README.md

metadata

title: Ocr Llm Test
emoji: 🌍
colorFrom: pink
colorTo: gray
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
short_description: Technical Assessment

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

OCR LLM Classifier

This project provides a simple interface for Optical Character Recognition (OCR) and spam classification using deep learning models. It supports three OCR methods (PaddleOCR, EasyOCR, and KerasOCR) and uses a DistilBERT model for classifying the extracted text as "Spam" or "Not Spam."

Features

• Extract text from images using OCR.
• Classify extracted text as either "Spam" or "Not Spam."
• Save the extracted text and classification results to a local JSON and CSV file.

How It Works

1. OCR: The app uses one of the three OCR methods to extract text from the uploaded image:

   • PaddleOCR
   • EasyOCR
   • KerasOCR

2. Classification: The extracted text is passed to a pre-trained DistilBERT model that classifies the text as either "Spam" or "Not Spam."

3. Save Results: The extracted text and classification results are saved locally in both JSON and CSV formats, allowing easy retrieval and review.

Installation

To get started with this project, follow these steps:

1. Clone the Repository

git clone https://github.com/yourusername/ocr-llm-test.git
cd ocr-llm-test

2. Install Dependencies

You can install the required dependencies using pip:

pip install -r requirements.txt

3. Run the App

To run the Gradio interface locally, execute:

python app.py

Once the app is running, it will be accessible through your web browser at http://localhost:7860.

API Documentation

1. API Endpoint

The main endpoint for this API is /predict.

2. API Call Example

Install the Python Client

If you don't already have it installed, run the following command:

pip install gradio_client

Make an API Call

from gradio_client import Client, handle_file

client = Client("winamnd/ocr-llm-test")
result = client.predict(
    method="PaddleOCR",
    img=handle_file('https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png'),
    api_name="/predict"
)
print(result)

3. Parameters

Parameter	Type	Description
method	Literal['PaddleOCR', 'EasyOCR', 'KerasOCR']	Choose the OCR method to be used for text extraction. Default is "PaddleOCR."
img	dict	The image input, which can be provided as a URL, path, or base64 encoded image.

Image Input Details

• path: Path to a local file.
• url: Publicly available URL for the image.
• size: The size of the image (in bytes).
• orig_name: Original filename.
• mime_type: MIME type of the image.
• is_stream: Always set to False.
• meta: Metadata.

4. Returns

The API returns a tuple with two elements:

• Extracted Text (str): The text extracted from the image.
• Spam Classification (str): The classification result ("Spam" or "Not Spam").