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diff --git a/contents_extractor_v2.py b/contents_extractor_v2.py
deleted file mode 100644
index 5ee0f49171c34f5d2913b2242940103d0704e2eb..0000000000000000000000000000000000000000
--- a/contents_extractor_v2.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from google import genai
-from google.genai import types
-import fitz
-import requests
-
-MODEL = "gemini-2.0-flash"
-
-# TODO: Make sure the last page must be included
-
-
-class ContentsExtractor:
-    def __init__(self, api_key: str):
-        self.client = genai.Client(api_key=api_key)
-
-    @staticmethod
-    def extract_first_pages(pdf_path, num_pages=4, is_path_url=False):
-        try:
-            if is_path_url:
-                r = requests.get(pdf_path)
-                data = r.content
-                doc = fitz.open(stream=data, filetype="pdf")
-            else:
-                doc = fitz.open(pdf_path)
-            total_pages = doc.page_count
-            pages_to_read = min(total_pages, num_pages)
-            all_text = []
-            for page_num in range(pages_to_read):
-                page = doc[page_num]
-                page_text = page.get_text()
-                all_text.append(page_text)
-            doc.close()
-            return "\n".join(all_text)
-        except Exception as e:
-            print(f"Something went wrong: {e}")
-            return None
-
-    def extract_contents(self, content):
-        response = self.client.models.generate_content(
-            model=MODEL,
-            contents=[f"""
-                Task:
-                You will be provided with the first pages of an exam board document. Your goal is to extract 
-                the main subject-related topics from the "Contents" section and structure them in a valid JSON format.
-                
-                Instructions:
-                1. Identify the "Contents" section, which lists all topics, subtopics, and their corresponding pages.
-                2. Extract only the **highest-level, subject-related subtopics** (ignore organizational or administrative sections).
-                3. For subtopics, include the full range of pages from the first to the last subtopic.
-                4. Return the output in the following JSON format:
-                
-                    {{
-                        "topic_name": [start_page, end_page]
-                    }}
-                
-                Important Notes:
-                - Ignore non-subject-related sections (e.g., "Introduction", "Exam Guidelines", "Appendices", "Assessment, Qualification at a glance").
-                - The extracted subtopics should represent major academic areas, not organizational or structural elements.
-                - Make sure that all of the pages for a subtopic are included, end page should be the start page of the topic
-                  that comes next after the extracted one in contents section.
-                
-                Examples:
-                1. Given this table of contents:
-
-                    1 Introduction – 2
-                        Why choose Edexcel A Level Mathematics? - 2
-                        Supporting you in planning and implementing this qualification - 3
-                        Qualification at a glance - 5
-                    2 Subject content and assessment information – 7
-                        Paper 1 and Paper 2: Pure Mathematics - 11
-                        Paper 3: Statistics and Mechanics - 30
-                        Assessment Objectives - 40
-                    3 Administration and general information – 42
-                        Entries - 42
-                        Access arrangements, reasonable adjustments, special consideration and malpractice - 42
-                        Student recruitment and progression - 45
-                    Appendix 1: Formulae – 49
-                    Appendix 2: Notation – 53
-                    Appendix 3: Use of calculators – 59
-                    Appendix 4: Assessment Objectives – 60
-                    Appendix 5: The context for the development of this qualification – 62
-                    Appendix 6: Transferable skills – 64
-                    Appendix 7: Level 3 Extended Project qualification – 65
-                    Appendix 8: Codes – 67
-                
-                   The correct output should be:
-                
-                    {{
-                        "Paper 1 and Paper 2: Pure Mathematics": [11, 29],
-                        "Paper 3: Statistics and Mechanics": [30, 42]
-                    }}
-                
-                2. Given this table of contents:
-
-                    Qualification at a glance – 1
-                        Assessment Objectives and weightings - 4
-                    Knowledge, skills and understanding – 5
-                        Theme 1: Introduction to markets and market failure - 5
-                        Theme 2: The UK economy – performance and policies - 11
-                        Theme 3: Business behaviour and the labour market - 21
-                        Theme 4: A global perspective - 29
-                    Assessment – 39
-                        Assessment summary - 39
-                        Assessment objectives - 41
-                        Assessment overview - 42
-                        Breakdown of assessment objectives - 42
-                            Synoptic assessment - 43
-                            Discount code and performance tables - 43
-                            Access arrangements, reasonable adjustments and special consideration - 44
-                            Malpractice - 45
-                            Equality Act 2010 and Pearson equality policy - 45
-                            Synoptic assessment - 46
-                            Awarding and reporting - 47
-                    Other information – 49
-                        Student recruitment -49
-                        Prior learning and other requirements -49
-                        Progression - 49
-                    Appendix 1: Transferable skills – 53
-                    Appendix 2: Level 3 Extended Project qualification – 55
-                    Appendix 3: Quantitative skills – 59
-                    Appendix 4: Codes – 61
-                    Appendix 5: Index – 63
-                
-                   The correct output should be:
-                
-                    {{
-                        "Theme 1: Introduction to markets and market failure": [5, 10]
-                        "Theme 2: The UK economy – performance and policies": - [11, 20]
-                        "Theme 3: Business behaviour and the labour market": [21, 28]
-                        "Theme 4: A global perspective": [29, 38]
-                    }}
-                    
-                    Now, extract topics from this text: {content}
-                """],
-            config=types.GenerateContentConfig(temperature=0.)
-        )
-        return response.text.strip().replace("```json", "").replace("```", "")
\ No newline at end of file
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diff --git a/input_output/pearson-A_Level_Economics.pdf b/input_output/pearson-A_Level_Economics.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..beb8df3e62349efaad6d3c3d01abe0f2ffeb653a
--- /dev/null
+++ b/input_output/pearson-A_Level_Economics.pdf
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37447a1580aa0006717ef4afcb418998f366e2a3120780a58e2180f5242b0f80
+size 1433534
diff --git a/mineru_test_local.py b/mineru_test_local.py
deleted file mode 100644
index ef7f38cdca97aeb0f39012c6b62ac937e2af3457..0000000000000000000000000000000000000000
--- a/mineru_test_local.py
+++ /dev/null
@@ -1,376 +0,0 @@
-#!/usr/bin/env python3
-import os
-import re
-import gc
-import json
-import base64
-import logging
-import concurrent.futures
-from io import BytesIO
-
-from google import genai
-from google.genai import types
-
-import torch
-import cv2
-
-# from inference_svm_model import SVMModel
-
-from table_row_extraction import TableExtractor
-
-from magic_pdf.data.dataset import PymuDocDataset
-from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
-    handlers=[
-        logging.StreamHandler(),
-        logging.FileHandler('mineru.log')
-    ]
-)
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-
-def call_gemini_for_table_classification(image_data: bytes) -> str:
-    """
-    Returns "TWO_COLUMN", "THREE_COLUMN", or "NO_TABLE".
-    """
-    prompt = """You are given an image. Determine if it shows a table that has exactly 2 or 3 columns. 
-The three-column 'table' image include such key features:
-    - Three columns header columns
-    - Headers like 'Topics', 'Content', 'Guidelines'
-    - Numbered sections (e.g., 8.4, 9.1)
-    - Educational curriculum-style structure
-The two-column 'table' image include such key features:
-    - Two columns header columns
-    - Headers like 'Subject content' and 'Additional information'
-    - Numbered sections (e.g., 2.1, 3.4)
-    - Educational curriculum-style structure
-    - Bullet description in 'Additional information'
-If the image is a relevant table with 2 columns, respond with 'TWO_COLUMN'.
-If the image is a relevant table with 3 columns, respond with 'THREE_COLUMN'.
-If the image does not show a table at all, respond with 'NO_TABLE'.
-Return only one of these exact labels as your entire response:
-TWO_COLUMN
-THREE_COLUMN
-NO_TABLE
-"""
-    client = genai.Client(api_key="AIzaSyDtoakpXa2pjJwcQB6TJ5QaXHNSA5JxcrU")
-    response = client.models.generate_content(
-        model="gemini-2.0-flash",
-        config=types.GenerateContentConfig(temperature=0.),
-        contents=[
-            {
-                "parts": [
-                    {"text": prompt},
-                    {
-                        "inline_data": {
-                            "mime_type": "image/jpeg",
-                            "data": base64.b64encode(image_data).decode('utf-8')
-                        }
-                    }
-                ]
-            }
-        ]
-    )
-    classification = response.text.strip() if (response and response.text) else "NO_TABLE"
-    classification = classification.upper()
-    if "THREE" in classification:
-        return "THREE_COLUMN"
-    elif "TWO" in classification:
-        return "TWO_COLUMN"
-    else:
-        return "NO_TABLE"
-
-def call_gemini_for_image_description(image_data: bytes) -> str:
-    prompt_text = """The provided image is a part of a question paper or markscheme.
-Extract all the necessary information from the image to be able to identify the question.
-To identify the question, we only need the following: question number and question part.
-Don't include redundant information.
-For example, if image contains text like: "Q1 Part A Answer: Life on earth was created by diety..."
-you should return just "Q1 Part A Mark Scheme"
-If there is no text on this image, return the description of the image. 20 words max.
-If there are not enough data, consider information from the surrounding context.
-Additionally, if the image contains a truncated part, you must describe it and mark as a
-part of some another image that goes before or after current image.
-If the image is of a multiple-choice question’s options, then modify your answer by appending
-'MCQ: A [option] B [option] C [option] D [option]' (replacing [option] with the actual options).
-Otherwise, follow the above instructions strictly.
-"""
-
-    try:
-        client = genai.Client(api_key="AIzaSyDtoakpXa2pjJwcQB6TJ5QaXHNSA5JxcrU")
-        response = client.models.generate_content(
-            model="gemini-2.0-flash",
-            config=types.GenerateContentConfig(temperature=0.),
-            contents=[
-                {
-                    "parts": [
-                        {"text": prompt_text},
-                        {
-                            "inline_data": {
-                                "mime_type": "image/jpeg",
-                                "data": base64.b64encode(image_data).decode('utf-8')
-                            }
-                        }
-                    ]
-                }
-            ]
-        )
-        return response.text.strip() if (response and response.text) else "Image description unavailable"
-
-    except Exception as e:
-        logger.error(f"Error getting image description: {str(e)}")
-        return "Image description unavailable"
-
-class DataWriter:
-    """
-    Base class for handling extracted images.
-    """
-    def write(self, path: str, data: bytes) -> None:
-        raise NotImplementedError
-
-    def post_process(self, key: str, md_content: str) -> str:
-        raise NotImplementedError
-
-class LocalImageWriter(DataWriter):
-    """
-    Writes extracted images to a local folder, then calls Gemini for:
-      - table classification
-      - or a normal short alt-text description
-    Then replaces the original placeholders in MD with these references.
-    Finally, for table images, runs TableExtractor and replaces that line
-    with references to each row/cell image.
-    """
-    def __init__(self, output_folder: str):
-        self.output_folder = output_folder
-        os.makedirs(self.output_folder, exist_ok=True)
-
-        self.images_dir = os.path.join(self.output_folder, "images")
-        os.makedirs(self.images_dir, exist_ok=True)
-
-        self.descriptions = {}
-        """
-        self.descriptions: {
-          "{extracted_path}": {
-             "data": bytes,
-             "relative_path": str, 
-             "description": "",
-             "table_classification": "TWO_COLUMN"/"THREE_COLUMN"/"NO_TABLE",
-             "final_alt": ""
-          }
-        }
-        """
-        self._img_count = 0
-
-    def write(self, path: str, data: bytes) -> None:
-        """
-        Saves images locally
-        for SVM turn on:
-            pred = self.svm_model.classify_image(data)
-            if pred == 0:
-                logger.info(f"Skipping blank image: {path}")
-                return
-        """
-        self._img_count += 1
-        local_filename = f"img_{self._img_count}.png"
-        local_path = os.path.join(self.images_dir, local_filename)
-
-        with open(local_path, "wb") as f:
-            f.write(data)
-
-        rel_path_for_md = os.path.relpath(local_path, self.output_folder)
-
-        self.descriptions[path] = {
-            "data": data,
-            "relative_path": rel_path_for_md,
-            "description": "",
-            "table_classification": "NO_TABLE",
-            "final_alt": ""
-        }
-
-    def post_process(self, key: str, md_content: str) -> str:
-        """
-        1) Classify each image with Gemini to see if it's a 2/3-col table or not.
-        2) If NO_TABLE => call gemini_for_image_description
-        3) Build final alt text
-        4) Replace ![]({key}{path}) with ![alt](relative_path)
-        5) For any "HAS TO BE PROCESSED" images => run TableExtractor
-           and replace that single line with references to each extracted cell.
-        """
-        if not self.descriptions:
-            return md_content
-
-        #table classification
-        with concurrent.futures.ThreadPoolExecutor(max_workers=len(self.descriptions)) as executor:
-            future_map = {
-                executor.submit(call_gemini_for_table_classification, info['data']): p
-                for p, info in self.descriptions.items()
-            }
-            for fut in concurrent.futures.as_completed(future_map):
-                path = future_map[fut]
-                try:
-                    classification = fut.result()
-                    self.descriptions[path]['table_classification'] = classification
-                except Exception as e:
-                    logger.error(f"[Gemini Table Classification Error] {path}: {e}")
-                    self.descriptions[path]['table_classification'] = "NO_TABLE"
-
-        with concurrent.futures.ThreadPoolExecutor(max_workers=len(self.descriptions)) as executor:
