MetaData Extraction

GPT-4 Vision Apparel Metadata Extractor

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Welcome to the GPT-4 Vision Apparel Metadata Extractor repository! This innovative application utilizes the cutting-edge GPT-4 Vision model API to extract detailed metadata from images, focusing specifically on apparel items. Developed with a Python frontend and Streamlit UI, our solution offers seamless integration and a user-friendly experience.

Features

• GPT-4 Vision Integration :- Utilize the power of GPT-4 Vision model API for accurate metadata extraction.
• Streamlit UI :- Enjoy a sleek and intuitive user interface built with Python and Streamlit.
• Multiple Input Options :- Choose between uploading an image or selecting from a dropdown menu.
• Top and Bottom Wear Classification :- Metadata is divided into two categories: top wear and bottom wear.

💻 How to Use

1. Clone the Repository: Clone this repository to your local machine.

   https://github.com/sanket98a/MetaData-Extraction.git

2. Install Dependencies :- Install the necessary Python dependencies.

     pip install -r requirements.txt

3. Run the Application :- Execute streamlit to launch the application locally.

   streamlit run image_level2.py

4. Upload an Image or Choose from Dropdown :- Select your preferred method to input the image.

5. View Metadata :- Explore the extracted metadata for the focused apparel item, categorized into top wear and bottom wear.

Benefits of Metadata Extraction in Apparel Industry

1. Improved Search and Discovery :- Enhance search functionality by enabling users to filter and discover apparel items based on metadata attributes such as brand, color, and style.
2. Personalized Recommendations :- Utilize extracted metadata to deliver personalized recommendations to customers, increasing engagement and satisfaction.
3. Inventory Management :- Streamline inventory management processes by accurately cataloging apparel items with metadata, facilitating tracking and organization.
4. Enhanced Customer Experience :- Provide customers with comprehensive information about apparel items, including size availability and styling options, improving the overall shopping experience.
5. Data-Driven Insights :- Gain valuable insights into customer preferences and market trends through analysis of metadata extracted from image data, informing strategic decision-making and product development.