CuisineClassifier

🍽 Cuisine Classifier (XGBoost)

This model classifies dishes based on their ingredients and assigns them either to a Cuisine (20 classes) or a Region (5 classes).
It uses an XGBoost classifier trained on normalized ingredient data.

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📊 Model Overview

• Task: Multiclass Classification (Cuisines & Regions)
• Input: List of ingredients (["salt", "flour", "sugar", ...])
• Output: Cuisine class (e.g. "italian") or Region (e.g. "Central Europe")
• Algorithm: XGBoost
• Training Data: Kaggle What’s Cooking? dataset, ingredients normalized using AllRecipes dataset
• Train/Test Split: 80 / 20, stratified
• Cross Validation: 5-fold CV with random_state=42

🌍 Region Mapping

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🧪 Performance

Model Comparison

✅ Conclusion:
XGBoost achieves the best results for the 20-class cuisine classification and clearly outperforms the baseline.
For the 5-region setting, Logistic Regression and XGBoost perform nearly identically — however, XGBoost provides more consistent results across classes.

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Per-Region Metrics (5 Classes)

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🚀 How to Use

from huggingface_hub import hf_hub_download
import joblib

class CuisineClassifier:

    def __init__(self, classifier="region"):
        print("Initializing CuisineClassifier...")

        components = ["cuisine_pipeline", "label_encoder"]
        paths = {}

        print("Downloading files from Hugging Face Hub...")
        for name in components:
            print(f"Downloading {name}.joblib ...")
            try:
                paths[name] = hf_hub_download(
                    repo_id="NoahMeissner/CuisineClassifier", 
                    filename=f"region_classifier/{name}.joblib"
                    if classifier == "cuisine":
                      filename=f"cuisine_classifier/{name}.joblib"
                )
                print(f"{name} downloaded.")
            except Exception as e:
                print(f"Failed to download {name}: {e}")
                raise

        print("Loading model components with joblib...")
        try:
            self.model = joblib.load(paths["cuisine_pipeline"])
            print("Model loaded.")
            self.label_encoder = joblib.load(paths["label_encoder"])
            print("Label encoder loaded.")
        except Exception as e:
            print(f"Failed to load components: {e}")
            raise

        print("All components loaded successfully.")

    def classify(self, text_input):
        data = " ".join(text_input)
        predicted_class = self.model.predict([data])
        predicted_label = self.label_encoder.inverse_transform(predicted_class)
        return predicted_label