-            fut_map2 = {}
-            for path, info in self.descriptions.items():
-                if info['table_classification'] == "NO_TABLE":
-                    fut = executor.submit(call_gemini_for_image_description, info['data'])
-                    fut_map2[fut] = path
-
-            for fut in concurrent.futures.as_completed(fut_map2):
-                path = fut_map2[fut]
-                try:
-                    desc = fut.result()
-                    self.descriptions[path]['description'] = desc
-                except Exception as e:
-                    logger.error(f"[Gemini Description Error] {path}: {e}")
-                    self.descriptions[path]['description'] = "Image description unavailable"
-
-        for path, info in self.descriptions.items():
-            classification = info['table_classification']
-            if classification == "TWO_COLUMN":
-                final_alt = "HAS TO BE PROCESSED - two column table"
-            elif classification == "THREE_COLUMN":
-                final_alt = "HAS TO BE PROCESSED - three column table"
-            else:
-                final_alt = info['description'] or "Image description unavailable"
-            info['final_alt'] = final_alt
-
-        for path, info in self.descriptions.items():
-            old_md = f"![]({key}{path})"
-            new_md = f"![{info['final_alt']}]({info['relative_path']})"
-            md_content = md_content.replace(old_md, new_md)
-
-        #for table images => run TableExtractor => replace the single line with references to row cells
-        md_content = self._process_table_images_in_markdown(md_content)
-        return md_content
-
-    def _process_table_images_in_markdown(self, md_content: str) -> str:
-        """
-        1) Find images with alt text "HAS TO BE PROCESSED - (two|three) column table"
-        2) For each, run TableExtractor, generate row/cell images
-        3) Build a snippet of lines referencing each cell image
-        4) Replace the single "HAS TO BE PROCESSED" line with that snippet
-        """
-        pattern = r"!\[HAS TO BE PROCESSED - (two|three) column table\]\(([^)]+)\)"
-        matches = re.findall(pattern, md_content, flags=re.IGNORECASE)
-        if not matches:
-            return md_content
-
-        for (col_type, image_path) in matches:
-            logger.info(f"Detected table image => {image_path}, columns={col_type}")
-
-            abs_image_path = os.path.join(self.output_folder, image_path)
-            try:
-                if col_type.lower() == 'two':
-                    extractor = TableExtractor(
-                        padding=0,
-                        skip_header=True,
-                        merge_two_col_rows=True,
-                        enable_subtopic_merge=True,
-                        subtopic_threshold=0.2
-                    )
-                else:
-                    extractor = TableExtractor(
-                        padding=0,
-                        skip_header=True,
-                        merge_two_col_rows=False,
-                        enable_subtopic_merge=False,
-                        subtopic_threshold=0.2
-                    )
-
-                row_boxes = extractor.process_image(abs_image_path)
-                out_folder = abs_image_path + "_rows"
-                os.makedirs(out_folder, exist_ok=True)
-                extractor.save_extracted_cells(abs_image_path, row_boxes, out_folder)
-                logger.info(f"Table extraction done for {image_path}, saved to {out_folder}")
-
-                # snippet lines for each row/cell image
-                snippet_lines = ["**Extracted table cells:**"]
-                for i, row in enumerate(row_boxes):
-                    row_dir = os.path.join(out_folder, f"row_{i}")
-                    for j, _ in enumerate(row):
-                        cell_filename = f"col_{j}.png"
-                        cell_abs_path = os.path.join(row_dir, cell_filename)
-
-                        cell_rel_path = os.path.relpath(cell_abs_path, self.output_folder)
-                        snippet_lines.append(f"![Row {i} Col {j}]({cell_rel_path})")
-
-                new_snippet = "\n".join(snippet_lines)
-
-                # 5) Replace the original single line
-                old_line = f"![HAS TO BE PROCESSED - {col_type} column table]({image_path})"
-                md_content = md_content.replace(old_line, new_snippet)
-
-            except Exception as e:
-                logger.error(f"Error processing table image {image_path}: {e}")
-
-        return md_content
-
-class LocalPDFProcessor:
-    def __init__(self, output_folder: str):
-        self.output_folder = output_folder
-        os.makedirs(self.output_folder, exist_ok=True)
-
-        # self.svm_model = SVMModel()
-
-        with open("/home/user/magic-pdf.json", "r") as f:
-            config = json.load(f)
-
-        self.layout_mode = config["layout-config"]["model"]
-        self.formula_enable = config["formula-config"]["enable"]
-        self.table_enable = False
-        self.language = "en"
-
-    def cleanup_gpu(self):
-        gc.collect()
-        torch.cuda.empty_cache()
-        logger.info("GPU memory cleaned up.")
-
-    def process(self, pdf_path: str) -> str:
-        logger.info(f"Processing local PDF: {pdf_path}")
-        try:
-            with open(pdf_path, "rb") as f:
-                pdf_bytes = f.read()
-
-            dataset = PymuDocDataset(pdf_bytes)
-            inference = doc_analyze(
-                dataset,
-                ocr=True,
-                lang=self.language,
-                layout_model=self.layout_mode,
-                formula_enable=self.formula_enable,
-                table_enable=self.table_enable
-            )
-
-            image_writer = LocalImageWriter(self.output_folder)
-            pipe_result = inference.pipe_ocr_mode(image_writer, lang=self.language)
-
-            # Generate initial Markdown
-            md_content = pipe_result.get_markdown("local-unique-prefix/")
-            final_markdown = image_writer.post_process("local-unique-prefix/", md_content)
-
-            # Save final .md file
-            md_path = os.path.join(self.output_folder, "final_output.md")
-            with open(md_path, "w", encoding="utf-8") as f:
-                f.write(final_markdown)
-
-            logger.info(f"Markdown saved to: {md_path}")
-            return final_markdown
-
-        finally:
-            self.cleanup_gpu()
-
-if __name__ == "__main__":
-    input_pdf = "/home/user/app/input_output/a-level-pearson-mathematics-specification.pdf"
-    output_dir = "/home/user/app/input_output/outputs"
-
-    processor = LocalPDFProcessor(output_folder=output_dir)
-    md_result = processor.process(input_pdf)
\ No newline at end of file
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--- a/topic_extr.py
+++ /dev/null
@@ -1,612 +0,0 @@
-#!/usr/bin/env python3
-import os
-import re
-import gc
-import json
-import logging
-import fitz
-import base64
-import concurrent.futures
-from io import BytesIO
-from typing import List, Dict, Any
-
-import torch
-import cv2
-import numpy as np
-
-# Attempt top-level import of google.genai
-try:
-    from google import genai
-    from google.genai import types
-except ImportError:
-    genai = None
-    types = None
-
-# magic-pdf imports
-from magic_pdf.data.dataset import PymuDocDataset
-from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
-
-# table extraction logic
-from table_row_extraction import TableExtractor
-
-###############################################################################
-# Logging Setup
-###############################################################################
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-
-###############################################################################
-# PDF Utility Functions
-###############################################################################
-def unify_whitespace(text: str) -> str:
-    """
-    Replace runs of whitespace with a single space, strip leading/trailing, then lowercase.
-    """
-    return re.sub(r"\s+", " ", text).strip().lower()
-
-def create_subset_pdf(original_pdf_bytes: bytes, page_indices: List[int]) -> bytes:
-    """
-    Creates a new PDF (in memory) containing only pages in page_indices (0-based).
-    Raises ValueError if page_indices is empty or out of range.
-    """
-    if not page_indices:
-        raise ValueError("No page indices provided for subset creation.")
-
-    doc = fitz.open(stream=original_pdf_bytes, filetype="pdf")
-    new_doc = fitz.open()
-    for p in sorted(set(page_indices)):
-        if 0 <= p < doc.page_count:
-            new_doc.insert_pdf(doc, from_page=p, to_page=p)
-        else:
-            logger.error(f"Page index {p} out of range (0..{doc.page_count - 1}).")
-            raise ValueError(f"Page index {p} out of range.")
-    subset_bytes = new_doc.tobytes()
-    new_doc.close()
-    doc.close()
-    return subset_bytes
-
-###############################################################################
-# Searching in PDF
-###############################################################################
-def find_all_occurrences(pdf_bytes: bytes, search_text: str) -> List[int]:
-    """
-    Return a sorted list of 0-based pages in which `search_text` (normalized) appears,
-    scanning the entire PDF in RAW mode.
-    """
-    doc = fitz.open(stream=pdf_bytes, filetype="pdf")
-    st_norm = unify_whitespace(search_text)
-    found = []
-    for i in range(doc.page_count):
-        raw = doc[i].get_text("raw")
-        norm = unify_whitespace(raw)
-        if st_norm in norm:
-            found.append(i)
-    doc.close()
-    return sorted(found)
-
-###############################################################################
-# Gemini LLM for Subtopic Extraction
-###############################################################################
-class GeminiTopicExtractor:
-    """
-    Extract subtopics from the PDF by reading the first `num_pages` pages, calling Gemini.
-    We expect a structure like:
-      {
-        "2 Subject content and assessment information": {
-          "Paper 1 and Paper 2: Pure Mathematics": [11, 29],
-          "Paper 3: Statistics and Mechanics": [30, 42]
-        }
-      }
-    or sometimes just a flat dict:
-      {
-        "Paper 1 and Paper 2: Pure Mathematics": [15, 33],
-        "Paper 3: Statistics and Mechanics": [34, 46]
-      }
-    We'll parse both forms.
-    """
-    def __init__(self, api_key: str = None, num_pages: int = 10):
-        self.api_key = api_key or os.getenv("GEMINI_API_KEY", "")
-        if not self.api_key:
-            logger.warning("No Gemini API key for subtopic extraction.")
-        self.num_pages = num_pages
-
-    def extract_subtopics(self, pdf_path: str) -> Dict[str, List[int]]:
-        """
-        Return a dict of subtopics => [start_page, end_page].
-        Could be empty if parsing fails or the LLM can't find subtopics.
-        """
-        first_pages_text = self._read_first_pages_raw(pdf_path, self.num_pages)
-        if not first_pages_text.strip():
-            logger.error("No text from first pages => cannot extract subtopics.")
-            return {}
-
-        if genai is None or types is None:
-            logger.warning("google.genai not installed. Returning empty subtopics.")
-            return {}
-
-        prompt = f"""
-You have the first pages of a PDF specification, including a table of contents.
-
-Instructions:
-1. Identify the 'Contents' section listing all topics, subtopics, and their corresponding pages.
-2. Extract subtopic names -> [start_page, end_page], in valid JSON format only.
-3. If you can't find any subtopics, return an empty JSON.
-
-Examples:
-
-1. Given this table of contents:
-
-1 Introduction – 2
-    Why choose Edexcel A Level Mathematics? - 2
-    Supporting you in planning and implementing this qualification - 3
-    Qualification at a glance - 5
-2 Subject content and assessment information – 7
-    Paper 1 and Paper 2: Pure Mathematics - 11
-    Paper 3: Statistics and Mechanics - 30
-    Assessment Objectives - 40
-3 Administration and general information – 42
-    Entries - 42
-    Access arrangements, reasonable adjustments, special consideration and malpractice - 42
-    Student recruitment and progression - 45
-Appendix 1: Formulae – 49
-Appendix 2: Notation – 53
-Appendix 3: Use of calculators – 59
-Appendix 4: Assessment Objectives – 60
-Appendix 5: The context for the development of this qualification – 62
-Appendix 6: Transferable skills – 64
-Appendix 7: Level 3 Extended Project qualification – 65
-Appendix 8: Codes – 67
-
-The correct output should be:
-
-{{
-    "Paper 1 and Paper 2: Pure Mathematics": [11, 29],
-    "Paper 3: Statistics and Mechanics": [30, 42]
-}}
-
-2. Given this table of contents:
-
-Qualification at a glance – 1
-    Assessment Objectives and weightings - 4
-Knowledge, skills and understanding – 5
-    Theme 1: Introduction to markets and market failure - 5
-    Theme 2: The UK economy – performance and policies - 11
-    Theme 3: Business behaviour and the labour market - 21
-    Theme 4: A global perspective - 29
-Assessment – 39
-    Assessment summary - 39
-    Assessment objectives - 41
-    Assessment overview - 42
-    Breakdown of assessment objectives - 42
-        Synoptic assessment - 43
-        Discount code and performance tables - 43
-        Access arrangements, reasonable adjustments and special consideration - 44
-        Malpractice - 45
-        Equality Act 2010 and Pearson equality policy - 45
-        Synoptic assessment - 46
-        Awarding and reporting - 47
-Other information – 49
-    Student recruitment -49
-    Prior learning and other requirements -49
-    Progression - 49
-Appendix 1: Transferable skills – 53
-Appendix 2: Level 3 Extended Project qualification – 55
-Appendix 3: Quantitative skills – 59
-Appendix 4: Codes – 61
-Appendix 5: Index – 63
-
-The correct output should be:
-
-{{
-    "Theme 1: Introduction to markets and market failure": [5, 10]
-    "Theme 2: The UK economy – performance and policies": - [11, 20]
-    "Theme 3: Business behaviour and the labour market": [21, 28]
-    "Theme 4: A global perspective": [29, 38]
-}}
-
-Now, extract topics from this text:
-{first_pages_text}
-"""
-        try:
-            client = genai.Client(api_key=self.api_key)
-            response = client.models.generate_content(
-                model="gemini-2.0-flash",
-                contents=[prompt],
-                config=types.GenerateContentConfig(temperature=0.0)
-            )
-            if not response or not response.text:
-                logger.warning("No text from LLM => returning empty subtopics.")
-                return {}
-
-            raw_json = response.text.strip()
-            # Clean up triple backticks
-            cleaned = raw_json.replace("```json", "").replace("```", "")
-
-            # Attempt to parse
-            data = json.loads(cleaned)
-            # data might be nested or flat
-            # if nested, e.g. {"2 Subject content": {"Paper 1...": [11,29]}}
-            # if flat, e.g. {"Paper 1...": [11,29]}
-            # We'll unify it to a single dict of subname => [start,end].
-            final_dict = {}
-
-            # If the top-level is a dict of dict
-            # We look for a dict whose values are themselves subtopics
-            # Or it might be a direct subtopic dict
-            # We'll try a quick approach:
-            #   - If any top-level value is a dict with numeric arrays, use that
-            #   - else assume data is the direct subtopic dict
-            found_sub_dict = None
-            for k, v in data.items():
-                if isinstance(v, dict):
-                    # might be the sub-sub dict
-                    found_sub_dict = v
-                    break
-
-            if found_sub_dict is not None:
-                # parse found_sub_dict
-                for subk, rng in found_sub_dict.items():
