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--- /dev/null
+++ b/topic_extr.py
@@ -0,0 +1,663 @@
+#!/usr/bin/env python3
+import os
+import re
+import gc
+import json
+import logging
+import fitz
+import base64
+import cv2
+import numpy as np
+from io import BytesIO
+from typing import List, Dict, Any
+
+import torch
+
+# Try to import 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 Subset Creation
+###############################################################################
+def create_subset_pdf(original_pdf_bytes: bytes, page_indices: List[int]) -> bytes:
+    """
+    Creates a new PDF (in memory) containing only the pages in page_indices (0-based).
+    """
+    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()
+    sorted_pages = sorted(set(page_indices))
+    for p in sorted_pages:
+        if 0 <= p < doc.page_count:
+            new_doc.insert_pdf(doc, from_page=p, to_page=p)
+        else:
+            logger.error(f"Page index {p} is out of range (0..{doc.page_count - 1}).")
+            raise ValueError(f"Page index {p} is out of range.")
+    subset_bytes = new_doc.tobytes()
+    new_doc.close()
+    doc.close()
+    return subset_bytes
+
+###############################################################################
+# Utility: Shrink Images Before Sending to Gemini
+###############################################################################
+def shrink_image_to_jpeg(image_data: bytes, max_dim: int = 800, jpeg_quality: int = 80) -> bytes:
+    """
+    Decode image_data, resize so largest dimension <= max_dim, then re-encode as JPEG.
+    This reduces request size to Gemini significantly.
+    """
+    try:
+        # Decode
+        arr = np.frombuffer(image_data, np.uint8)
+        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+        if img is None:
+            # Not a valid image, return as is
+            return image_data
+
+        h, w, _ = img.shape
+        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)
+
+        # Re-encode
+        encode_params = [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality]
+        success, enc = cv2.imencode(".jpg", img, encode_params)
+        if success:
+            return enc.tobytes()
+        else:
+            logger.warning("Could not encode resized image, returning original.")
+            return image_data
+    except Exception as e:
+        logger.warning(f"shrink_image_to_jpeg error: {e}. Returning original data.")
+        return image_data
+
+###############################################################################
+# Gemini LLM - Subtopic Extraction
+###############################################################################
+class GeminiTopicExtractor:
+    """
+    Reads the first few pages of a PDF to get the table of contents text,
+    then uses Gemini to parse out topics -> [start_page, end_page].
+    """
+    def __init__(self, api_key: str = None, num_pages: int = 14):
+        self.api_key = api_key or os.getenv("GEMINI_API_KEY", "")
+        if not self.api_key:
+            logger.warning("No Gemini API key provided for subtopic extraction.")
+        self.num_pages = num_pages
+
+    def extract_subtopics(self, pdf_path: str) -> Dict[str, Any]:
+        text_content = self._read_first_pages(pdf_path, self.num_pages)
+        if not text_content.strip():
+            logger.error("No text extracted from the first pages of the PDF.")
+            return {}
+
+        if genai is None or types is None:
+            logger.warning("google.genai is not installed. Returning empty subtopics.")
+            return {}
+
+        prompt = f"""
+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:
+    Instructions:
+    1. Identify the 'Contents' section listing all topics, subtopics, and their corresponding pages.
+    2. Extract only the **highest-level, subject-related subtopics** (ignore administrative sections).
+    3. For each subtopic, return [start_page, end_page] (1-based).
+    4. Output valid JSON in the following format:
+        {{
+        "Topic A": [start_page, end_page],
+        "Topic B": [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 -1 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}
+"""
+
+        try:
+            logger.debug("Calling Gemini to extract subtopics...")
+            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)
+            )
+            # Log partial or full LLM response for debugging
+            if response and response.text:
+                logger.info(f"[Gemini subtopic extraction] Raw LLM response:\n{response.text}")
+
+            raw_text = response.text.strip() if (response and response.text) else "{}"
+            cleaned = raw_text.replace("```json", "").replace("```", "")
+            data = json.loads(cleaned)
+            return data
+        except Exception as e:
+            logger.error(f"Gemini subtopic extraction error: {e}")
+            return {}
+
+    def _read_first_pages(self, pdf_path: str, num_pages: int) -> str:
+        text_parts = []
+        try:
+            doc = fitz.open(pdf_path)
+            pages_to_read = min(doc.page_count, num_pages)
+            for p in range(pages_to_read):
+                page_text = doc.load_page(p).get_text()
+                text_parts.append(page_text)
+            doc.close()
+        except Exception as e:
+            logger.error(f"Could not open/read PDF: {e}")
+        return "\n".join(text_parts)
+
+###############################################################################
+# Gemini-based Image 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 (Flash).
