public IActionResult Recommend_Restaurant([FromRoute] int id)
{
var activeuser = _userProfileService.Get_User_details(id);
List <(int RestaurantId, float score)> ratings = new List <(int restaurantId, float score)>();
RestaurantPrediction restaurantPrediction = null;
List <Restaurant> Best_Restaurants = _restaurantService.GetBestRestaurants;
foreach (var restaurant in Best_Restaurants)
{
// To determine the possible results of visiting the best restaurants generated
restaurantPrediction = _machine_model.GetPredictionEngine("Restaurant_Recommendation_Model").Predict(new RestaurantRating
{
userId = id,
restaurantId = restaurant.RestaurantId
});
// Using a range of between 1 and 5 to predict the possible results outcome
float _score = (float)Math.Round(restaurantPrediction.PredictedRating, 1);
// using the _score to create a recommendation for each restaurant in the best restaurant list
ratings.Add((restaurant.RestaurantId, _score));
}
var results = new List <Restaurant_Recommendation_Results>();
foreach (var item in ratings)
{
results.Add(new Restaurant_Recommendation_Results()
{
Restaurant_Name = Best_Restaurants
.FirstOrDefault(x => x.RestaurantId == item.RestaurantId).RestaurantName,
Restaurant_Type = Best_Restaurants
.FirstOrDefault(x => x.RestaurantId == item.RestaurantId).RestaurantType,
Restaurant_Rating = item.score
});
}
return(Ok(results));
}
public void Predict(string inputData)
{
//1. Akin to what was done in the Trainer class, we verify that the model exists
//prior to reading it
if (!File.Exists(ModelPath))
{
Console.WriteLine($"Failed to find model at {ModelPath}");
return;
}
//2. Then, we define the ITransformer object
//This object will contain our model once we load via the Model.Load method
ITransformer mlModel;
using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
mlModel = MlContext.Model.Load(stream, out _);
}
if (mlModel == null)
{
Console.WriteLine("Failed to load model");
return;
}
//3. Next, create a PredictionEngine object given the model we loaded earlier
PredictionEngine <RestaurantFeedback, RestaurantPrediction> predictionEngine = MlContext.Model.CreatePredictionEngine <RestaurantFeedback, RestaurantPrediction>(mlModel);
//4. Then, call the Predict method on the PredictionEngine class
RestaurantPrediction prediction = predictionEngine.Predict(new RestaurantFeedback {
Text = inputData
});
//5. Finally, display the prediction output along with the probability
Console.WriteLine($"Based on \"{inputData}\", the feedback is predicted to be:{Environment.NewLine}{(prediction.Prediction ? "Negative" : "Positive")} at a {prediction.Probability:P0} confidence");
}