-                    if isinstance(rng, list) and len(rng) == 2:
-                        final_dict[subk] = rng
-            else:
-                # maybe data is the direct subtopic dict
-                # parse data
-                for subk, rng in data.items():
-                    if isinstance(rng, list) and len(rng) == 2:
-                        final_dict[subk] = rng
-
-            return final_dict
-        except Exception as e:
-            logger.error(f"Gemini subtopic extraction error: {e}")
-            return {}
-
-    def _read_first_pages_raw(self, pdf_path: str, num_pages: int) -> str:
-        text_parts = []
-        try:
-            doc = fitz.open(pdf_path)
-            pages_to_read = min(num_pages, doc.page_count)
-            for i in range(pages_to_read):
-                raw_text = doc[i].get_text("raw")
-                text_parts.append(raw_text)
-            doc.close()
-        except Exception as e:
-            logger.error(f"Could not open PDF: {e}")
-        return "\n".join(text_parts)
-
-###############################################################################
-# Concurrency for Table Classification
-###############################################################################
-def call_gemini_for_table_classification(image_data: bytes, api_key: str) -> str:
-    """
-    Classify an image as TWO_COLUMN, THREE_COLUMN, or NO_TABLE using Gemini.
-    """
-    if not api_key:
-        logger.warning("No Gemini API key => NO_TABLE.")
-        return "NO_TABLE"
-    if genai is None or types is None:
-        logger.warning("google.genai not installed => NO_TABLE.")
-        return "NO_TABLE"
-
-    # Attempt to shrink
-    try:
-        arr = np.frombuffer(image_data, np.uint8)
-        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
-        if img is not None:
-            h, w, _ = img.shape
-            max_dim = 800
-            scale = 1.0
-            if max(h, w) > max_dim:
-                scale = max_dim / float(max(h, w))
-            if scale < 1.0:
-                new_w = int(w * scale)
-                new_h = int(h * scale)
-                img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_AREA)
-            encode_params = [int(cv2.IMWRITE_JPEG_QUALITY), 70]
-            success, enc = cv2.imencode(".jpg", img, encode_params)
-            if success:
-                image_data = enc.tobytes()
-    except Exception as e:
-        logger.warning(f"shrink_image_to_jpeg error: {e}")
-
-    prompt = """You are given an image. Determine if it shows a table that has exactly 2 or 3 columns.
-Return only one label:
-TWO_COLUMN
-THREE_COLUMN
-NO_TABLE
-"""
-    try:
-        client = genai.Client(api_key=api_key)
-        resp = client.models.generate_content(
-            model="gemini-2.0-flash",
-            contents=[
-                {
-                    "parts": [
-                        {"text": prompt},
-                        {
-                            "inline_data": {
-                                "mime_type": "image/jpeg",
-                                "data": base64.b64encode(image_data).decode('utf-8')
-                            }
-                        }
-                    ]
-                }
-            ],
-            config=types.GenerateContentConfig(temperature=0.0)
-        )
-        if resp and resp.text:
-            classification = resp.text.strip().upper()
-            if "THREE" in classification:
-                return "THREE_COLUMN"
-            elif "TWO" in classification:
-                return "TWO_COLUMN"
-        return "NO_TABLE"
-    except Exception as e:
-        logger.error(f"Gemini table classification error: {e}")
-        return "NO_TABLE"
-
-###############################################################################
-# LocalImageWriter
-###############################################################################
-class LocalImageWriter:
-    """
-    Writes extracted images, then does concurrency-based table classification calls.
-    """
-    def __init__(self, output_folder: str, gemini_api_key: str):
-        self.output_folder = output_folder
-        os.makedirs(self.output_folder, exist_ok=True)
-
-        self.images_dir = os.path.join(self.output_folder, "images")
-        os.makedirs(self.images_dir, exist_ok=True)
-
-        self.descriptions = {}
-        self._img_count = 0
-        self.gemini_api_key = gemini_api_key
-
-    def write(self, path: str, data: bytes) -> None:
-        self._img_count += 1
-        fname = f"img_{self._img_count}.png"
-        fpath = os.path.join(self.images_dir, fname)
-        with open(fpath, "wb") as f:
-            f.write(data)
-        rel_path = os.path.relpath(fpath, self.output_folder)
-        self.descriptions[path] = {
-            "data": data,
-            "relative_path": rel_path,
-            "table_classification": "NO_TABLE",
-            "final_alt": ""
-        }
-
-    def post_process(self, key: str, md_content: str) -> str:
-        logger.info("Classifying images to detect tables (concurrent)...")
-        with concurrent.futures.ThreadPoolExecutor(max_workers=6) as exe:
-            fut_map = {}
-            for p, info in self.descriptions.items():
-                fut = exe.submit(call_gemini_for_table_classification, info["data"], self.gemini_api_key)
-                fut_map[fut] = p
-
-            for fut in concurrent.futures.as_completed(fut_map):
-                path = fut_map[fut]
-                try:
-                    classification = fut.result()
-                    self.descriptions[path]['table_classification'] = classification
-                except Exception as e:
-                    logger.error(f"Table classification error: {e}")
-                    self.descriptions[path]['table_classification'] = "NO_TABLE"
-
-        # 2) Set final alt text
-        for p, info in self.descriptions.items():
-            cls = info['table_classification']
-            if cls == "TWO_COLUMN":
-                info['final_alt'] = "HAS TO BE PROCESSED - two column table"
-            elif cls == "THREE_COLUMN":
-                info['final_alt'] = "HAS TO BE PROCESSED - three column table"
-            else:
-                info['final_alt'] = "NO_TABLE image"
-
-        # 3) Replace placeholders in the Markdown
-        for p, info in self.descriptions.items():
-            old_md = f"![]({key}{p})"
-            new_md = f"![{info['final_alt']}]({info['relative_path']})"
-            md_content = md_content.replace(old_md, new_md)
-
-        # 4) If any table images => extract rows
-        md_content = self._process_table_images_in_markdown(md_content)
-
-        # 5) Keep only lines that are image references
-        final_lines = []
-        for line in md_content.split("\n"):
-            if re.match(r"^\!\[.*\]\(.*\)", line.strip()):
-                final_lines.append(line.strip())
-
-        return "\n".join(final_lines)
-
-    def _process_table_images_in_markdown(self, md_content: str) -> str:
-        pat = r"!\[HAS TO BE PROCESSED - (two|three) column table\]\(([^)]+)\)"
-        matches = re.findall(pat, md_content, flags=re.IGNORECASE)
-        if not matches:
-            return md_content
-
-        for (col_type, image_path) in matches:
-            logger.info(f"Processing table image => {image_path}, columns={col_type}")
-            abs_image_path = os.path.join(self.output_folder, image_path)
-            try:
-                if col_type.lower() == 'two':
-                    extractor = TableExtractor(
-                        skip_header=True,
-                        merge_two_col_rows=True,
-                        enable_subtopic_merge=True,
-                        subtopic_threshold=0.2
-                    )
-                else:
-                    extractor = TableExtractor(
-                        skip_header=True,
-                        merge_two_col_rows=False,
-                        enable_subtopic_merge=False,
-                        subtopic_threshold=0.2
-                    )
-                row_boxes = extractor.process_image(abs_image_path)
-                out_folder = abs_image_path + "_rows"
-                os.makedirs(out_folder, exist_ok=True)
-                extractor.save_extracted_cells(abs_image_path, row_boxes, out_folder)
-
-                snippet = ["**Extracted table cells:**"]
-                for i, row in enumerate(row_boxes):
-                    row_dir = os.path.join(out_folder, f"row_{i}")
-                    for j, _ in enumerate(row):
-                        cell_file = f"col_{j}.png"
-                        cell_path = os.path.join(row_dir, cell_file)
-                        relp = os.path.relpath(cell_path, self.output_folder)
-                        snippet.append(f"![Row {i} Col {j}]({relp})")
-
-                new_snip = "\n".join(snippet)
-                old_line = f"![HAS TO BE PROCESSED - {col_type} column table]({image_path})"
-                md_content = md_content.replace(old_line, new_snip)
-            except Exception as e:
-                logger.error(f"Error processing table image {image_path}: {e}")
-
-        return md_content
-
-###############################################################################
-# MineruNoTextProcessor
-###############################################################################
-class MineruNoTextProcessor:
-    """
-    1) Use Gemini to get subtopics => e.g. {"Paper 1 and Paper 2: Pure Mathematics": [11,29], ...}
-    2) For each subtopic name => find real occurrence in PDF at or after (start_page-1).
-    3) offset = occurrence_page - (start_page-1). clamp offset >= 0
-    4) Flatten final pages, subset PDF, run magic-pdf => concurrency => final MD
-    5) If no subtopics found, process entire PDF as fallback.
-    """
-    def __init__(self, output_folder: str, gemini_api_key: str = None):
-        self.output_folder = output_folder
-        os.makedirs(self.output_folder, exist_ok=True)
-
-        self.layout_model = "doclayout_yolo"
-        self.formula_enable = True
-        self.table_enable = False
-        self.language = "en"
-
-        # Use our new flexible approach
-        self.subtopic_extractor = GeminiTopicExtractor(api_key=gemini_api_key, num_pages=10)
-        self.gemini_api_key = gemini_api_key or os.getenv("GEMINI_API_KEY", "")
-
-    def cleanup_gpu(self):
-        try:
-            gc.collect()
-            torch.cuda.empty_cache()
-            logger.info("GPU memory cleaned up.")
-        except Exception as e:
-            logger.error(f"Error during GPU cleanup: {e}")
-
-    def process(self, pdf_path: str) -> str:
-        logger.info(f"Processing PDF: {pdf_path}")
-        try:
-            # 1) Extract subtopics from Gemini
-            subtopics = self.subtopic_extractor.extract_subtopics(pdf_path)
-            logger.info(f"Gemini returned subtopics: {subtopics}")
-
-            # 2) Read entire PDF
-            with open(pdf_path, "rb") as f:
-                pdf_bytes = f.read()
-            doc = fitz.open(stream=pdf_bytes, filetype="pdf")
-            total_pages = doc.page_count
-            doc.close()
-
-            final_pages = set()
-            if not subtopics:
-                logger.warning("No subtopics found. We'll process the entire PDF as fallback.")
-                final_pages = set(range(total_pages))
-            else:
-                # For each subtopic, find occurrence >= (start_p-1)
-                for subname, rng in subtopics.items():
-                    if not (isinstance(rng, list) and len(rng) == 2):
-                        logger.warning(f"Skipping subtopic '{subname}' => invalid range {rng}")
-                        continue
-                    start_p, end_p = rng
-                    if start_p > end_p:
-                        logger.warning(f"Skipping subtopic '{subname}' => start> end {rng}")
-                        continue
-
-                    # find occurrences
-                    occs = find_all_occurrences(pdf_bytes, subname)
-                    logger.info(f"Occurrences of subtopic '{subname}': {occs}")
-
-                    doc_start_0 = start_p - 1
-                    chosen_page = None
-                    for p in occs:
-                        if p >= doc_start_0:
-                            chosen_page = p
-                            break
-                    if chosen_page is None:
-                        # fallback to last or 0
-                        if occs:
-                            chosen_page = occs[-1]
-                            logger.warning(f"No occurrence >= {doc_start_0} for '{subname}'. Using last => {chosen_page}")
-                        else:
-                            chosen_page = 0
-                            logger.warning(f"No occurrences for '{subname}'. Using page 0.")
-
-                    raw_offset = chosen_page - doc_start_0
-                    offset = max(0, raw_offset)
-                    logger.info(f"Subtopic '{subname}': doc_start={start_p}, chosen_page={chosen_page}, raw_offset={raw_offset}, offset={offset}")
-
-                    s0 = (start_p - 1) + offset
-                    e0 = (end_p - 1) + offset
-                    s0 = max(0, min(total_pages - 1, s0))
-                    e0 = max(0, min(total_pages - 1, e0))
-                    for pp in range(s0, e0 + 1):
-                        final_pages.add(pp)
-
-            # 3) If final_pages is empty => fallback entire PDF
-            if not final_pages:
-                logger.warning("No valid pages after offset. We'll process entire PDF.")
-                final_pages = set(range(total_pages))
-
-            logger.info(f"Processing pages (0-based): {sorted(final_pages)}")
-            subset_pdf_bytes = create_subset_pdf(pdf_bytes, sorted(final_pages))
-
-            # 4) doc_analyze => concurrency => final MD
-            dataset = PymuDocDataset(subset_pdf_bytes)
-            inference = doc_analyze(
-                dataset,
-                ocr=True,
-                lang=self.language,
-                layout_model=self.layout_model,
-                formula_enable=self.formula_enable,
-                table_enable=self.table_enable
-            )
-            logger.info("doc_analyze complete. Extracting images...")
-
-            writer = LocalImageWriter(self.output_folder, self.gemini_api_key)
-            pipe_result = inference.pipe_ocr_mode(writer, lang=self.language)
-            md_content = pipe_result.get_markdown("local-unique-prefix/")
-
-            final_markdown = writer.post_process("local-unique-prefix/", md_content)
-
-            # 5) Save
-            out_path = os.path.join(self.output_folder, "final_output.md")
-            with open(out_path, "w", encoding="utf-8") as f:
-                f.write(final_markdown)
-
-            logger.info(f"Markdown saved to: {out_path}")
-            return final_markdown
-
-        finally:
-            self.cleanup_gpu()
-
-###############################################################################
-# Example Main
-###############################################################################
-if __name__ == "__main__":
-    input_pdf = "/home/user/app/input_output/a-level-pearson-mathematics-specification.pdf"
-    output_dir = "/home/user/app/output"
-    gemini_key = os.getenv("GEMINI_API_KEY", "AIzaSyDtoakpXa2pjJwcQB6TJ5QaXHNSA5JxcrU")
-
-    try:
-        processor = MineruNoTextProcessor(output_folder=output_dir, gemini_api_key=gemini_key)
-        md_output = processor.process(input_pdf)
-        print("Final Markdown Output:")
-        print(md_output)
-    except Exception as e:
-        logger.error(f"Processing failed: {e}")
\ No newline at end of file
diff --git a/topic_extraction.py b/topic_extraction.py
index 6396b7c33b51c8ca41e1b8606bbeb10312e17741..6859593b798936e00ccd2da819de2c95e93d679e 100644
--- a/topic_extraction.py
+++ b/topic_extraction.py
@@ -10,164 +10,213 @@ import concurrent.futures
 from io import BytesIO
 from typing import List, Dict, Any
 
