AI-Driven Streamlit App

Overview

Developed an AI pipeline that processes images to segment objects, identify them in real-world contexts, extract text, and generate summaries using advanced natural language processing models. The project also includes a web application built with Streamlit where users can upload images to:

• Segment objects using ResNet-50.
• Identify real-world objects.
• Extract text using Optical Character Recognition (OCR).
• Generate automated summaries using GPT-2/GPT-Neo.

Features

• Interactive UI: A responsive and user-friendly interface built with Streamlit.
• Data Visualization: Interactive charts and plots to visualize data in real-time.
• Machine Learning Integration: Integration with machine learning models for data predictions and insights.
• Media Upload: Functionality to upload and manage media files, including images and videos.

Technologies Used

• PyTorch
• Streamlit
• ResNet-50 (for real-world object identification)
• OCR (Optical Character Recognition)
• GPT-2/GPT-Neo (for text generation and summarization)

Installation

To set up and run the AI-Driven-Streamlit-App on your local machine, follow these steps:

Prerequisites

Ensure you have Python 3.7 or higher installed. Check your Python version with:

python --version

Clone the Repository

Clone the repository from GitHub:

git clone https://github.com/MithunKumar09/AI-Driven-Image-Processing-Pipeline-with-Real-World-Object-Identification-And-Automated-Text-Generate.git
cd AiInternTask

Set Up a Virtual Environment

Create and activate a virtual environment:

On Windows:

python -m venv newenv2
newenv2\Scripts\activate

On macOS/Linux:

python -m venv newenv2
source newenv2/bin/activate

Install Dependencies

Install the required Python packages:

pip install -r requirements.txt

Create a .env File

Create a .env file in the root directory to store sensitive configuration details. Ensure it includes necessary environment variables such as API keys or database credentials. The .env file should look something like this:

# Example environment variables
API_KEY=your_api_key_here
DATABASE_URL=your_database_url_here
SECRET_KEY=your_secret_key_here

Run the Application

Start the Streamlit application with:

streamlit run app.py

Project Structure

├── app.py                    # The main Streamlit application file that initializes and runs the app.
├── requirements.txt          # A list of Python packages required for the project.
├── media/                    # Directory for storing uploaded media files.
├── .gitignore                # Specifies files and directories to be ignored by Git.
├── .env                      # Environment variables file (not included in the repository for security reasons).
└── models/                   # Contains the machine learning models used in the application.
    ├── segmentation_model.py
    ├── identification_model.py
    ├── text_extraction_model.py
    └── summarization_model.py

Usage

• Access the App: Open your web browser and go to http://localhost:8501 to interact with the Streamlit app.
• Upload Media: Use the media upload functionality to add images and videos.
• View Data Visualizations: Explore dynamic charts and plots to analyze data.
• Interact with Machine Learning Models: Input data to receive predictions and insights from the integrated models.

Contributing

Contributions to the AI-Driven-Streamlit-App are welcome. To contribute:

1. Fork the repository on GitHub.

2. Create a new branch for your feature or fix:

   git checkout -b feature/your-feature

3. Make your changes and commit them:

   git commit -am 'Add new feature'

4. Push your changes to your forked repository:

   git push origin feature/your-feature

5. Create a Pull Request to merge your changes into the main repository.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or further information, please contact:

• Email: mithunkumaar098@gmail.com
• GitHub: MithunKumar09

Acknowledgements

• Streamlit: For providing a robust framework for building interactive applications.
• Open Source Libraries: Various Python libraries and tools used throughout this project.