How to Save and Load Timestamped Model Predictions in JSON Format to Google Drive from Google Colab

Prerequisites:

• A Google account to access Google Colab and Google Drive.
• Basic understanding of Python, JSON file format, and machine learning models.

Steps:

1. Mounting Google Drive in Google Colab:
   • Begin by mounting your Google Drive to your Google Colab environment with the following code in a new Colab notebook:

from google.colab import drive
drive.mount('/content/drive')

• Follow the prompt to authorize Colab to access your Google Drive.

2. Generating Model Predictions:
   • Let's assume you have a trained model and you've generated some predictions. For simplicity, we'll use the following dummy predictions:

# Assume these are your model's predictions
predictions = {
    "image1": {"class": "dog", "confidence": 0.98},
    "image2": {"class": "cat", "confidence": 0.95}
}

3. Creating a Timestamp:
   • Import the necessary library and generate a timestamp:

import datetime
timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')

4. Constructing File Name with Timestamp:
   • Construct a file name that includes the base string "predictions", an underscore, and the timestamp:

original_string = "predictions"
new_string = original_string + "_" + timestamp

5. Saving Predictions to a JSON File:
   • Now save the predictions to a JSON file with the constructed file name in your Google Drive:

import json

# Path in your Google Drive
file_path = f'/content/drive/My Drive/your_folder/{new_string}.json'

# Write predictions to JSON file
with open(file_path, 'w') as json_file:
    json.dump(predictions, json_file)

6. Reading Predictions from a JSON File:
   • Reading the predictions back is straightforward:

# Read predictions from JSON file
with open(file_path, 'r') as json_file:
    read_predictions = json.load(json_file)

# Now 'read_predictions' contains the data from 'predictions_timestamp.json'
print(read_predictions)

Conclusion: By following these steps, you can easily save and read model predictions in JSON format to and from Google Drive using Google Colab. Appending a timestamp to the file name helps in organizing your files better and tracking when each prediction was made. This setup ensures that your data is structured, easily accessible, and safely stored in Google Drive, ready for sharing or further analysis.