-from google import genai
-from google.genai import types
-
 import torch
 import cv2
+import numpy as np
+
+# Attempt top-level import of google.genai
+try:
+    from google import genai
+    from google.genai import types
+except ImportError:
+    genai = None
+    types = None
 
+# magic-pdf imports
 from magic_pdf.data.dataset import PymuDocDataset
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 
+# table extraction logic
 from table_row_extraction import TableExtractor
 
+###############################################################################
+# Logging Setup
+###############################################################################
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
+###############################################################################
+# PDF Utility Functions
+###############################################################################
+def unify_whitespace(text: str) -> str:
+    """
+    Replace runs of whitespace with a single space, strip leading/trailing, then lowercase.
+    """
+    return re.sub(r"\s+", " ", text).strip().lower()
+
 def create_subset_pdf(original_pdf_bytes: bytes, page_indices: List[int]) -> bytes:
     """
-    Using PyMuPDF, create a new PDF that contains only the pages in page_indices (0-based).
-    Return the resulting PDF as bytes.
+    Creates a new PDF (in memory) containing only pages in page_indices (0-based).
+    Raises ValueError if page_indices is empty or out of range.
     """
     if not page_indices:
-        return original_pdf_bytes  # If empty, just return original
+        raise ValueError("No page indices provided for subset creation.")
 
