Update README.md

README.md

@@ -1,6 +1,5 @@
 ---
 title: ' Ocr Model'
-emoji: 🐠
 colorFrom: green
 colorTo: red
 sdk: streamlit
@@ -8,5 +7,91 @@ 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](https://huggingface.co/).
+
+2. Create a new Space:
+   - Go to [huggingface.co/spaces](https://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