DeepChoice

AI Shopping Super App for eCommerce

🛠️ Configuration

• Download the dataset from Kaggle and place it in the data directory. The dataset contains product images (optional) and metadata.

• Create a .env file in the root directory and set the following environment variables:

  GROQ_API_KEY=your_groq_api_key
  GOOGLE_API_KEY=your_google_api_key

• Download the fine-tuned complaints model from the Kaggle and place it in the complaints_model directory at the root level.

🐳 Docker Compose

• To run the app using Docker Compose:

  docker-compose up --build

  • FastAPI backend: http://localhost:8000
  • Next.js frontend: http://localhost:3000

⚙️ Manual Setup

• Install the required packages:

  pip install -r requirements.txt

• Run the uvicorn dev server:

  uvicorn app.main:app --reload

  It will create the embeddings in data/*.index and run the backend server on http://127.0.0.1:8000.

• Run the Next.js frontend in a separate terminal:

  cd frontend
  npm install
  npm run dev

  It will run the frontend server on http://localhost:3000.

• Open the frontend in your browser: http://localhost:3000

Relevant Resources:

• Products Dataset Drive link (36MB): H&M Personalized Fashion Recommendations
• Product Images - Optional (30.1GB): H&M Product Images

Preview