     doc = fitz.open(stream=original_pdf_bytes, filetype="pdf")
-    new_doc = fitz.open()  # empty PDF to insert pages into
-
-    sorted_pages = sorted(set(page_indices))
-    for p in sorted_pages:
+    new_doc = fitz.open()
+    for p in sorted(set(page_indices)):
         if 0 <= p < doc.page_count:
             new_doc.insert_pdf(doc, from_page=p, to_page=p)
         else:
-            logger.warning(f"Page index {p} is out of range, skipping.")
-
+            logger.error(f"Page index {p} out of range (0..{doc.page_count - 1}).")
+            raise ValueError(f"Page index {p} out of range.")
     subset_bytes = new_doc.tobytes()
     new_doc.close()
     doc.close()
     return subset_bytes
 
+###############################################################################
+# Searching in PDF
+###############################################################################
+def find_all_occurrences(pdf_bytes: bytes, search_text: str) -> List[int]:
+    """
+    Return a sorted list of 0-based pages in which `search_text` (normalized) appears,
+    scanning the entire PDF in RAW mode.
+    """
+    doc = fitz.open(stream=pdf_bytes, filetype="pdf")
+    st_norm = unify_whitespace(search_text)
+    found = []
+    for i in range(doc.page_count):
+        raw = doc[i].get_text("raw")
+        norm = unify_whitespace(raw)
+        if st_norm in norm:
+            found.append(i)
+    doc.close()
+    return sorted(found)
+
+###############################################################################
+# Gemini LLM for Subtopic Extraction
+###############################################################################
 class GeminiTopicExtractor:
     """
-    Uses Gemini to parse the PDF text, looking specifically for 
-    "2 Subject content and assessment information" and subtopics with pages.
+    Extract subtopics from the PDF by reading the first `num_pages` pages, calling Gemini.
+    We expect a structure like:
+      {
+        "2 Subject content and assessment information": {
+          "Paper 1 and Paper 2: Pure Mathematics": [11, 29],
+          "Paper 3: Statistics and Mechanics": [30, 42]
+        }
+      }
+    or sometimes just a flat dict:
+      {
+        "Paper 1 and Paper 2: Pure Mathematics": [15, 33],
+        "Paper 3: Statistics and Mechanics": [34, 46]
+      }
+    We'll parse both forms.
     """
-    def __init__(self, api_key: str = None):
-        self.api_key = api_key or os.getenv("GEMINI_API_KEY", "AIzaSyDtoakpXa2pjJwcQB6TJ5QaXHNSA5JxcrU")
+    def __init__(self, api_key: str = None, num_pages: int = 10):
+        self.api_key = api_key or os.getenv("GEMINI_API_KEY", "")
         if not self.api_key:
-            raise ValueError("Gemini API key not found in environment or constructor.")
-
-        if genai is None or types is None:
-            logger.warning("google.genai is not installed. Subtopic extraction won't work.")
+            logger.warning("No Gemini API key for subtopic extraction.")
+        self.num_pages = num_pages
 
-    def extract_subtopics(self, pdf_path: str) -> Dict[str, Any]:
+    def extract_subtopics(self, pdf_path: str) -> Dict[str, List[int]]:
         """
-        1) Read entire PDF text
-        2) Ask Gemini for JSON structure like:
-           {
-             "2 Subject content and assessment information": {
-                "Paper 1 and Paper 2: Pure Mathematics": [11, 29],
-                "Paper 3: Statistics and Mechanics": [30, 42]
-             }
-           }
-        3) Return parsed JSON
+        Return a dict of subtopics => [start_page, end_page].
+        Could be empty if parsing fails or the LLM can't find subtopics.
         """
-        text_content = self._read_entire_pdf(pdf_path)
-        if not text_content.strip():
-            logger.warning("No text extracted from PDF. Returning empty JSON.")
+        first_pages_text = self._read_first_pages_raw(pdf_path, self.num_pages)
+        if not first_pages_text.strip():
+            logger.error("No text from first pages => cannot extract subtopics.")
+            return {}
+
+        if genai is None or types is None:
+            logger.warning("google.genai not installed. Returning empty subtopics.")
             return {}
 
