Update README.md

README.md

@@ -1,8 +1,135 @@
 ---
-license: mit
 language:
 - en
+license: mit
+library_name: xgboost
+pipeline_tag: text-classification
+tags:
+- xgboost
+- multiclass
+- cuisine
+- region-classification
+- kaggle
 metrics:
-- f1
 - accuracy
----
+- f1
+model-index:
+- name: CuisineClassifier
+  results:
+  - task:
+      type: text-classification
+      name: Cuisine (20 classes)
+    dataset:
+      name: What's Cooking? (Kaggle)
+      type: whats-
+      url: https://www.kaggle.com/datasets/kaggle/recipe-ingredients-dataset
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.77
+    - type: f1
+      value: 0.69
+  - task:
+      type: text-classification
+      name: Region (5 classes)
+    dataset:
+      name: What's Cooking? (Kaggle) — aggregated to regions
+      type: whats-cooking
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.89
+---
+
+# 🍽 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.
+
+---
+
+## 📊 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](https://xgboost.ai/)  
+- **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`
+
+---
+
+## 🧪 Performance
+
+### Model Comparison
+
+| Metric | Stratified Baseline | Logistic Regression | XGBoost |
+|-------|----------------------|---------------------|---------|
+| **Precision (20 cuisines)** | 0.05 | 0.65 | **0.75** |
+| **Recall (20 cuisines)**    | 0.05 | **0.69** | 0.66 |
+| **Macro F1 (20 cuisines)**  | 0.05 | 0.67 | **0.69** |
+| **Accuracy (20 cuisines)**  | 0.10 | 0.75 | **0.77** |
+| **Accuracy (5 regions)**    | 0.27 | **0.89** | **0.89** |
+
+✅ **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.
+
+---
+
+### Per-Region Metrics (5 Classes)
+
+| Region          | Precision (XGB) | Recall (XGB) | F1 (XGB) |
+|-----------------|------------------|--------------|----------|
+| Asia           | 0.94 | 0.92 | 0.93 |
+| Central Europe | 0.85 | **0.93** | 0.89 |
+| Latin America  | 0.92 | 0.88 | 0.90 |
+| Middle East    | **0.88** | 0.74 | 0.81 |
+| North America  | **0.87** | 0.76 | 0.81 |
+
+---
+
+## 🚀 How to Use
+
+```python
+from huggingface_hub import hf_hub_download
+import joblib
+
+class CuisineClassifier:
+
+    def __init__(self, dataset=None):
+        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"{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