+    We shrink the image first to speed up requests.
+    """
+    if not api_key:
+        logger.warning("No Gemini API key found, returning NO_TABLE.")
+        return "NO_TABLE"
+    if not genai or not types:
+        logger.warning("google.genai not installed, returning NO_TABLE.")
+        return "NO_TABLE"
+
+    # Shrink image
+    shrunk_data = shrink_image_to_jpeg(image_data, max_dim=800, jpeg_quality=80)
+
+    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
+"""
+    try:
+        # Example of optional manual timeout approach (commented out):
+        # import signal
+        # def handler(signum, frame):
+        #     raise TimeoutError("Table classification timed out!")
+        # signal.signal(signal.SIGALRM, handler)
+        # signal.alarm(30)  # 30s timeout
+
+        logger.debug("Sending image to Gemini for table classification...")
+        client = genai.Client(api_key=api_key)
+        response = client.models.generate_content(
+            model="gemini-2.0-flash",
+            contents=[
+                {
+                    "parts": [
+                        {"text": prompt},
+                        {
+                            "inline_data": {
+                                "mime_type": "image/jpeg",
+                                "data": base64.b64encode(shrunk_data).decode('utf-8')
+                            }
+                        }
+                    ]
+                }
+            ],
+            config=types.GenerateContentConfig(temperature=0.0)
+        )
+        # signal.alarm(0)  # cancel timeout
+
+        if response and response.text:
+            logger.info(f"[Gemini table classification] LLM raw response:\n{response.text}")
+
+        classification = (response.text.strip().upper()
+                          if (response and response.text) else "NO_TABLE")
+        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"Gemini table classification error: {e}")
+        return "NO_TABLE"
+
+def call_gemini_for_image_description(image_data: bytes, api_key: str) -> str:
+    """
+    Use Gemini (Flash) to extract a short description from an image.
+    We also shrink the image first to reduce request time.
+    """
+    if not api_key:
+        logger.warning("No Gemini API key found, returning fallback description.")
+        return "Image description unavailable"
+    if not genai or not types:
+        logger.warning("google.genai not installed, returning fallback description.")
+        return "Image description unavailable"
+
+    shrunk_data = shrink_image_to_jpeg(image_data, max_dim=800, jpeg_quality=80)
+
+    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:
+        logger.debug("Sending image to Gemini for description...")
+        client = genai.Client(api_key=api_key)
+        response = client.models.generate_content(
+            model="gemini-2.0-flash",
+            contents=[
+                {
+                    "parts": [
+                        {"text": prompt_text},
+                        {
+                            "inline_data": {
+                                "mime_type": "image/jpeg",
+                                "data": base64.b64encode(shrunk_data).decode('utf-8')
+                            }
+                        }
+                    ]
+                }
+            ],
+            config=types.GenerateContentConfig(temperature=0.0)
+        )
+        if response and response.text:
+            logger.info(f"[Gemini image description] LLM raw response:\n{response.text}")
+
+        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"
+
+###############################################################################
+# Local Image Writer (Sequential Gemini Calls)
+###############################################################################
+class LocalImageWriter:
+    """
+    Saves extracted images, classifies them with Gemini for table/no-table, 
+    describes them if no-table, then modifies the Markdown to replace 
+    the original references with final alt text. Also processes table images 
+    into row/column cell images.
+    """
+    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:
+        """
+        Called by magic-pdf to save each extracted image. 
+        We store metadata so we can classify the images later.
+        """
+        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,
+            "table_classification": "NO_TABLE",
+            "final_alt": ""
+        }
+
+    def post_process(self, key: str, md_content: str) -> str:
+        """
+        1) Classify images as table/no-table (sequential).
+        2) Describe non-table images (sequential).
+        3) Replace placeholders in the Markdown with final alt text.
+        4) Process table images => row/col cell images => update Markdown.
+        5) Keep only image-reference lines in the final Markdown.
+        """
+        # 1) Table classification
+        logger.info("Classifying images to detect tables (sequential)...")
+        for p, info in self.descriptions.items():
+            classification = call_gemini_for_table_classification(info["data"], self.gemini_api_key)
+            self.descriptions[p]['table_classification'] = classification
+
+        # 2) Image description for non-table
+        logger.info("Generating image descriptions for non-table images (sequential)...")
+        for p, info in self.descriptions.items():
+            if info['table_classification'] == "NO_TABLE":
+                desc = call_gemini_for_image_description(info["data"], self.gemini_api_key)
+                info['final_alt'] = desc
+
+        # For images classified as 2/3-column tables => set alt
+        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"
+
+        # 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) Process table images => row/col
+        md_content = self._process_table_images_in_markdown(md_content)
+
+        # 5) Keep only image-reference lines
+        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:
+        """
+        For images flagged as 2/3-column tables, run TableExtractor, 
+        split into row/column cell images, and replace the single 
+        table image reference with multiple cell references.