         prompt = f"""
-You are given the text of a specification PDF. 
-Instructions:
-                1. Identify the "Contents" section, which lists all topics, subtopics, and their corresponding pages.
-                2. Extract only the **highest-level, subject-related subtopics** (ignore organizational or administrative sections).
-                3. For subtopics, include the full range of pages from the first to the last subtopic.
-                4. Return the output in the following JSON format:
-                
-                    {{
-                        "topic_name": [start_page, end_page]
-                    }}
-                
-                Important Notes:
-                - Ignore non-subject-related sections (e.g., "Introduction", "Exam Guidelines", "Appendices", "Assessment, Qualification at a glance").
-                - The extracted subtopics should represent major academic areas, not organizational or structural elements.
-                - Make sure that all of the pages for a subtopic are included, end page should be the start page of the topic
-                  that comes next after the extracted one in contents section.
-                
-                Examples:
-                1. Given this table of contents:
-                    1 Introduction – 2
-                        Why choose Edexcel A Level Mathematics? - 2
-                        Supporting you in planning and implementing this qualification - 3
-                        Qualification at a glance - 5
-                    2 Subject content and assessment information – 7
-                        Paper 1 and Paper 2: Pure Mathematics - 11
-                        Paper 3: Statistics and Mechanics - 30
-                        Assessment Objectives - 40
-                    3 Administration and general information – 42
-                        Entries - 42
-                        Access arrangements, reasonable adjustments, special consideration and malpractice - 42
-                        Student recruitment and progression - 45
-                    Appendix 1: Formulae – 49
-                    Appendix 2: Notation – 53
-                    Appendix 3: Use of calculators – 59
-                    Appendix 4: Assessment Objectives – 60
-                    Appendix 5: The context for the development of this qualification – 62
-                    Appendix 6: Transferable skills – 64
-                    Appendix 7: Level 3 Extended Project qualification – 65
-                    Appendix 8: Codes – 67
-                
-                   The correct output should be:
-                
-                    {{
-                        "Paper 1 and Paper 2: Pure Mathematics": [11, 29],
-                        "Paper 3: Statistics and Mechanics": [30, 42]
-                    }}
-                
-                2. Given this table of contents:
-                    Qualification at a glance – 1
-                        Assessment Objectives and weightings - 4
-                    Knowledge, skills and understanding – 5
-                        Theme 1: Introduction to markets and market failure - 5
-                        Theme 2: The UK economy – performance and policies - 11
-                        Theme 3: Business behaviour and the labour market - 21
-                        Theme 4: A global perspective - 29
-                    Assessment – 39
-                        Assessment summary - 39
-                        Assessment objectives - 41
-                        Assessment overview - 42
-                        Breakdown of assessment objectives - 42
-                            Synoptic assessment - 43
-                            Discount code and performance tables - 43
-                            Access arrangements, reasonable adjustments and special consideration - 44
-                            Malpractice - 45
-                            Equality Act 2010 and Pearson equality policy - 45
-                            Synoptic assessment - 46
-                            Awarding and reporting - 47
-                    Other information – 49
-                        Student recruitment -49
-                        Prior learning and other requirements -49
-                        Progression - 49
-                    Appendix 1: Transferable skills – 53
-                    Appendix 2: Level 3 Extended Project qualification – 55
-                    Appendix 3: Quantitative skills – 59
-                    Appendix 4: Codes – 61
-                    Appendix 5: Index – 63
-                
-                   The correct output should be:
-                
-                    {{
-                        "Theme 1: Introduction to markets and market failure": [5, 10]
-                        "Theme 2: The UK economy – performance and policies": - [11, 20]
-                        "Theme 3: Business behaviour and the labour market": [21, 28]
-                        "Theme 4: A global perspective": [29, 38]
-                    }}
-                    
-                    Now, extract topics from this text:{text_content}
-        """
+You have the first pages of a PDF specification, including a table of contents.
 
+Instructions:
+1. Identify the 'Contents' section listing all topics, subtopics, and their corresponding pages.
+2. Identify the major academic subtopics (common desired topic names "Paper X", "Theme X", "Content of X").
+3. For each subtopic, give the range of pages [start_page, end_page] (1-based) from the table of contents.
+4. Output only valid JSON of the form:
+    {{
+    "Subtopic A": [start_page, end_page],
+    "Subtopic B": [start_page, end_page]
+    }}
+5. If you can't find any subtopics, return an empty JSON.
+
+Important notes:
+- the correct "end_page" must be the page number of the next topic or subtopic minus 1.
+
+Examples:
+
+1. Given this table of contents:
+
+1 Introduction – 2
+    Why choose Edexcel A Level Mathematics? - 2
+    Supporting you in planning and implementing this qualification - 3
+    Qualification at a glance - 5
+2 Subject content and assessment information – 7
+    Paper 1 and Paper 2: Pure Mathematics - 11
+    Paper 3: Statistics and Mechanics - 30
+    Assessment Objectives - 40
+3 Administration and general information – 42
+    Entries - 42
+    Access arrangements, reasonable adjustments, special consideration and malpractice - 42
+    Student recruitment and progression - 45
+Appendix 1: Formulae – 49
+Appendix 2: Notation – 53
+Appendix 3: Use of calculators – 59
+Appendix 4: Assessment Objectives – 60
+Appendix 5: The context for the development of this qualification – 62
+Appendix 6: Transferable skills – 64
+Appendix 7: Level 3 Extended Project qualification – 65
+Appendix 8: Codes – 67
+
+The correct output should be:
+
+{{
+    "Paper 1 and Paper 2: Pure Mathematics": [11, 29],
+    "Paper 3: Statistics and Mechanics": [30, 42]
+}}
+
+2. Given this table of contents:
+
+Qualification at a glance – 1
+    Assessment Objectives and weightings - 4
+Knowledge, skills and understanding – 5
+    Theme 1: Introduction to markets and market failure - 5
+    Theme 2: The UK economy – performance and policies - 11
+    Theme 3: Business behaviour and the labour market - 21
+    Theme 4: A global perspective - 29
+Assessment – 39
+    Assessment summary - 39
+    Assessment objectives - 41
+    Assessment overview - 42
+    Breakdown of assessment objectives - 42
+        Synoptic assessment - 43
+        Discount code and performance tables - 43
+        Access arrangements, reasonable adjustments and special consideration - 44
+        Malpractice - 45
+        Equality Act 2010 and Pearson equality policy - 45
+        Synoptic assessment - 46
+        Awarding and reporting - 47
+Other information – 49
+    Student recruitment -49
+    Prior learning and other requirements -49
+    Progression - 49
+Appendix 1: Transferable skills – 53
+Appendix 2: Level 3 Extended Project qualification – 55
+Appendix 3: Quantitative skills – 59
+Appendix 4: Codes – 61
+Appendix 5: Index – 63
+
+The correct output should be:
+
+{{
+    "Theme 1: Introduction to markets and market failure": [5, 10]
+    "Theme 2: The UK economy – performance and policies": [11, 20]
+    "Theme 3: Business behaviour and the labour market": [21, 28]
+    "Theme 4: A global perspective": [29, 38]
+}}
+
+Now, extract topics from this text:
+{first_pages_text}
+"""
         try:
             client = genai.Client(api_key=self.api_key)
             response = client.models.generate_content(
@@ -175,59 +224,109 @@ Instructions:
                 contents=[prompt],
                 config=types.GenerateContentConfig(temperature=0.0)
             )
-            raw_text = response.text.strip() if response and response.text else "{}"
-            # Clean up any triple backticks
-            cleaned = raw_text.replace("```json", "").replace("```", "")
+            if not response or not response.text:
+                logger.warning("No text from LLM => returning empty subtopics.")
+                return {}
+
+            raw_json = response.text.strip()
+            # Clean up triple backticks
+            cleaned = raw_json.replace("```json", "").replace("```", "")
+
+            # Attempt to parse
             data = json.loads(cleaned)
-            return data
+            # data might be nested or flat
+            # if nested, e.g. {"2 Subject content": {"Paper 1...": [11,29]}}
+            # if flat, e.g. {"Paper 1...": [11,29]}
+            # We'll unify it to a single dict of subname => [start,end].
+            final_dict = {}
+
+            # If the top-level is a dict of dict
+            # We look for a dict whose values are themselves subtopics
+            # Or it might be a direct subtopic dict
+            # We'll try a quick approach:
+            #   - If any top-level value is a dict with numeric arrays, use that
+            #   - else assume data is the direct subtopic dict
+            found_sub_dict = None
+            for k, v in data.items():
+                if isinstance(v, dict):
+                    # might be the sub-sub dict
+                    found_sub_dict = v
+                    break
+
+            if found_sub_dict is not None:
+                # parse found_sub_dict
+                for subk, rng in found_sub_dict.items():
+                    if isinstance(rng, list) and len(rng) == 2:
+                        final_dict[subk] = rng
+            else:
+                # maybe data is the direct subtopic dict
+                # parse data
+                for subk, rng in data.items():
+                    if isinstance(rng, list) and len(rng) == 2:
+                        final_dict[subk] = rng
+
+            return final_dict
         except Exception as e:
-            logger.error(f"Error from Gemini subtopic extraction: {e}")
+            logger.error(f"Gemini subtopic extraction error: {e}")
             return {}
 
-    def _read_entire_pdf(self, pdf_path: str) -> str:
-        """
-        Return the entire PDF text by concatenating all pages.
-        """
+    def _read_first_pages_raw(self, pdf_path: str, num_pages: int) -> str:
         text_parts = []
         try:
             doc = fitz.open(pdf_path)
-            for p in range(doc.page_count):
-                page_text = doc.load_page(p).get_text()
-                text_parts.append(page_text)
+            pages_to_read = min(num_pages, doc.page_count)
+            for i in range(pages_to_read):
+                raw_text = doc[i].get_text("raw")
+                text_parts.append(raw_text)
             doc.close()
         except Exception as e:
-            logger.error(f"Could not open/read PDF: {e}")
+            logger.error(f"Could not open PDF: {e}")
         return "\n".join(text_parts)
 
-def call_gemini_for_table_classification(image_data: bytes) -> str:
+###############################################################################
+# Concurrency for Table Classification
+###############################################################################
+def call_gemini_for_table_classification(image_data: bytes, api_key: str) -> str:
+    """
+    Classify an image as TWO_COLUMN, THREE_COLUMN, or NO_TABLE using Gemini.
+    """
+    if not api_key:
+        logger.warning("No Gemini API key => NO_TABLE.")
+        return "NO_TABLE"
     if genai is None or types is None:
-        logger.warning("Gemini not available. Returning NO_TABLE.")
+        logger.warning("google.genai not installed => NO_TABLE.")
         return "NO_TABLE"
 
