README.md

metadata

title: ' Ocr Model'
colorFrom: green
colorTo: red
sdk: streamlit
sdk_version: 1.38.0
app_file: app.py
pinned: false

OCR Application with Real-Time Token Streaming

This project is a Streamlit-based web application that uses the.Qwen2VL model to perform Optical Character Recognition (OCR) on uploaded images. It features real-time token streaming and keyword search functionality, deployed on Hugging Face Spaces.

Features

• Image upload and display
• Real-time OCR text extraction with token streaming
• Keyword search in extracted text
• Support for multiple languages (including Hindi and English)

Prerequisites

• Python 3.7+
• pip (Python package manager)

Installation

1. Clone this repository:

   git clone <your-repository-url>
   cd <your-project-directory>
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

Running the Application Locally

1. Ensure you're in the project directory and your virtual environment is activated (if you're using one).

2. Run the Streamlit app:

   streamlit run app.py
   

3. Open your web browser and navigate to the URL provided by Streamlit (usually http://localhost:8501).

Usage

1. Upload an image using the file uploader.
2. Wait for the OCR process to complete. You'll see the extracted text appear in real-time.
3. Use the keyword search feature to find specific words in the extracted text.

Deployment on Hugging Face Spaces

This application is deployed on Hugging Face Spaces using the Streamlit SDK. Here's an overview of the deployment process:

1. Create a Hugging Face account: If you haven't already, sign up at huggingface.co.

2. Create a new Space:

   • Go to huggingface.co/spaces and click on "Create new Space".
   • Choose "Streamlit" as the SDK.
   • Set up your Space with a name and visibility settings.

3. Prepare your repository:

   • Ensure your app.py and requirements.txt are in the root of your repository.
   • Add a README.md file (this file) to your repository.

4. Configure the Space:

   • In your Space's settings, under "Repository", link your GitHub repository.
   • Set the Python version if necessary.
   • Add any required secrets or environment variables.

5. Deploy:

   • Hugging Face Spaces will automatically deploy your app when you push changes to your linked repository.
   • You can also manually trigger a rebuild from the Space's settings.

6. Access your deployed app:

   • Your app will be available at https://huggingface.co/spaces/<your-username>/<your-space-name>.

Remember to update your requirements.txt file if you make any changes to your project dependencies.

Notes

• The application uses CPU for inference by default. If you have a CUDA-capable GPU available on your deployment platform, you can modify the device_map and to() calls in app.py to use GPU acceleration.
• The model and processor are cached using Streamlit's @st.cache_resource decorator to improve performance on subsequent runs.

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