Update topic_extraction.py
Browse files- topic_extraction.py +89 -31
topic_extraction.py
CHANGED
@@ -4,28 +4,21 @@ import re
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import gc
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import json
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import logging
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import fitz
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import base64
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import concurrent.futures
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from io import BytesIO
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from typing import List, Dict, Any
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from google import genai
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from google.genai import types
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except ImportError:
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genai = None
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types = None
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import torch
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import cv2
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# Magic PDF pipeline
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from magic_pdf.data.dataset import PymuDocDataset
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from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
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# Your TableExtractor from topic_extraction_upgrade (or similar)
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from table_row_extraction import TableExtractor
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logging.basicConfig(level=logging.INFO)
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@@ -87,19 +80,92 @@ class GeminiTopicExtractor:
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prompt = f"""
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You are given the text of a specification PDF.
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"""
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try:
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@@ -133,10 +199,6 @@ TEXT:
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logger.error(f"Could not open/read PDF: {e}")
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return "\n".join(text_parts)
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# -------------------------------------------------------------------
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# Gemini-based table classification (Mineru style)
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# -------------------------------------------------------------------
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def call_gemini_for_table_classification(image_data: bytes) -> str:
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if genai is None or types is None:
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logger.warning("Gemini not available. Returning NO_TABLE.")
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@@ -482,10 +544,6 @@ class MineruNoTextProcessor:
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pages.append(p)
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return pages
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# -------------------------------------------------------------------
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# Example usage
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# -------------------------------------------------------------------
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if __name__ == "__main__":
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input_pdf = "/home/user/app/input_output/a-level-pearson-mathematics-specification.pdf"
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output_dir = "/home/user/app/input_output/output"
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import gc
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import json
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import logging
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import fitz
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import base64
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import concurrent.futures
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from io import BytesIO
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from typing import List, Dict, Any
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from google import genai
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from google.genai import types
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import torch
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import cv2
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from magic_pdf.data.dataset import PymuDocDataset
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from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
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from table_row_extraction import TableExtractor
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logging.basicConfig(level=logging.INFO)
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prompt = f"""
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You are given the text of a specification PDF.
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Instructions:
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1. Identify the "Contents" section, which lists all topics, subtopics, and their corresponding pages.
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2. Extract only the **highest-level, subject-related subtopics** (ignore organizational or administrative sections).
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3. For subtopics, include the full range of pages from the first to the last subtopic.
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4. Return the output in the following JSON format:
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{{
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"topic_name": [start_page, end_page]
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}}
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Important Notes:
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- Ignore non-subject-related sections (e.g., "Introduction", "Exam Guidelines", "Appendices", "Assessment, Qualification at a glance").
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- The extracted subtopics should represent major academic areas, not organizational or structural elements.
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- Make sure that all of the pages for a subtopic are included, end page should be the start page of the topic
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that comes next after the extracted one in contents section.
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Examples:
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1. Given this table of contents:
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1 Introduction β 2
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Why choose Edexcel A Level Mathematics? - 2
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Supporting you in planning and implementing this qualification - 3
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Qualification at a glance - 5
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2 Subject content and assessment information β 7
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Paper 1 and Paper 2: Pure Mathematics - 11
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Paper 3: Statistics and Mechanics - 30
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Assessment Objectives - 40
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3 Administration and general information β 42
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Entries - 42
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Access arrangements, reasonable adjustments, special consideration and malpractice - 42
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Student recruitment and progression - 45
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Appendix 1: Formulae β 49
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Appendix 2: Notation β 53
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Appendix 3: Use of calculators β 59
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Appendix 4: Assessment Objectives β 60
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Appendix 5: The context for the development of this qualification β 62
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Appendix 6: Transferable skills β 64
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Appendix 7: Level 3 Extended Project qualification β 65
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Appendix 8: Codes β 67
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The correct output should be:
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{{
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"Paper 1 and Paper 2: Pure Mathematics": [11, 29],
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"Paper 3: Statistics and Mechanics": [30, 42]
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}}
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2. Given this table of contents:
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Qualification at a glance β 1
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Assessment Objectives and weightings - 4
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Knowledge, skills and understanding β 5
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Theme 1: Introduction to markets and market failure - 5
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Theme 2: The UK economy β performance and policies - 11
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Theme 3: Business behaviour and the labour market - 21
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Theme 4: A global perspective - 29
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Assessment β 39
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Assessment summary - 39
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Assessment objectives - 41
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Assessment overview - 42
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Breakdown of assessment objectives - 42
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Synoptic assessment - 43
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Discount code and performance tables - 43
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Access arrangements, reasonable adjustments and special consideration - 44
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Malpractice - 45
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Equality Act 2010 and Pearson equality policy - 45
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Synoptic assessment - 46
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Awarding and reporting - 47
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Other information β 49
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Student recruitment -49
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Prior learning and other requirements -49
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Progression - 49
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Appendix 1: Transferable skills β 53
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Appendix 2: Level 3 Extended Project qualification β 55
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Appendix 3: Quantitative skills β 59
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Appendix 4: Codes β 61
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Appendix 5: Index β 63
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The correct output should be:
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{{
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"Theme 1: Introduction to markets and market failure": [5, 10]
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"Theme 2: The UK economy β performance and policies": - [11, 20]
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"Theme 3: Business behaviour and the labour market": [21, 28]
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"Theme 4: A global perspective": [29, 38]
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}}
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Now, extract topics from this text:{text_content}
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"""
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try:
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logger.error(f"Could not open/read PDF: {e}")
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return "\n".join(text_parts)
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def call_gemini_for_table_classification(image_data: bytes) -> str:
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if genai is None or types is None:
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logger.warning("Gemini not available. Returning NO_TABLE.")
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pages.append(p)
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return pages
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if __name__ == "__main__":
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input_pdf = "/home/user/app/input_output/a-level-pearson-mathematics-specification.pdf"
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output_dir = "/home/user/app/input_output/output"
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