-    prompt = """You are given an image. Determine if it shows a table that has exactly 2 or 3 columns. 
-The three-column 'table' image include such key features:
-    - Three columns header columns
-    - Headers like 'Topics', 'Content', 'Guidelines'
-    - Numbered sections (e.g., 8.4, 9.1)
-    - Educational curriculum-style structure
-The two-column 'table' image include such key features:
-    - Two columns header columns
-    - Headers like 'Subject content' and 'Additional information'
-    - Numbered sections (e.g., 2.1, 3.4)
-    - Educational curriculum-style structure
-    - Bullet description in 'Additional information'
-If the image is a relevant table with 2 columns, respond with 'TWO_COLUMN'.
-If the image is a relevant table with 3 columns, respond with 'THREE_COLUMN'.
-If the image does not show a table at all, respond with 'NO_TABLE'.
-Return only one of these exact labels as your entire response:
+    # Attempt to shrink
+    try:
+        arr = np.frombuffer(image_data, np.uint8)
+        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+        if img is not None:
+            h, w, _ = img.shape
+            max_dim = 800
+            scale = 1.0
+            if max(h, w) > max_dim:
+                scale = max_dim / float(max(h, w))
+            if scale < 1.0:
+                new_w = int(w * scale)
+                new_h = int(h * scale)
+                img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_AREA)
+            encode_params = [int(cv2.IMWRITE_JPEG_QUALITY), 70]
+            success, enc = cv2.imencode(".jpg", img, encode_params)
+            if success:
+                image_data = enc.tobytes()
+    except Exception as e:
+        logger.warning(f"shrink_image_to_jpeg error: {e}")
+
+    prompt = """You are given an image. Determine if it shows a table that has exactly 2 or 3 columns.
+Return only one label:
 TWO_COLUMN
 THREE_COLUMN
 NO_TABLE
 """
-
     try:
-        client = genai.Client(api_key=os.getenv("GEMINI_API_KEY", "AIzaSyDtoakpXa2pjJwcQB6TJ5QaXHNSA5JxcrU"))
-        response = client.models.generate_content(
+        client = genai.Client(api_key=api_key)
+        resp = client.models.generate_content(
             model="gemini-2.0-flash",
             contents=[
                 {
@@ -244,70 +343,25 @@ NO_TABLE
             ],
             config=types.GenerateContentConfig(temperature=0.0)
         )
-        classification = response.text.strip() if (response and response.text) else "NO_TABLE"
-        classification = classification.upper()
-        if "THREE" in classification:
-            return "THREE_COLUMN"
-        elif "TWO" in classification:
-            return "TWO_COLUMN"
-        else:
-            return "NO_TABLE"
-
-    except Exception as e:
-        logger.error(f"Table classification error: {e}")
+        if resp and resp.text:
+            classification = resp.text.strip().upper()
+            if "THREE" in classification:
+                return "THREE_COLUMN"
+            elif "TWO" in classification:
+                return "TWO_COLUMN"
         return "NO_TABLE"
-
-def call_gemini_for_image_description(image_data: bytes) -> str:
-    if genai is None or types is None:
-        logger.warning("Gemini not available. Returning fallback desc.")
-        return "Image description unavailable"
-
-    prompt_text = """The provided image is a part of a question paper or markscheme.
-Extract all the necessary information from the image to be able to identify the question.
-To identify the question, we only need the following: question number and question part.
-Don't include redundant information.
-For example, if image contains text like: "Q1 Part A Answer: Life on earth was created by diety..."
-you should return just "Q1 Part A Mark Scheme"
-If there is no text on this image, return the description of the image. 20 words max.
-If there are not enough data, consider information from the surrounding context.
-Additionally, if the image contains a truncated part, you must describe it and mark as a
-part of some another image that goes before or after current image.
-If the image is of a multiple-choice question’s options, then modify your answer by appending
-'MCQ: A [option] B [option] C [option] D [option]' (replacing [option] with the actual options).
-Otherwise, follow the above instructions strictly.
-"""
-
-    try:
-        client = genai.Client(api_key=os.getenv("GEMINI_API_KEY", "AIzaSyDtoakpXa2pjJwcQB6TJ5QaXHNSA5JxcrU"))
-        response = client.models.generate_content(
-            model="gemini-2.0-flash",
-            contents=[
-                {
-                    "parts": [
-                        {"text": prompt_text},
-                        {
-                            "inline_data": {
-                                "mime_type": "image/jpeg",
-                                "data": base64.b64encode(image_data).decode('utf-8')
-                            }
-                        }
-                    ]
-                }
-            ],
-            config=types.GenerateContentConfig(temperature=0.0)
-        )
-        return response.text.strip() if response and response.text else "Image description unavailable"
-
     except Exception as e:
-        logger.error(f"Gemini image description error: {e}")
-        return "Image description unavailable"
+        logger.error(f"Gemini table classification error: {e}")
+        return "NO_TABLE"
 
+###############################################################################
+# LocalImageWriter
+###############################################################################
 class LocalImageWriter:
     """
-    Only writes images, does table classification, then modifies final MD
-    so that we keep only table references.
+    Writes extracted images, then does concurrency-based table classification calls.
     """
-    def __init__(self, output_folder: str):
+    def __init__(self, output_folder: str, gemini_api_key: str):
         self.output_folder = output_folder
         os.makedirs(self.output_folder, exist_ok=True)
 
@@ -316,131 +370,124 @@ class LocalImageWriter:
 
         self.descriptions = {}
         self._img_count = 0
+        self.gemini_api_key = gemini_api_key
 
     def write(self, path: str, data: bytes) -> None:
         self._img_count += 1
-        local_filename = f"img_{self._img_count}.png"
-        local_path = os.path.join(self.images_dir, local_filename)
-
-        with open(local_path, "wb") as f:
+        fname = f"img_{self._img_count}.png"
+        fpath = os.path.join(self.images_dir, fname)
+        with open(fpath, "wb") as f:
             f.write(data)
-
-        rel_path_for_md = os.path.relpath(local_path, self.output_folder)
+        rel_path = os.path.relpath(fpath, self.output_folder)
         self.descriptions[path] = {
             "data": data,
-            "relative_path": rel_path_for_md,
+            "relative_path": rel_path,
             "table_classification": "NO_TABLE",
             "final_alt": ""
         }
 
     def post_process(self, key: str, md_content: str) -> str:
-        # 1) Table classification
-        with concurrent.futures.ThreadPoolExecutor(max_workers=len(self.descriptions)) as executor:
-            fut_map = {
-                executor.submit(call_gemini_for_table_classification, info["data"]): p
-                for p, info in self.descriptions.items()
-            }
+        logger.info("Classifying images to detect tables (concurrent)...")
+        with concurrent.futures.ThreadPoolExecutor(max_workers=6) as exe:
+            fut_map = {}
+            for p, info in self.descriptions.items():
+                fut = exe.submit(call_gemini_for_table_classification, info["data"], self.gemini_api_key)
+                fut_map[fut] = p
+
             for fut in concurrent.futures.as_completed(fut_map):
                 path = fut_map[fut]
                 try:
                     classification = fut.result()
                     self.descriptions[path]['table_classification'] = classification
                 except Exception as e:
-                    logger.error(f"[Gemini Table Classification Error for {path}]: {e}")
+                    logger.error(f"Table classification error: {e}")
                     self.descriptions[path]['table_classification'] = "NO_TABLE"
 
-        # 2) If NO_TABLE => normal gemini-based description
-        with concurrent.futures.ThreadPoolExecutor(max_workers=len(self.descriptions)) as executor:
-            fut_map2 = {}
-            for p, info in self.descriptions.items():
-                if info['table_classification'] == "NO_TABLE":
-                    fut = executor.submit(call_gemini_for_image_description, info['data'])
-                    fut_map2[fut] = p
-
-            for fut in concurrent.futures.as_completed(fut_map2):
-                path = fut_map2[fut]
-                try:
-                    desc = fut.result()
-                    self.descriptions[path]['final_alt'] = desc
-                except Exception as e:
-                    logger.error(f"[Gemini Desc Error for {path}]: {e}")
-                    self.descriptions[path]['final_alt'] = "Image description unavailable"
-
-        #If 2/3-col => "HAS TO BE PROCESSED"
+        # 2) Set final alt text
         for p, info in self.descriptions.items():
             cls = info['table_classification']
             if cls == "TWO_COLUMN":
                 info['final_alt'] = "HAS TO BE PROCESSED - two column table"
             elif cls == "THREE_COLUMN":
                 info['final_alt'] = "HAS TO BE PROCESSED - three column table"
-            elif not info['final_alt']:
-                info['final_alt'] = "Image description unavailable"
+            else:
+                info['final_alt'] = "NO_TABLE image"
 
+        # 3) Replace placeholders in the Markdown
         for p, info in self.descriptions.items():
             old_md = f"![]({key}{p})"
             new_md = f"![{info['final_alt']}]({info['relative_path']})"
             md_content = md_content.replace(old_md, new_md)
 
+        # 4) If any table images => extract rows
         md_content = self._process_table_images_in_markdown(md_content)
 
-        # 6) **Remove all text** => keep only lines that are image references
+        # 5) Keep only lines that are image references
         final_lines = []
         for line in md_content.split("\n"):
-            # We only keep lines that start with "!" or have "!["
-            # (i.e. lines referencing images)
             if re.match(r"^\!\[.*\]\(.*\)", line.strip()):
                 final_lines.append(line.strip())
-        new_md = "\n".join(final_lines)
-        return new_md
+
+        return "\n".join(final_lines)
 
     def _process_table_images_in_markdown(self, md_content: str) -> str:
-        pattern = r"!\[HAS TO BE PROCESSED - (two|three) column table\]\(([^)]+)\)"
-        matches = re.findall(pattern, md_content, flags=re.IGNORECASE)
+        pat = r"!\[HAS TO BE PROCESSED - (two|three) column table\]\(([^)]+)\)"
+        matches = re.findall(pat, md_content, flags=re.IGNORECASE)
         if not matches:
             return md_content
 
         for (col_type, image_path) in matches:
-            logger.info(f"Detected table image => {image_path}, columns={col_type}")
+            logger.info(f"Processing table image => {image_path}, columns={col_type}")
             abs_image_path = os.path.join(self.output_folder, image_path)
             try:
                 if col_type.lower() == 'two':
                     extractor = TableExtractor(
+                        skip_header=True,
                         merge_two_col_rows=True,
                         enable_subtopic_merge=True,
                         subtopic_threshold=0.2
                     )
                 else:
                     extractor = TableExtractor(
+                        skip_header=True,
                         merge_two_col_rows=False,
-                        enable_subtopic_merge=False
+                        enable_subtopic_merge=False,
+                        subtopic_threshold=0.2
                     )
-
                 row_boxes = extractor.process_image(abs_image_path)
                 out_folder = abs_image_path + "_rows"
                 os.makedirs(out_folder, exist_ok=True)
                 extractor.save_extracted_cells(abs_image_path, row_boxes, out_folder)
 
