Update README.md

README.md

@@ -36,63 +36,66 @@ Make sure you have the following installed on your machine:
 
    git clone <repository-url>
    cd <repository-name>
+   
+2. **Create the virtual environment**:
 
 
-Create a Virtual Environment (Optional but Recommended):
+   python -m venv ocr-env
+   source ocr-env/bin/activate  # On Windows use `ocr-env\Scripts\activate`
 
-It is recommended to create a virtual environment to manage your dependencies.
 
-python -m venv ocr-env
-source ocr-env/bin/activate  # On Windows use: ocr-env\Scripts\activate
+3. **Install the Required Libraries**:
 
+   pip install -r requirements.txt
 
-Install Required Libraries:
 
-Use the following command to install the required libraries:
+## Running the Application locally
+To run the web application on your local machine, execute the following command in your terminal:
 
-pip install -r requirements.txt
+python app.py
 
-Running the Application Locally
+## Deployment Process
 
-To run the web application locally, execute the following command in your terminal:
+Once I was satisfied with the functionality of my web application, I decided to deploy it to make it accessible to others. Here’s how I did it:
 
-python app.py
+**1. Choose a Deployment Platform**: I opted for Hugging Face Spaces because it allows easy deployment for machine learning applications. However, other platforms like Streamlit Sharing or Heroku could also work.
 
+**2. Clone the Repository**: First, I cloned my project repository from GitHub or any other version control platform I was using. This ensured I had all the latest code on my local machine.
 
+git clone https://github.com/username/OCR_Model.git
 
-##Deployment Process
-Once I was satisfied with the functionality of my web application, I decided to deploy it to make it accessible to others. Here’s how I did it:
+**3. Set Up Environment Variables**: If my application required any sensitive information or API keys, I made sure to set those up in environment variables on the deployment platform.
 
-Choose a Deployment Platform: I opted for Hugging Face Spaces because it allows easy deployment for machine learning applications. However, other platforms like Streamlit Sharing or Heroku could also work.
+**4. Requirements File**: I created a requirements.txt file that listed all the necessary libraries:
 
-Clone the Repository: First, I cloned my project repository from GitHub or any other version control platform I was using. This ensured I had all the latest code on my local machine.
+-gradio
+-transformers
+-Pillow
+-requests
+-torch
+-tensorflow
+-tf-keras
 
-bash
-Copy code
-git clone https://github.com/username/OCR_Model.git
-Set Up Environment Variables: If my application required any sensitive information or API keys, I made sure to set those up in environment variables on the deployment platform.
-
-Requirements File: I created a requirements.txt file that listed all the necessary libraries:
-
-plaintext
-Copy code
-gradio
-transformers
-Pillow
-requests
-torch
-tensorflow
-tf-keras
 This file would ensure that the platform installs all the dependencies needed to run the application.
 
-Deployment Configuration: On Hugging Face Spaces, I navigated to the "Create a Space" option and selected the "Gradio" template. I uploaded my code and the requirements.txt file to the space. The platform automatically detects the required libraries and installs them.
+**5. Deployment Configuration**: On Hugging Face Spaces, I navigated to the "Create a Space" option and selected the "Gradio" template. I uploaded my code as app.py and the requirements.txt file to the space. The platform automatically detects the required libraries and installs them.
 
-Running the Application: After the upload, I clicked on the "Run" button. The Hugging Face platform handles the execution of my application. I could see real-time logs, which helped in debugging if anything went wrong during the startup process.
+**6. Running the Application**: After the upload, I clicked on the "Run" button. The Hugging Face platform handles the execution of my application. I could see real-time logs, which helped in debugging if anything went wrong during the startup process.
 
-Testing: Once the application was running, I accessed the URL provided by Hugging Face to test its functionality. I made sure everything was working as expected before sharing it with others.
+**7. Testing**: Once the application was running, I accessed the URL provided by Hugging Face to test its functionality. I made sure everything was working as expected before sharing it with others.
 
-Sharing: After confirming that the application was live and functional, I shared the link with friends, colleagues, and any potential users to gather feedback and improve the application.
+**8. Sharing**: After confirming that the application was live and functional, I shared the link with friends, colleagues, and any potential users to gather feedback and improve the application.
 
 By following these steps, I successfully deployed my web application, making it accessible for anyone interested in using my OCR model.
 
+## Contributing
+Contributions are welcome! If you have suggestions for improvements or new features, please open an issue or submit a pull request.
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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference