SellCraft

*This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle*SellCraft
SellCraft is an AI-powered product listing generator designed to help
small businesses, online sellers, and side hustlers create product copy
more quickly.
What it does
SellCraft provides a simple web interface for entering product details
and generating marketing copy, such as:

• A product title
• A product description
• A social media caption The goal is to reduce the time sellers spend writing product listings and make it easier to prepare copy for online stores and social media. Project status The frontend and Express server have been created. The current server implementation is configured to call the Hugging Face Inference API using the Qwen/Qwen2.5-7B-Instruct model.

Important: The hosted inference request currently cannot be relied
on because the Hugging Face account returned a 402 Payment Required
response indicating that no inference credits remained. Local inference
with Ollama is planned, but should be described as working only after
the model has downloaded successfully and the app has been connected to
it and tested.

Built with

• HTML, CSS, and JavaScript for the frontend
• Node.js and Express for the backend
• Hugging Face Inference API integration in the current server implementation
• Qwen2.5 model configuration Run locally Prerequisites
• Node.js and npm
• A Hugging Face access token with the required inference permissions, if using the current hosted-inference implementation
• Clone the repository git clone (https://github.com/srinithivijayakumar82-cloud/Sellcraft) cd SellCraft
• Install dependencies npm install
• Configure your environment Create a .env file in the project root (the same folder as server.js) and add your own token: HF_TOKEN=your_hugging_face_token_here Never commit .env or share your token publicly. The .gitignore file should include: node_modules/ .env
• Start the server npm start Then open http://localhost:3000 in your browser.

Security

• Keep API tokens in environment variables, not in source code.
• Do not upload .env to GitHub.
• Do not upload node_modules; dependencies can be installed with npm install.
• If a token has ever been published publicly, revoke it and create a replacement. Open innovation SellCraft is an open-source project intended to explore how AI can help small sellers create useful product copy. Open models and local inference can offer developers more control over model selection, experimentation, and where inference runs. The local-inference path is still a work in progress and should not be presented as completed until it has been integrated and tested. Current limitations and next steps
• Restore working AI generation by using a provider with available inference access or by integrating and testing a local Ollama model.
• Add validation and clearer error handling for model/provider failures.
• Test the full product-listing flow from the browser through the backend.
• Add a demo link or video once a working demo is available. Contributing Issues and improvements are welcome. Please avoid committing secrets, environment files, or generated dependency folders.

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What I Built

Demo

Code

How I Built It

srinithivijayakumar82-cloud / Sellcraft

SellCraft SellCraft is an AI-powered product listing generator designed to help small businesses, online sellers, and side hustlers create product copy more quickly What it does SellCraft provides a simple web interface for entering product details and generating marketing copy, such as:

• A product title
• A product description
• A social media caption The goal is to reduce the time sellers spend writing product listings and make it easier to prepare copy for online stores and social media Project status The frontend and Express server have been created. The current server implementation is configured to call the Hugging Face Inference API using the Qwen/Qwen2.5-7B-Instruct model.

Important: The hosted inference request currently cannot be relied on because the Hugging Face account returned a 402 Payment Required response indicating that no inference credits remained. Local inference with Ollama is planned, but should be described as working only after the model has downloaded successfully…

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