diff --git a/pdf-blueprint.ipynb b/pdf-blueprint.ipynb
index e021b8d..fc556ef 100644
--- a/pdf-blueprint.ipynb
+++ b/pdf-blueprint.ipynb
@@ -1333,6 +1333,35 @@
"display_image(generated_metadata, 0) # Display the first image"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualize metadata using Gradio application"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Requirements to launch the application\n",
+ "! pip install gradio gradio-pdf langchain-core PyMuPDF azure-storage-blob"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pdf_extraction_utils.app import PdfDemo\n",
+ "\n",
+ "demo = PdfDemo(client)\n",
+ "demo.create_gradio_app()"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/pdf_extraction_utils/__init__.py b/pdf_extraction_utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pdf_extraction_utils/app.py b/pdf_extraction_utils/app.py
new file mode 100644
index 0000000..381d0e9
--- /dev/null
+++ b/pdf_extraction_utils/app.py
@@ -0,0 +1,327 @@
+import gradio as gr
+import time
+import json
+import os
+import fitz # PyMuPDF
+import base64
+import io
+
+from PIL import Image
+from pathlib import Path
+from gradio_pdf import PDF
+from nv_ingest_client.client import NvIngestClient
+from nv_ingest_client.primitives import JobSpec
+from nv_ingest_client.primitives.tasks import ExtractTask
+from nv_ingest_client.util.file_processing.extract import extract_file_content
+from langchain_core.documents import Document
+
+
+def highlight_text(pdf_path, highlights, image_coordinates):
+ """
+ Highlights text and image bounding boxes in a PDF and creates a new PDF.
+
+ Args:
+ pdf_path (str): Path to the original PDF.
+ highlights (dict): Dictionary containing page numbers as keys and bounding boxes for text as values.
+ image_coordinates (dict): Dictionary containing page numbers as keys and bounding boxes for images as values.
+
+ Returns:
+ str: Path to the new highlighted PDF.
+ """
+ # Open the original PDF
+ pdf_document = fitz.open(pdf_path)
+ output_path = "highlighted_output.pdf"
+ new_pdf_document = fitz.open()
+
+ # Process each page in the original PDF
+ for page_num in range(len(pdf_document)):
+ page = pdf_document.load_page(page_num)
+ new_page = new_pdf_document.new_page(width=page.rect.width, height=page.rect.height)
+
+ # Draw the original page content
+ new_page.show_pdf_page(page.rect, pdf_document, page_num)
+ pdf_height = new_page.rect.height
+
+ # Draw text highlights
+ for bbox in highlights.get(page_num, []):
+ # Highlight with a yellow rectangle
+ new_page.draw_rect(bbox, color=(1, 1, 0), width=2)
+
+ # Draw image bounding boxes
+ if page_num in image_coordinates:
+ for img_bbox in image_coordinates[page_num]:
+ # Bounding box coordinates: [x0, y0, x1, y1]
+ x0, y0, x1, y1 = img_bbox
+ adjusted_y0 = pdf_height - y1 # Adjust for PDF coordinate system
+ adjusted_y1 = pdf_height - y0
+ # Draw a red rectangle around the image
+ new_page.draw_rect([x0, adjusted_y0, x1, adjusted_y1], color=(1, 0, 0), width=2)
+
+ # Save and close the new PDF
+ new_pdf_document.save(output_path)
+ new_pdf_document.close()
+ pdf_document.close()
+
+ return output_path
+
+def get_text_coordinates(pdf_path, search_text):
+ """
+ Searches for multi-line text in a PDF and returns coordinates for each line.
+
+ Args:
+ pdf_path (str): Path to the PDF file.
+ search_text (str): Text to search for in the PDF.
+
+ Returns:
+ dict: Dictionary containing page numbers as keys and lists of bounding boxes as values.
+ """
+ doc = fitz.open(pdf_path)
+ text_coordinates = {}
+ search_lines = search_text.splitlines()
+
+ for page_num in range(len(doc)):
+ page = doc.load_page(page_num)
+ text_coordinates[page_num] = []
+
+ for search_line in search_lines:
+ # Search for the exact match of the line
+ bbox_list = page.search_for(search_line.strip())
+ if bbox_list:
+ text_coordinates[page_num].extend(bbox_list)
+
+ doc.close()
+ return text_coordinates
+
+def base64_to_image(base64_str):
+ """
+ Converts a base64 string to a PIL Image.