+        """
+        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"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_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)
+                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
+
+###############################################################################
+# Mineru (magic-pdf) Pipeline with Page-Range Preprocessing
+###############################################################################
+class MineruNoTextProcessor:
+    """
+    1) Extracts page ranges from the PDF's table of contents (via Gemini).
+    2) Creates a subset PDF in memory for those pages.
+    3) Runs magic-pdf analysis on the subset PDF.
+    4) Generates a Markdown file with images, including table images 
+       split into row/column cells.
+    """
+    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
+        # keep table_enable=False so that entire table is an image
+        self.table_enable = False
+        self.language = "en"
+
+        self.subtopic_extractor = GeminiTopicExtractor(api_key=gemini_api_key, num_pages=4)
+        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 the PDF's contents
+            topics_data = self.subtopic_extractor.extract_subtopics(pdf_path)
+            if not topics_data:
+                raise ValueError("No valid topics extracted from the PDF's table of contents.")
+
+            # 2) Flatten page indices from all topics (1-based)
+            page_indices = self._collect_page_indices(topics_data)
+            if not page_indices:
+                raise ValueError("Extracted page indices are empty.")
+
+            # 3) Read the original PDF into memory
+            with open(pdf_path, "rb") as f:
+                original_pdf_bytes = f.read()
+
+            # 4) Validate pages and create subset (convert 1-based to 0-based)
+            doc = fitz.open(stream=original_pdf_bytes, filetype="pdf")
+            total_pages = doc.page_count
+            doc.close()
+
+            zero_based = []
+            for p in page_indices:
+                z = p - 1
+                if 0 <= z < total_pages:
+                    zero_based.append(z)
+                else:
+                    logger.error(f"Page {p} (converted to {z}) is out of 1..{total_pages}")
+                    raise ValueError(f"Page {p} is out of valid range.")
+            zero_based = sorted(set(zero_based))
+            if not zero_based:
+                raise ValueError("No valid pages after conversion to 0-based indices.")
+
+            logger.info(f"Processing pages (0-based): {zero_based}")
+            subset_pdf_bytes = create_subset_pdf(original_pdf_bytes, zero_based)
+
+            # 5) Run magic-pdf analysis on the subset PDF
+            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...")
+
+            # 6) Convert to Markdown (images only) via pipe_ocr_mode
+            image_writer = LocalImageWriter(self.output_folder, gemini_api_key=self.gemini_api_key)
+            pipe_result = inference.pipe_ocr_mode(image_writer, lang=self.language)
+            md_content = pipe_result.get_markdown("local-unique-prefix/")
+
+            # 7) Post-process => classify table images => final MD
+            final_markdown = image_writer.post_process("local-unique-prefix/", md_content)
+
+            # 8) Save final Markdown
+            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()
+
+    def _collect_page_indices(self, topics_data: Dict[str, Any]) -> List[int]:
+        """
+        Flatten the subtopic ranges into a list of pages (1-based).
+        Example: {"Topic A": [11,29], "Topic B": [30,42]} => [11..29, 30..42]
+        """
+        pages = []
+        for topic, rng in topics_data.items():
+            if isinstance(rng, list) and len(rng) == 2:
+                start_p, end_p = rng
+                if start_p > end_p:
+                    logger.error(f"Invalid page range for topic '{topic}': {rng}")
+                    raise ValueError(f"Invalid page range for topic '{topic}': {rng}")
+                pages.extend(range(start_p, end_p + 1))
+            else:
+                logger.warning(f"Skipping topic '{topic}' with invalid range: {rng}")
+        return pages
+
+###############################################################################
+# Main Execution
+###############################################################################
+if __name__ == "__main__":
+    # Example usage:
+    input_pdf = "/home/user/app/input_output/a-level-pearson-mathematics-specification.pdf"
+    output_dir = "/home/user/app/input_output/output"
+
+    # Provide your Gemini API key (or rely on GEMINI_API_KEY env var).
+    gemini_key = os.getenv("GEMINI_API_KEY", "AIzaSyDtoakpXa2pjJwcQB6TJ5QaXHNSA5JxcrU")
+    # gemini_key = "YOUR_GEMINI_API_KEY"
+
+    try:
+        processor = MineruNoTextProcessor(output_folder=output_dir, gemini_api_key=gemini_key)
+        final_md = processor.process(input_pdf)
+        print("Final Markdown Output:")
+        print(final_md)
+    except Exception as e:
+        logger.error(f"Processing failed: {e}")
\ No newline at end of file