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updated README and notebook
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‎README.md

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@@ -39,11 +39,6 @@ DataFog can be installed via pip:
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pip install datafog
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```
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## Examples -
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### v3.2.0 NEW
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Based on the provided test cases, here's a suitable "Getting Started" section for the documentation:
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## Getting Started
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### Usage
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Here are some examples of how to use the DataFog library:
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The [Getting Started notebook](/datafog-python/examples/getting_started.ipynb) features a standalone Colab notebook that lets you get up and running in no time.
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#### Text PII Annotation
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```python
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from datafog import DataFog
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image_url = "https://example.com/image.png"
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image_url = "https://pbs.twimg.com/media/GM3-wpeWkAAP-cX.jpg"
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datafog = DataFog()
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annotated_text = await datafog.run_ocr_pipeline([image_url])
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print(annotated_text)
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Note: The DataFog library uses asynchronous programming, so make sure to use the `async`/`await` syntax when calling the appropriate methods.
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### v3.1.0
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### Base case: PII annotation of text-files
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```
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from datafog import OCRPIIAnnotator, TextPIIAnnotator
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import json
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import requests
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response = requests.get('https://gist.githubusercontent.com/sidmohan0/1aa3ec38b4e6594d3c34b113f2e0962d/raw/42e57146197be0f85a5901cd1dcdd9ad15b31bab/sotu_2023.txt')
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response.raise_for_status() # Ensure the request was successful
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text = response.text
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# print(text)
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text_annotator = TextPIIAnnotator()
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annotated_text = text_annotator.run(text, output_path=f"sotu_2023_output.json")
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print("Annotated Text:", annotated_text)
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```
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### OCR Reference Set (Images)
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```
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image_set = {
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"medical_invoice": "https://s3.amazonaws.com/thumbnails.venngage.com/template/dc377004-1c2d-49f2-8ddf-d63f11c8d9c2.png",
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"sales_receipt": "https://templates.invoicehome.com/sales-receipt-template-us-classic-white-750px.png",
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"press_release": "https://newsroom.cisco.com/c/dam/r/newsroom/en/us/assets/a/y2023/m09/cisco_splunk_1200x675_v3.png",
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"insurance_claim_scanned_form": "https://www.pdffiller.com/preview/101/35/101035394.png",
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"scanned_internal_record": "https://www.pdffiller.com/preview/435/972/435972694.png",
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"executive_email": "https://pbs.twimg.com/media/GM3-wpeWkAAP-cX.jpg"
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}
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```
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### OCR text extraction from images + PII annotation
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with this, you can then run the following steps:
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```
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from datafog import OCRPIIAnnotator, TextPIIAnnotator
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import json
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image_url = image_set["executive_email"]
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annotator = OCRPIIAnnotator()
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annotated_text = annotator.run(image_url, output_path=f"executive_email_output.json")
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print("Annotated Text:", annotated_text)
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```
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and the output should look like this:
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```
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Annotated Text: {'DATE_TIME': ['Wednesday', 'June 12, 2019'], 'LOC': [], 'NRP': [], 'ORG': [], 'PER': ['Kevin Scott Sent', 'Satya Nadella', 'Bill Gates Subject', 'Thoughts']}
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```
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### With PySpark
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Note: as of 3.1.0, you'll need to start the Spark session by instancing the DataFog class as shown below
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```
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from datafog import DataFog
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from datafog.pii_annotation import ImageProcessor
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datafog = DataFog()
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# let's process the images that we shared above
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processed_images = [(name, ImageProcessor().download_image(url=image_url)) for name, image_url in image_set.items()]
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from datafog.pii_annotation import SparkService
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parsed_images = [(name, ImageProcessor().parse_image(img)) for name, img in processed_images]
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df = SparkService().spark.createDataFrame(parsed_images, ["image_name", "parsed_data"])
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# Display DataFrame
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df.show(truncate=False)
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```
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## Contributing
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‎examples/getting_started.ipynb

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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting datafog==3.2.0b20\n",
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" Downloading datafog-3.2.0b20.tar.gz (15 kB)\n",
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" Installing build dependencies ... \u001b[?25ldone\n",
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"\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: nest_asyncio in /Users/sidmohan/.pyenv/versions/3.11.7/envs/2.2.0b1/lib/python3.11/site-packages (1.6.0)\n",
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"!pip install nest_asyncio"
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]

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