-                # Build snippet
-                snippet_lines = ["**Extracted table cells:**"]
+                snippet = ["**Extracted table cells:**"]
                 for i, row in enumerate(row_boxes):
                     row_dir = os.path.join(out_folder, f"row_{i}")
                     for j, _ in enumerate(row):
-                        cell_filename = f"col_{j}.png"
-                        cell_abs_path = os.path.join(row_dir, cell_filename)
-                        cell_rel_path = os.path.relpath(cell_abs_path, self.output_folder)
-                        snippet_lines.append(f"![Row {i} Col {j}]({cell_rel_path})")
+                        cell_file = f"col_{j}.png"
+                        cell_path = os.path.join(row_dir, cell_file)
+                        relp = os.path.relpath(cell_path, self.output_folder)
+                        snippet.append(f"![Row {i} Col {j}]({relp})")
 
-                new_snippet = "\n".join(snippet_lines)
+                new_snip = "\n".join(snippet)
                 old_line = f"![HAS TO BE PROCESSED - {col_type} column table]({image_path})"
-                md_content = md_content.replace(old_line, new_snippet)
-
+                md_content = md_content.replace(old_line, new_snip)
             except Exception as e:
                 logger.error(f"Error processing table image {image_path}: {e}")
 
         return md_content
 
+###############################################################################
+# MineruNoTextProcessor
+###############################################################################
 class MineruNoTextProcessor:
-    def __init__(self, output_folder: str):
+    """
+    1) Use Gemini to get subtopics => e.g. {"Paper 1 and Paper 2: Pure Mathematics": [11,29], ...}
+    2) For each subtopic name => find real occurrence in PDF at or after (start_page-1).
+    3) offset = occurrence_page - (start_page-1). clamp offset >= 0
+    4) Flatten final pages, subset PDF, run magic-pdf => concurrency => final MD
+    5) If no subtopics found, process entire PDF as fallback.
+    """
+    def __init__(self, output_folder: str, gemini_api_key: str = None):
         self.output_folder = output_folder
         os.makedirs(self.output_folder, exist_ok=True)
 
@@ -449,7 +496,9 @@ class MineruNoTextProcessor:
         self.table_enable = False
         self.language = "en"
 
-        self.subtopic_extractor = GeminiTopicExtractor()
+        # Use our new flexible approach
+        self.subtopic_extractor = GeminiTopicExtractor(api_key=gemini_api_key, num_pages=10)
+        self.gemini_api_key = gemini_api_key or os.getenv("GEMINI_API_KEY", "")
 
     def cleanup_gpu(self):
         try:
@@ -462,42 +511,71 @@ class MineruNoTextProcessor:
     def process(self, pdf_path: str) -> str:
         logger.info(f"Processing PDF: {pdf_path}")
         try:
-            # 1) Extract subtopics
-            data = self.subtopic_extractor.extract_subtopics(pdf_path)
-            if not data or "2 Subject content and assessment information" not in data:
-                logger.warning("Gemini did not return '2 Subject content...' or data is empty.")
-                page_indices = None
-            else:
-                # 2) Flatten pages
-                page_indices = self._collect_page_indices(data["2 Subject content and assessment information"])
+            # 1) Extract subtopics from Gemini
+            subtopics = self.subtopic_extractor.extract_subtopics(pdf_path)
+            logger.info(f"Gemini returned subtopics: {subtopics}")
 
+            # 2) Read entire PDF
             with open(pdf_path, "rb") as f:
-                original_pdf_bytes = f.read()
-
-            # If no pages found => entire doc
-            if page_indices:
-                # Convert from 1-based => 0-based
-                doc = fitz.open(stream=original_pdf_bytes, filetype="pdf")
-                max_p = doc.page_count
-                doc.close()
-
-                zero_based = []
-                for p in page_indices:
-                    z = p - 1
-                    if 0 <= z < max_p:
-                        zero_based.append(z)
-                zero_based = sorted(set(zero_based))
-
-                if zero_based:
-                    logger.info(f"Subtopic pages (0-based): {zero_based}")
-                    subset_pdf_bytes = create_subset_pdf(original_pdf_bytes, zero_based)
-                else:
-                    logger.warning("No valid subtopic pages, using entire doc.")
-                    subset_pdf_bytes = original_pdf_bytes
-            else:
-                subset_pdf_bytes = original_pdf_bytes
+                pdf_bytes = f.read()
+            doc = fitz.open(stream=pdf_bytes, filetype="pdf")
+            total_pages = doc.page_count
+            doc.close()
 
-            # 3) doc_analyze with subset
+            final_pages = set()
+            if not subtopics:
+                logger.warning("No subtopics found. We'll process the entire PDF as fallback.")
+                final_pages = set(range(total_pages))
+            else:
+                # For each subtopic, find occurrence >= (start_p-1)
+                for subname, rng in subtopics.items():
+                    if not (isinstance(rng, list) and len(rng) == 2):
+                        logger.warning(f"Skipping subtopic '{subname}' => invalid range {rng}")
+                        continue
+                    start_p, end_p = rng
+                    if start_p > end_p:
+                        logger.warning(f"Skipping subtopic '{subname}' => start> end {rng}")
+                        continue
+
+                    # find occurrences
+                    occs = find_all_occurrences(pdf_bytes, subname)
+                    logger.info(f"Occurrences of subtopic '{subname}': {occs}")
+
+                    doc_start_0 = start_p - 1
+                    chosen_page = None
+                    for p in occs:
+                        if p >= doc_start_0:
+                            chosen_page = p
+                            break
+                    if chosen_page is None:
+                        # fallback to last or 0
+                        if occs:
+                            chosen_page = occs[-1]
+                            logger.warning(f"No occurrence >= {doc_start_0} for '{subname}'. Using last => {chosen_page}")
+                        else:
+                            chosen_page = 0
+                            logger.warning(f"No occurrences for '{subname}'. Using page 0.")
+
+                    raw_offset = chosen_page - doc_start_0
+                    offset = max(0, raw_offset)
+                    logger.info(f"Subtopic '{subname}': doc_start={start_p}, chosen_page={chosen_page}, raw_offset={raw_offset}, offset={offset}")
+
+                    s0 = (start_p - 1) + offset
+                    e0 = (end_p - 1) + offset
+                    s0 = max(0, min(total_pages - 1, s0))
+                    e0 = max(0, min(total_pages - 1, e0))
+                    for pp in range(s0, e0 + 1):
+                        final_pages.add(pp)
+
+            # 3) If final_pages is empty => fallback entire PDF
+            if not final_pages:
+                logger.warning("No valid pages after offset. We'll process entire PDF.")
+                final_pages = set(range(total_pages))
+
+            logger.info(f"Processing pages (0-based): {sorted(final_pages)}")
+            subset_pdf_bytes = create_subset_pdf(pdf_bytes, sorted(final_pages))
+
+            # 4) doc_analyze => concurrency => final MD
             dataset = PymuDocDataset(subset_pdf_bytes)
             inference = doc_analyze(
                 dataset,
@@ -509,44 +587,35 @@ class MineruNoTextProcessor:
             )
             logger.info("doc_analyze complete. Extracting images...")
 
-            # 4) Only images => table classification => final MD
-            image_writer = LocalImageWriter(self.output_folder)
-            pipe_result = inference.pipe_ocr_mode(image_writer, lang=self.language)
-
+            writer = LocalImageWriter(self.output_folder, self.gemini_api_key)
+            pipe_result = inference.pipe_ocr_mode(writer, lang=self.language)
             md_content = pipe_result.get_markdown("local-unique-prefix/")
-            final_markdown = image_writer.post_process("local-unique-prefix/", md_content)
 
-            # 5) Save final
-            md_path = os.path.join(self.output_folder, "final_output.md")
-            with open(md_path, "w", encoding="utf-8") as f:
+            final_markdown = writer.post_process("local-unique-prefix/", md_content)
+
+            # 5) Save
+            out_path = os.path.join(self.output_folder, "final_output.md")
+            with open(out_path, "w", encoding="utf-8") as f:
                 f.write(final_markdown)
 
-            logger.info(f"Markdown saved to: {md_path}")
+            logger.info(f"Markdown saved to: {out_path}")
             return final_markdown
 
         finally:
             self.cleanup_gpu()
 
-    def _collect_page_indices(self, subtopic_dict: Dict[str, List[int]]) -> List[int]:
-        """
-        {
-          "Paper 1 and Paper 2: Pure Mathematics": [11, 29],
-          "Paper 3: Statistics and Mechanics": [30, 42]
-        }
-        Return [11..29, 30..42] => a flattened list of pages
-        """
-        pages = []
-        for _, rng in subtopic_dict.items():
-            if isinstance(rng, list) and len(rng) == 2:
-                start_p, end_p = rng
-                # add all pages from start to end (inclusive)
-                for p in range(start_p, end_p + 1):
-                    pages.append(p)
-        return pages
-
+###############################################################################
+# Example Main
+###############################################################################
 if __name__ == "__main__":
-    input_pdf = "/home/user/app/input_output/a-level-pearson-mathematics-specification.pdf"
-    output_dir = "/home/user/app/input_output/output"
+    input_pdf = "/home/user/app/input_output/ocr-specification-economics.pdf"
+    output_dir = "/home/user/app/outputs"
+    gemini_key = os.getenv("GEMINI_API_KEY", "AIzaSyDtoakpXa2pjJwcQB6TJ5QaXHNSA5JxcrU")
 
-    processor = MineruNoTextProcessor(output_folder=output_dir)
-    final_md = processor.process(input_pdf)
\ No newline at end of file
+    try:
+        processor = MineruNoTextProcessor(output_folder=output_dir, gemini_api_key=gemini_key)
+        md_output = processor.process(input_pdf)
+        print("Final Markdown Output:")
+        print(md_output)
+    except Exception as e:
+        logger.error(f"Processing failed: {e}")
\ No newline at end of file