Get ML Recommendation

curl --request POST \
  --url https://api.example.com/api/ml/recommend \
  --header 'Content-Type: application/json' \
  --data '
{
  "Nutritional similarity": {},
  "Diet compatibility": {},
  "Meal type matching": {},
  "Exclusion logic": {}
}
'

import requests

url = "https://api.example.com/api/ml/recommend"

payload = {
    "Nutritional similarity": {},
    "Diet compatibility": {},
    "Meal type matching": {},
    "Exclusion logic": {}
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    'Nutritional similarity': {},
    'Diet compatibility': {},
    'Meal type matching': {},
    'Exclusion logic': {}
  })
};

fetch('https://api.example.com/api/ml/recommend', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.example.com/api/ml/recommend",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'Nutritional similarity' => [
        
    ],
    'Diet compatibility' => [
        
    ],
    'Meal type matching' => [
        
    ],
    'Exclusion logic' => [
        
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.example.com/api/ml/recommend"

	payload := strings.NewReader("{\n  \"Nutritional similarity\": {},\n  \"Diet compatibility\": {},\n  \"Meal type matching\": {},\n  \"Exclusion logic\": {}\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.example.com/api/ml/recommend")
  .header("Content-Type", "application/json")
  .body("{\n  \"Nutritional similarity\": {},\n  \"Diet compatibility\": {},\n  \"Meal type matching\": {},\n  \"Exclusion logic\": {}\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.example.com/api/ml/recommend")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"Nutritional similarity\": {},\n  \"Diet compatibility\": {},\n  \"Meal type matching\": {},\n  \"Exclusion logic\": {}\n}"

response = http.request(request)
puts response.read_body

{
  "recommendations": [
    {
      "id": 123,
      "recipe_id": 123,
      "name": "<string>",
      "calories": 123,
      "protein": 123,
      "carbs": 123,
      "fat": 123,
      "similarity_score": 123,
      "meal_types": [
        {}
      ],
      "diet_types": [
        {}
      ],
      "image_url": "<string>",
      "recipe_url": "<string>"
    }
  ]
}

POST

api

recommend

Get ML Recommendation

curl --request POST \
  --url https://api.example.com/api/ml/recommend \
  --header 'Content-Type: application/json' \
  --data '
{
  "Nutritional similarity": {},
  "Diet compatibility": {},
  "Meal type matching": {},
  "Exclusion logic": {}
}
'

import requests

url = "https://api.example.com/api/ml/recommend"

payload = {
    "Nutritional similarity": {},
    "Diet compatibility": {},
    "Meal type matching": {},
    "Exclusion logic": {}
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    'Nutritional similarity': {},
    'Diet compatibility': {},
    'Meal type matching': {},
    'Exclusion logic': {}
  })
};

fetch('https://api.example.com/api/ml/recommend', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.example.com/api/ml/recommend",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'Nutritional similarity' => [
        
    ],
    'Diet compatibility' => [
        
    ],
    'Meal type matching' => [
        
    ],
    'Exclusion logic' => [
        
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.example.com/api/ml/recommend"

	payload := strings.NewReader("{\n  \"Nutritional similarity\": {},\n  \"Diet compatibility\": {},\n  \"Meal type matching\": {},\n  \"Exclusion logic\": {}\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.example.com/api/ml/recommend")
  .header("Content-Type", "application/json")
  .body("{\n  \"Nutritional similarity\": {},\n  \"Diet compatibility\": {},\n  \"Meal type matching\": {},\n  \"Exclusion logic\": {}\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.example.com/api/ml/recommend")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"Nutritional similarity\": {},\n  \"Diet compatibility\": {},\n  \"Meal type matching\": {},\n  \"Exclusion logic\": {}\n}"

response = http.request(request)
puts response.read_body

{
  "recommendations": [
    {
      "id": 123,
      "recipe_id": 123,
      "name": "<string>",
      "calories": 123,
      "protein": 123,
      "carbs": 123,
      "fat": 123,
      "similarity_score": 123,
      "meal_types": [
        {}
      ],
      "diet_types": [
        {}
      ],
      "image_url": "<string>",
      "recipe_url": "<string>"
    }
  ]
}

This specific endpoint is not directly implemented. Recipe recommendations are provided through the chat interface and the swap meal endpoint.

