Update README.md

README.md

@@ -3,7 +3,9 @@ license: apache-2.0
 datasets:
 - ewanlong/Food_Classification_Dataset
 ---
+# **Indian-Western-Food-34**  
 
+> **Indian-Western-Food-34** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify food images into various Indian and Western dishes using the **SiglipForImageClassification** architecture.  
 
 ```py
 Classification Report:
@@ -47,4 +49,116 @@ Crispy Chicken     0.9811    0.9707    0.9759      1500
       accuracy                         0.9729     23873
      macro avg     0.9719    0.9775    0.9745     23873
   weighted avg     0.9731    0.9729    0.9729     23873
-```
+```
+
+---
+
+The model categorizes images into 34 food classes:  
+
+### **Western Foods**  
+- **Class 0:** "Baked Potato"  
+- **Class 1:** "Crispy Chicken"  
+- **Class 2:** "Donut"  
+- **Class 3:** "Fries"  
+- **Class 4:** "Hot Dog"  
+- **Class 5:** "Sandwich"  
+- **Class 6:** "Taco"  
+- **Class 7:** "Taquito"  
+- **Class 8:** "Apple Pie"  
+- **Class 9:** "Burger"  
+- **Class 13:** "Cheesecake"  
+- **Class 18:** "Fried Rice"  
+- **Class 19:** "Ice Cream"  
+- **Class 27:** "Omelette"  
+- **Class 31:** "Pizza"  
+- **Class 33:** "Sushi"  
+
+### **Indian Foods**  
+- **Class 10:** "Butter Naan"  
+- **Class 11:** "Chai"  
+- **Class 12:** "Chapati"  
+- **Class 14:** "Chicken Curry"  
+- **Class 15:** "Chole Bhature"  
+- **Class 16:** "Dal Makhani"  
+- **Class 17:** "Dhokla"  
+- **Class 20:** "Idli"  
+- **Class 21:** "Jalebi"  
+- **Class 22:** "Kaathi Rolls"  
+- **Class 23:** "Kadai Paneer"  
+- **Class 24:** "Kulfi"  
+- **Class 25:** "Masala Dosa"  
+- **Class 26:** "Momos"  
+- **Class 28:** "Paani Puri"  
+- **Class 29:** "Pakode"  
+- **Class 30:** "Pav Bhaji"  
+- **Class 32:** "Samosa"  
+
+---
+
+# **Run with Transformers🤗**  
+
+```python
+!pip install -q transformers torch pillow gradio
+```  
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Indian-Western-Food-34"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def food_classification(image):
+    """Predicts the type of food in an image."""
+    image = Image.fromarray(image).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+    
+    labels = {
+        "0": "Baked Potato", "1": "Crispy Chicken", "2": "Donut", "3": "Fries", 
+        "4": "Hot Dog", "5": "Sandwich", "6": "Taco", "7": "Taquito", "8": "Apple Pie", 
+        "9": "Burger", "10": "Butter Naan", "11": "Chai", "12": "Chapati", "13": "Cheesecake", 
+        "14": "Chicken Curry", "15": "Chole Bhature", "16": "Dal Makhani", "17": "Dhokla", 
+        "18": "Fried Rice", "19": "Ice Cream", "20": "Idli", "21": "Jalebi", "22": "Kaathi Rolls", 
+        "23": "Kadai Paneer", "24": "Kulfi", "25": "Masala Dosa", "26": "Momos", "27": "Omelette", 
+        "28": "Paani Puri", "29": "Pakode", "30": "Pav Bhaji", "31": "Pizza", "32": "Samosa", 
+        "33": "Sushi"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=food_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Indian & Western Food Classification",
+    description="Upload a food image to classify it into one of the 34 food types."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use:**  
+
+The **Indian-Western-Food-34** model is designed to classify food images into Indian and Western dishes. Potential use cases include:  
+
+- **Restaurant & Food Delivery Apps:** Enhancing food recognition for better menu recommendations.  
+- **Health & Nutrition Apps:** Tracking calorie intake and diet preferences.  
+- **Food Blogging & Social Media:** Auto-tagging food items in posts.  
+- **Educational Purposes:** Teaching AI-based food classification.