Walmart Sparkathon Project

Shopping Assistant Backend

This project is a backend implementation of a shopping assistant for the Walmart Sparkathon. It uses OpenAI's GPT model to interpret user queries and the Walmart API (via SerpAPI) to fetch product information.

Repository: https://github.com/HarshJ23/walmart-sparkathon-project

Features

• Interprets natural language shopping queries
• Generates product suggestions based on user input
• Searches Walmart's product catalog
• Returns relevant product information

Tech Stack

• Python
• Flask
• OpenAI API
• SerpAPI (for Walmart product search)

Local Setup

Follow these steps to set up the project locally:

1. Clone the repository

   git clone https://github.com/HarshJ23/walmart-sparkathon-project.git
   cd walmart-sparkathon-project
   

2. Navigate to the backend folder

   cd backend
   

3. Set up a virtual environment

   python -m venv venv 
   venv\Scripts\activate
   

4. Install dependencies

   pip install -r requirements.txt
   

5. Set up environment variables

   Create a .env file in the root directory and add the following(check .env.example for reference):

   OPENAI_API_KEY=your_openai_api_key
   SERPAPI_API_KEY=your_serpapi_api_key
   

   Replace your_openai_api_key and your_serpapi_api_key with your actual API keys.

6. Run the application

   python app_v1.py
   

   The server should start running on http://localhost:8000.

Usage

Send a POST request to http://localhost:8000/shop with a JSON body:

example query:

{
  "text": "Plan the snacks for football watch party at my house."
}

The response will include the assistant's interpretation and relevant product results.

Frontend Setup

Follow these steps to set up the frontend locally:

1. Navigate to the frontend folder

   cd frontend
   

2. Install dependencies

   npm install
   

3. Run the development server

   npm run dev
   

   The frontend should now be running on http://localhost:3000.

Usage

Access the application by opening http://localhost:3000 in your web browser. Use the interface to input your shopping queries and receive product suggestions.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.