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Prerequisites

  • Python 3.6+ - requests library (install with pip install requests)
  • A recent Node.js version - axios library (install with npm install axios)

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Code Example

This example demonstrates how to use the Food Analysis API to analyze a food image. We’ll show how to do this in both Python and Node.js. 
import requests
import base64

# Function to encode the image
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

# API endpoint
url = "http://api.calai.app/v4/scanImage"

# Path to your image file
image_path = "path/to/your/image.jpg"

# Encoding the image
base64_image = encode_image(image_path)

# Preparing the payload
payload = {
  data: {
    "imageData": base64_image
    }
}

# Bearer token
bearer_token = "YOUR_API_KEY"

# Headers
headers = {
    "Authorization": f"Bearer {bearer_token}"
}

# Making the API request
response = requests.post(url, json=payload, headers=headers)

# Check if the request was successful
if response.status_code == 200:
    result = response.json()
    if result["success"]:
        # Process the successful response
        analysis = result["data"]
        print(f"Meal Name: {analysis['name']}")
        print(f"Calories: {analysis['calories']}")
        print(f"Protein: {analysis['protein']}g")
        print(f"Carbs: {analysis['carbs']}g")
        print(f"Fats: {analysis['fats']}g")
    else:
        # Handle API-level error
        print(f"API Error: {result['error']}")
else:
    # Handle HTTP error
    print(f"HTTP Error: {response.status_code}")
Both examples do the following:
  1. Encode an image file to base64.
  2. Prepare the payload with the encoded image data.
  3. Send a POST request to the /scanImage endpoint.
  4. Process the response, handling both successful analyses and potential errors.
Remember to replace "path/to/your/image.jpg" with the actual path to your image file, and ensure you have the necessary libraries installed (requests for Python, axios for Node.js).