by Lazy Sloth
Scrape Data from PDF using Python & Flask
from flask import Flask, request, render_template, jsonify
import PyPDF2
import io
from gunicorn.app.base import BaseApplication
from logging_config import configure_logging
app = Flask(__name__)
@app.route("/")
def root_route():
return render_template('upload.html')
@app.route("/upload", methods=["POST"])
def upload_pdf():
if 'pdf_file' not in request.files:
return "No file part"
file = request.files['pdf_file']
if file.filename == '':
return "No selected file"
if file and allowed_file(file.filename):
pdfReader = PyPDF2.PdfReader(io.BytesIO(file.read()))
text = ""
for page in pdfReader.pages:
Frequently Asked Questions
How can this PDF scraper tool benefit businesses?
The PDF scraper tool built with Python and Flask can significantly benefit businesses by automating the extraction of text from PDF documents. This can save time and reduce errors in data entry processes, especially for companies that deal with large volumes of PDF files. For example, legal firms could use this tool to quickly extract key information from contracts, or financial institutions could use it to process PDF invoices or statements.
What industries might find this PDF scraper particularly useful?
Several industries can benefit from this PDF scraper tool: - Legal: For extracting information from contracts and legal documents - Finance: For processing financial statements and reports - Healthcare: For extracting patient information from medical records - Human Resources: For processing resumes and employee documents - Research: For extracting data from academic papers and reports
How can I customize the PDF scraper to extract specific types of data?
To customize the PDF scraper to extract specific types of data, you can modify the upload_pdf()
function in the main.py
file. For example, if you want to extract only email addresses, you could use regular expressions:
```python import re
@app.route("/upload", methods=["POST"]) def upload_pdf(): # ... existing code ... text = "" for page in pdfReader.pages: text += page.extract_text()
# Extract email addresses
email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
emails = re.findall(email_pattern, text)
return render_template('template.html', extracted_text=emails)
```
This modification would extract only email addresses from the PDF and display them on the webpage.
Can this PDF scraper tool be integrated into existing business systems?
Yes, the PDF scraper tool can be integrated into existing business systems. Since it's built with Flask, it can be easily deployed as a web service and accessed via HTTP requests. This means other applications in your business ecosystem can send PDF files to this service and receive the extracted text in response. This flexibility allows for seamless integration with various business processes and workflows.
How can I improve the performance of the PDF scraper for large files?
To improve performance for large PDF files, you can implement asynchronous processing and caching. Here's an example of how you could modify the upload_pdf()
function to process files asynchronously:
```python from flask import Flask, request, render_template, jsonify from celery import Celery import PyPDF2 import io
app = Flask(name) celery = Celery(app.name, broker='redis://localhost:6379/0')
@celery.task def process_pdf(file_content): pdfReader = PyPDF2.PdfReader(io.BytesIO(file_content)) text = "" for page in pdfReader.pages: text += page.extract_text() return text
@app.route("/upload", methods=["POST"]) def upload_pdf(): if 'pdf_file' not in request.files: return "No file part" file = request.files['pdf_file'] if file.filename == '': return "No selected file" if file and allowed_file(file.filename): task = process_pdf.delay(file.read()) return jsonify({"task_id": task.id}), 202 return "File not allowed" ```
This modification uses Celery to process the PDF asynchronously, which can significantly improve performance for large files in the PDF scraper tool.
Created: | Last Updated:
Introduction to the PDF Data Extraction Template
Welcome to the Lazy template guide for extracting data from PDF files. This template is designed to help you build an application that allows users to upload PDF files and then displays the extracted text on a webpage. This is particularly useful for those looking to automate the process of data retrieval from PDF documents without delving into the complexities of programming or deployment.
Getting Started
To begin using this template, simply click on "Start with this Template" on the Lazy platform. This will pre-populate the code in the Lazy Builder interface, so you won't need to copy, paste, or delete any code manually.
Test: Deploying the App
Once you have initiated the template, press the "Test" button. This will begin the deployment of your application on the Lazy platform. The Lazy CLI will handle the deployment process, and you won't need to worry about installing libraries or setting up your environment.
Using the App
After the deployment is complete, you will receive a dedicated server link. This link will take you to the web interface where you can upload a PDF file. Here's how to use the interface:
- Visit the provided server link to access the upload page.
- Click on the "Choose File" button to select the PDF file you wish to extract data from.
- After selecting the file, click on the "Submit" button to upload the file and process it.
- The application will then display the extracted text on a new webpage.
Integrating the App
If you wish to integrate this PDF data extraction functionality into another service or frontend, you can use the server link provided by Lazy. For instance, you could embed the link in an iframe within your existing web application or use it as part of a larger workflow that requires PDF data extraction.
Here is a sample code snippet that you could use to integrate the upload form into another HTML page:
<iframe src="YOUR_LAZY_SERVER_LINK/upload.html" width="100%" height="500"></iframe>
Replace "YOUR_LAZY_SERVER_LINK" with the actual link provided by Lazy.
Remember, this template is designed to work seamlessly within the Lazy platform, so all the heavy lifting of deployment and environment configuration is taken care of for you. Enjoy building your PDF data extraction tool with ease!
Here are 5 key business benefits for this PDF data extraction template:
Template Benefits
-
Automated Data Extraction: Quickly extract text from PDF documents, saving time and reducing manual data entry errors for businesses dealing with large volumes of PDF files.
-
Improved Document Processing: Streamline document workflows by automatically converting PDF content into searchable, editable text, enhancing information retrieval and analysis capabilities.
-
Cost-Effective Solution: Provide a lightweight, open-source alternative to expensive commercial PDF processing software, making it accessible for small to medium-sized businesses.
-
Enhanced Compliance: Facilitate easier compliance with data management regulations by making PDF content more accessible and easier to audit or search for specific information.
-
Increased Productivity: Enable employees to quickly access and utilize information from PDF documents, improving decision-making processes and overall workplace efficiency.