+
+ Args:
+ base64_str (str): Base64-encoded string of an image.
+
+ Returns:
+ PIL.Image.Image: Decoded and resized PIL Image.
+ """
+ # Remove the base64 prefix if present
+ if base64_str.startswith('data:image/'):
+ base64_str = base64_str.split(',')[1]
+
+ # Decode the base64 string
+ image_data = base64.b64decode(base64_str)
+
+ # Load the image from the decoded bytes
+ image = Image.open(io.BytesIO(image_data))
+ # Resize the image for display purposes
+ image = image.resize((800, 600)) # Adjust size as needed
+ return image
+
+class PdfDemo:
+ def __init__(self, client):
+ self.client = client
+
+ def process_pdf_file(self, file_path):
+ start_time = time.time()
+
+ file_content, file_type = extract_file_content(file_path)
+
+ job_spec = JobSpec(
+ document_type=file_type,
+ payload=file_content,
+ source_id=file_path,
+ source_name=file_path,
+ extended_options={"tracing_options": {"trace": True, "ts_send": time.time_ns()}},
+ )
+
+ extract_task = ExtractTask(
+ document_type=file_type,
+ extract_text=True,
+ extract_images=True,
+ extract_tables=True,
+ )
+
+ job_spec.add_task(extract_task)
+ job_id = self.client.add_job(job_spec)
+
+ self.client.submit_job(job_id, "morpheus_task_queue")
+ result = self.client.fetch_job_result(job_id, timeout=60)
+
+ # Compute the time taken to process the PDF file
+ end_time = time.time()
+ elapsed_time = end_time - start_time
+ elapsed_time_rounded = round(elapsed_time, 2)
+
+ return result, elapsed_time_rounded
+
+ def extracted_multimodal_data(self, file_path):
+ result, time_taken = self.process_pdf_file(file_path)
+ table_chart_content = []
+ image_base64_list = []
+ image_content_location_by_page = {}
+ full_text_content = ""
+
+ for element in result[0]:
+ if element['document_type'] == 'text':
+ document = Document(element['metadata']['content'])
+ full_text_content += document.page_content
+ elif element['document_type'] == 'structured':
+ table_content = Document(element['metadata']['table_metadata']['table_content'])
+ table_chart_content.append(table_content)
+ elif element['document_type'] == 'image':
+ image_data = Document(element['metadata']['content']).page_content
+ image_base64_list.append(image_data)
+ page_number = element['metadata']['content_metadata']['hierarchy']['page']
+ image_location = element['metadata']['image_metadata']['image_location']
+ image_content_location_by_page.setdefault(page_number, []).append(image_location)
+
+ json_data = json.dumps(result)
+
+ return (
+ full_text_content,
+ table_chart_content,
+ image_base64_list,
+ image_content_location_by_page,
+ json_data,
+ time_taken,
+ )
+
+ def fetch_predefined_files_from_local(self):
+ """
+ Fetch predefined PDF files from a local folder.
+
+ Args:
+ folder_path (str): Path to the folder containing predefined PDF files.
+
+ Returns:
+ dict: A dictionary mapping file names to file paths.
+ """
+ predefined_files = {}
+ script_dir = os.path.dirname(os.path.abspath(__file__))
+ folder_path = os.path.join(script_dir, "pdf_examples")
+ folder = Path(folder_path) # Create a Path object from the folder path
+ if folder.exists() and folder.is_dir():
+ for pdf_file in folder.glob("*.pdf"):
+ predefined_files[pdf_file.stem] = str(pdf_file)
+ return predefined_files
+
+ def run_analysis(self, file, highlight):
+ (
+ text,
+ table_chart,
+ image_base64_list,
+ image_content_location,
+ json_data,
+ time_taken_for_processing,
+ ) = self.extracted_multimodal_data(file)
+
+ start_time = time.time()
+ coordinates = get_text_coordinates(file, text)
+
+ # Convert base64 strings to PIL images
+ images = [base64_to_image(base64_str) for base64_str in image_base64_list]
+
+ if highlight:
+ highlighted_pdf_path = highlight_text(file, coordinates, image_content_location)
+ end_time = time.time()
+ elapsed_time_highlighting = end_time - start_time
+ elapsed_time_rounded_highlighting = round(elapsed_time_highlighting, 2)
+ else:
+ highlighted_pdf_path = file
+ elapsed_time_rounded_highlighting = 0
+
+ return (
+ highlighted_pdf_path,
+ text,
+ table_chart,
+ images,
+ json_data,
+ f"**Time taken for extracting:** {time_taken_for_processing:.2f} (s)",
+ f"**Time taken for highlighting:** {elapsed_time_rounded_highlighting:.2f} (s)"
+ )
+
+ def create_gradio_app(self):
+ """
+ Creates and launches the Gradio app for multimodal data extraction.