How Recommendations Work

SmartEat AI uses a machine learning model to provide personalized recipe recommendations. The system uses:

K-Nearest Neighbors (KNN) algorithm for similarity matching
Nutritional profile matching based on calories, protein, carbs, and fat
Diet type filtering (vegan, vegetarian, high-protein, low-carb, etc.)
Meal type compatibility (breakfast, lunch, dinner, snack)
User preference learning from your profile tastes and restrictions

Recommendation Features

1. Smart Meal Plan Generation

When you request a meal plan through the chat interface, the AI:

Analyzes your profile (goals, macros, preferences)
Selects recipes that match your nutritional targets
Ensures variety across the week
Respects your dietary restrictions and preferences
Balances meal types according to your meals-per-day setting

2. Recipe Similarity Matching

The ML model finds similar recipes based on:

calculation

Calculates distance in a scaled feature space of calories, protein, carbohydrates, and fat content

filter

Ensures recommended recipes match your selected diet types (e.g., vegan, high-protein)

constraint

Only recommends recipes suitable for the target meal slot (breakfast, lunch, dinner, snack)

filter

Prevents recommending recipes already in your current meal plan

Getting Recommendations

You can get personalized recommendations through:

Via Chat Interface

Request via Chat

curl -X POST https://api.smarteat.ai/api/chat/ \
  -H "Authorization: Bearer <token>" \
  -d 'message=Suggest a high-protein breakfast for tomorrow'

Via Swap Endpoint

For direct recipe swapping, use the Swap Meal Recommendation endpoint.

ML Model Architecture

Feature Space

The recommendation engine operates in a scaled feature space:

Feature Scaling

features = [
    'calories',      # Total caloric content
    'protein',       # Protein in grams
    'carbs',         # Carbohydrates in grams  
    'fat'           # Fat in grams
]

# Features are normalized using StandardScaler
# This ensures equal weight across different scales

Similarity Calculation

K-Nearest Neighbors

# Model configuration
n_neighbors = 550  # Search space size (configurable)
metric = 'euclidean'  # Distance calculation method

# The model finds the closest recipes in the scaled feature space
# then filters by diet and meal type constraints

Example Recommendation Flow

Response Schema (Conceptual)

If this endpoint were implemented, it would return:

array

Array of recommended recipes

Show Recipe recommendation

integer

Database ID of the recipe

integer

External recipe ID from the dataset

string

Name of the recipe

integer

Total calories per serving

integer

Protein in grams

integer

Carbohydrates in grams

integer

Fat in grams

number

Distance metric indicating how similar this recipe is to your preferences (lower is more similar)

array

Array of compatible meal types (breakfast, lunch, dinner, snack)

array

Array of diet categories this recipe belongs to

string

URL to recipe image

string

URL to full recipe details

Notes

The ML model is loaded on application startup for fast inference
Recommendations are personalized based on your complete profile
The system learns from your preferences over time
Use the Swap Meal endpoint for direct recipe substitutions

Authentication

Profile

Plans

Chat

Recommendations

Get ML Recommendation

How Recommendations Work

Recommendation Features

1. Smart Meal Plan Generation

2. Recipe Similarity Matching

Getting Recommendations

Via Chat Interface

Via Swap Endpoint

ML Model Architecture

Feature Space

Similarity Calculation

Example Recommendation Flow

Response Schema (Conceptual)

Notes

​How Recommendations Work

​Recommendation Features

​1. Smart Meal Plan Generation

​2. Recipe Similarity Matching

​Getting Recommendations

​Via Chat Interface

​Via Swap Endpoint

​ML Model Architecture

​Feature Space

​Similarity Calculation

​Example Recommendation Flow

​Response Schema (Conceptual)

​Notes

How Recommendations Work

Recommendation Features

1. Smart Meal Plan Generation

2. Recipe Similarity Matching

Getting Recommendations

Via Chat Interface

Via Swap Endpoint

ML Model Architecture

Feature Space

Similarity Calculation

Example Recommendation Flow

Response Schema (Conceptual)

Notes