Pdf Now
def _show_concepts(self): print("\n🔑 KEY CONCEPTS:") for i, concept in enumerate(self.analyzer.key_concepts[:15], 1): print(f"\ni. concept['term'].upper() (appears concept['frequency']x)") if concept['context']: print(f" Context: concept['context'][0][:150]...")
def _take_quiz(self): questions = self.analyzer.generate_study_questions()[:5] score = 0 print("\nđź“ť QUICK QUIZ (5 questions)") print("Answer in your own words, then press Enter for sample answer\n") for i, q in enumerate(questions, 1): print(f"\ni. q['question']") input("Press Enter to see sample answer...") print(f"\n Sample approach: q['hint']") print(" Review the relevant section for complete answer.\n") def main(): # Replace with your PDF path pdf_path = "urban_planning_lecture_notes.pdf" urban planning lecture notes pdf
import PyPDF2 import re from typing import List, Dict, Tuple import json from collections import Counter import nltk from nltk.corpus import stopwords from nltk.tokenize import sent_tokenize, word_tokenize from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity import pandas as pd import spacy Download required NLTK data nltk.download('punkt') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') Load spaCy model (run: python -m spacy download en_core_web_sm) nlp = spacy.load('en_core_web_sm') concept in enumerate(self.analyzer.key_concepts[:15]
class UrbanPlanningNotesAnalyzer: def (self, pdf_path: str): self.pdf_path = pdf_path self.full_text = "" self.pages_text = [] self.sections = {} self.key_concepts = [] self.case_studies = [] q in enumerate(questions
def extract_case_studies(self) -> List[Dict]: """Identify and extract case studies from lecture notes""" case_patterns = [ r'(?i)case study[:]\s*(.+?)(?:\n\n|\n\s*\n|$)', r'(?i)example[:]\s*(.+?)(?:\n\n|\n\s*\n|$)', r'(?i)([A-Z][a-z]+(?:[-\s][A-Z][a-z]+)*)\s+(?:is\s+an\s+example|demonstrates|illustrates)', ] case_studies = [] sentences = sent_tokenize(self.full_text) for i, sentence in enumerate(sentences): for pattern in case_patterns: matches = re.findall(pattern, sentence) for match in matches: # Get surrounding context start_idx = max(0, i - 2) end_idx = min(len(sentences), i + 3) context = ' '.join(sentences[start_idx:end_idx]) case_studies.append( 'title': match if isinstance(match, str) else match[0], 'description': sentence, 'context': context ) self.case_studies = case_studies return case_studies
