Create README.md

README.md

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+---
+library_name: keras
+tags:
+- image-classification
+- food-recognition
+- food-vision
+- uecfood256
+- tensorflow
+datasets:
+- uecfood256
+pipeline_tag: image-classification
+---
+
+# Food Recognition Model (UECFOOD256)
+
+This model is a deep learning classifier capable of recognizing **256 different types of food**. It was trained using **TensorFlow/Keras** on the **UECFOOD256** dataset.
+
+## Model Details
+- **Model Architecture:** [INSERT BASE MODEL HERE, e.g., MobileNetV2 / EfficientNetB0] (Fine-tuned)
+- **Dataset:** UECFOOD256 (256 food categories)
+- **Framework:** TensorFlow / Keras
+- **Task:** Image Classification (Multi-class)
+
+## Intended Use
+This model is designed to take an image of food as input and output the specific food category name. It is useful for:
+- Calorie tracking apps
+- Dietary monitoring
+- Food logging automation
+
+## How to Use
+
+You can load this model directly in Python using the `huggingface_hub` library. 
+
+**Important:** This model requires a `class_names.txt` file (included in the repo) to map the model's integer predictions back to readable food names.
+
+### Inference Code
+
+```python
+import numpy as np
+from huggingface_hub import from_pretrained_keras, hf_hub_download
+from keras.preprocessing import image
+import tensorflow as tf
+
+# 1. Define the Repo ID
+repo_id = "Hiratax/food-recognition-model"
+
+# 2. Load the Model
+print("Loading model...")
+model = from_pretrained_keras(repo_id)
+
+# 3. Load the Class Names Mapping
+class_names_path = hf_hub_download(repo_id=repo_id, filename="class_names.txt")
+with open(class_names_path, 'r') as f:
+    class_names = [line.strip() for line in f.readlines()]
+
+# 4. Prediction Function
+def predict_food(img_path):
+    # Resize image to 224x224 (Standard for this model)
+    img = image.load_img(img_path, target_size=(224, 224))
+    img_array = image.img_to_array(img)
+    img_batch = np.expand_dims(img_array, axis=0)
+
+    # Predict
+    predictions = model.predict(img_batch)
+    predicted_index = np.argmax(predictions[0])
+    confidence = np.max(predictions[0])
+    
+    return class_names[predicted_index], confidence
+
+# 5. Run on an image
+# food, conf = predict_food("my_sushi.jpg")
+# print(f"Prediction: {food} ({conf:.2f})")