+ """
+
+ css = "styles.css"
+ predefined_files = self.fetch_predefined_files_from_local()
+ with gr.Blocks(css=css) as demo:
+ gr.Markdown(
+ """
+ <div style="font-size: 36px; font-weight: bold; color: #76B900; text-align: center; margin-bottom: 20px;">
+ NVIDIA-INGEST: MULTI-MODAL DATA EXTRACTION
+ </div>
+ """
+ )
+ with gr.Row():
+ with gr.Column(scale=1):
+ file_input = gr.File(
+ label="Upload PDF File", elem_id="small-file-input", interactive=True
+ )
+ gr.Markdown(
+ """
+ <div style="font-size: 24px; text-align: center;">
+ Example pdfs
+ </div>
+ """
+ )
+
+ btn_actions = []
+ for file_name, file_path in predefined_files.items():
+ btn = gr.Button(file_name, variant="secondary")
+ btn_actions.append((btn, file_path))
+
+ with gr.Column(scale=4):
+ bt_analysis = gr.Button("Run analysis")
+ with gr.Row():
+ time_taken_for_processing = gr.Markdown()
+ time_taken_for_highlighting = gr.Markdown()
+ bounding_box_toggle = gr.Checkbox(label="Highlight Extracted Data")
+ pdf_viewer = PDF(
+ label="Extract Multimodal Data", interactive=True, elem_id="pdf_viewer"
+ )
+
+ with gr.Column(scale=4):
+ with gr.Tab("Content"):
+ text_content = gr.Textbox(label="Text", value="")
+ table_chart_content = gr.Textbox(label="Tables", value="")
+ image_gallery = gr.Gallery(
+ label="Charts & Images",
+ elem_id="gallery",
+ columns=[3],
+ rows=[1],
+ object_fit="contain",
+ height="auto",
+ )
+ with gr.Tab("Result"):
+ json_display = gr.JSON()
+
+ # Link the file input to the PDF viewer
+ file_input.change(
+ lambda file: file.name if file else None,
+ inputs=file_input,
+ outputs=pdf_viewer,
+ )
+
+ for btn, file_path in btn_actions:
+ btn.click(
+ lambda file_path=file_path: file_path,
+ inputs=None,
+ outputs=pdf_viewer,
+ )
+
+ bt_analysis.click(
+ self.run_analysis,
+ inputs=[pdf_viewer, bounding_box_toggle],
+ outputs=[
+ pdf_viewer,
+ text_content,
+ table_chart_content,
+ image_gallery,
+ json_display,
+ time_taken_for_processing,
+ time_taken_for_highlighting,
+ ],
+ )
+
+ demo.launch(share=True)
diff --git a/pdf_extraction_utils/pdf_examples/Alphabet.pdf b/pdf_extraction_utils/pdf_examples/Alphabet.pdf
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diff --git a/pdf_extraction_utils/pdf_examples/Apple.pdf b/pdf_extraction_utils/pdf_examples/Apple.pdf
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diff --git a/pdf_extraction_utils/pdf_examples/Meta.pdf b/pdf_extraction_utils/pdf_examples/Meta.pdf
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diff --git a/pdf_extraction_utils/pdf_examples/NVIDIA.pdf b/pdf_extraction_utils/pdf_examples/NVIDIA.pdf
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diff --git a/pdf_extraction_utils/pdf_examples/Test 1.pdf b/pdf_extraction_utils/pdf_examples/Test 1.pdf
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diff --git a/pdf_extraction_utils/pdf_examples/Test 2.pdf b/pdf_extraction_utils/pdf_examples/Test 2.pdf
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