NFT pricing model.

1. Install Python and start a virtual environment
2. Install required packages

pip install -r requirement.txt

3. Run data.py to get data for model - calls from opensea api, process data and saves into data.csv

python3 data.py

4. Run predict.py to get predictions - saves into preds.csv

python3 predict.py 

5. Alternatively run notebooks in colab

• Get data.csv from repository to load into colab notebooks Penguins_data_&_model.ipynb & Penguins_analysis.ipynb

• NFT pricing model
  • app.py: Flask API/web application
  • data.py: function to get and process the data
  • fit.py: initiates a new model, trains the model, and saves as joblib model
  • predict.py: takes in data and returns predictions
  • requirements.txt: list of packages that the app will import
  • lib
    • data: directory that contains the data and prediction files
    • models: directory that contains the pickled model files

1. Run the Flask API locally for testing. Go to directory with app.py.

python app.py

2. In a new terminal window, use HTTPie to make a GET request at the URL of the API.

http http://127.0.0.1:5000/api nft_id==1 

3. Example of successful output.

HTTP/1.0 200 OK
Content-Length: 203
Content-Type: application/json
Date: Thu, 19 Aug 2021 05:48:52 GMT
Server: Werkzeug/1.0.1 Python/3.7.7
{
    "image_link": "https://api.pudgypenguins.io/penguin/image/1",
    "nft_id": 1,
    "opensea-link": "https://opensea.io/assets/0xbd3531da5cf5857e7cfaa92426877b022e612cf8/1",
    "prediction": 3.4
}