How to optimize PDF from URL asynchronously for PDF optimization API in Python using PDF.co Web API

Follow this simple tutorial to learn optimize PDF from URL asynchronously to have PDF optimization API in Python

Check these thousands of pre-made source code samples for simple implementation in your own programming projects. PDF optimization API in Python can be applied with PDF.co Web API. PDF.co Web API is the Web API with a set of tools for documents manipulation, data conversion, data extraction, splitting and merging of documents. Includes image recognition, built-in OCR, barcode generation and barcode decoders to decode bar codes from scans, pictures and pdf.

If you want to speed up the application’s code writing then Python code samples for Python developers help to implement using PDF.co Web API. This Python sample code can be used by copying and pasting into your project. Once done,just compile your project and click Run. Further improvement of the code will make it more robust.

PDF.co Web API – free trial version is available on our website. Also, there are other code samples to help you with your Python application included into trial version.

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OptimizePdfFromUrlAsynchronously.py
      
""" Cloud API asynchronous "PDF To Text" job example. Allows to avoid timeout errors when processing huge or scanned PDF documents. """ import os import requests # pip install requests import time import datetime # The authentication key (API Key). # Get your own by registering at https://app.pdf.co/documentation/api API_KEY = "******************************************" # Base URL for PDF.co Web API requests BASE_URL = "https://api.pdf.co/v1" # Direct URL of source PDF file. SourceFileURL = "https://bytescout-com.s3.amazonaws.com/files/demo-files/cloud-api/pdf-optimize/sample.pdf" # PDF document password. Leave empty for unprotected documents. Password = "" # Destination PDF file name DestinationFile = ".\\result.pdf" # (!) Make asynchronous job Async = True def main(args = None): optimizePDF(SourceFileURL, DestinationFile) def optimizePDF(uploadedFileUrl, destinationFile): """Optimize PDF using PDF.co Web API""" # Prepare URL for 'Optimize PDF' API request url = "{}/pdf/optimize?async={}&name={}&password={}&url={}".format( BASE_URL, Async, os.path.basename(destinationFile), Password, uploadedFileUrl ) # Execute request and get response as JSON response = requests.get(url, headers={ "x-api-key": API_KEY, "content-type": "application/octet-stream" }) if (response.status_code == 200): json = response.json() if json["error"] == False: # Asynchronous job ID jobId = json["jobId"] # URL of the result file resultFileUrl = json["url"] # Check the job status in a loop. # If you don't want to pause the main thread you can rework the code # to use a separate thread for the status checking and completion. while True: status = checkJobStatus(jobId) # Possible statuses: "working", "failed", "aborted", "success". # Display timestamp and status (for demo purposes) print(datetime.datetime.now().strftime("%H:%M.%S") + ": " + status) if status == "success": # Download result file r = requests.get(resultFileUrl, stream=True) if (r.status_code == 200): with open(destinationFile, 'wb') as file: for chunk in r: file.write(chunk) print(f"Result file saved as \"{destinationFile}\" file.") else: print(f"Request error: {response.status_code} {response.reason}") break elif status == "working": # Pause for a few seconds time.sleep(3) else: print(status) break else: # Show service reported error print(json["message"]) else: print(f"Request error: {response.status_code} {response.reason}") def checkJobStatus(jobId): """Checks server job status""" url = f"{BASE_URL}/job/check?jobid={jobId}" response = requests.get(url, headers={ "x-api-key": API_KEY }) if (response.status_code == 200): json = response.json() return json["status"] else: print(f"Request error: {response.status_code} {response.reason}") return None if __name__ == '__main__': main()

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