📸 Shutterstock Content Upload Generator

Built by @NYskytigers
Generate professional Shutterstock-style captions and keywords automatically.
Export your metadata as shutterstock_content_upload.csv in one click.

Automatically generate Shutterstock-ready captions and SEO keywords from your stock photos and images — powered by BLIP (image captioning) and KeyBERT (keyword extraction).
This app runs 100% locally — no API key or internet connection required after initial setup.

🚀 Features

• 🧠 AI caption generation using the BLIP model (Salesforce/blip-image-captioning-base)
• 🔑 Keyword extraction using KeyBERT (all-MiniLM-L6-v2)
• 🗂️ Export to CSV format
• ✏️ Full Editing Control
• 🎨 Category and metadata selection
• 💾 Works completely offline — no OpenAI or external APIs needed

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🛠️ Requirements

• Python 3.10+
• A virtual environment (venv recommended)
• Packages listed in requirements.txt

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⚙️ Installation

1. Clone this repository

   git clone https://github.com/nyskytigers/stock-auto-caption.git
   cd stock-auto-caption
   

2. Create and activate a virtual environment

    python -m venv venv
   

   • On Windows

     venv\Scripts\activate
   

   • On macOS/Linux

     # source venv/bin/activate  
   

3. Install dependencies

    pip install -r requirements.txt
   
   

4. ▶️ Run the App

    streamlit run app.py
   

   Then open your browser and go to: http://localhost:8501

📦 Output

All generated results can be downloaded as a Shutterstock-compatible CSV file(sutterstock_content_upload.csv):

Shutterstock Example

Filename	Description	Keywords	Categories	Editorial	Mature content	Illustration
photo1.jpg	Close-up portrait of a cat	cat, feline, pet, cute, whiskers, animal	Animals/Wildlife	no	no	yes

Adobe Stock Example

Filename	Title	Keywords	Category	Releases
photo2.jpg	A beautiful mountain landscape	mountain, landscape, nature, sky, clouds	12

iStock (Zipped CSVs)

Generates istock_metadata.zip containing one CSV per image: File: photo3.csv

file name	description	country	title	keywords	color
photo3.eps	Urban city skyline		Urban city skyline	city, urban, sky...	yes

🧠 Models Used

BLIP: Bootstrapped Language-Image Pretraining
→ Generates descriptive captions from images.

KeyBERT
→ Extracts SEO-friendly keywords from text using sentence embeddings.

🧩 Future Plans

💬 User feedback saving (learning system)
🔍 CLIP-based similarity search for smarter keyword suggestions
🌐 Public web version hosted via Streamlit Cloud or Hugging Face Spaces

🪪 License This project is open-source and available under the MIT License.

👤 Author @NYskytigers 🕸️ Website 🦋 Bluesky 📺 YouTube