Update README.md

README.md

@@ -2,8 +2,26 @@
 license: apache-2.0
 datasets:
 - avnishs17/food_not_food
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- text-generation-inference
+- food
+- biology
+- Food-or-Not
 ---
 
+![f/n.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ZjKzw5Y6XOtCqiHsoNArs.png)
+
+# **Food-or-Not-SigLIP2**
+
+> **Food-or-Not-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **binary image classification**. It is trained to distinguish between images of **food** and **non-food** objects using the **SiglipForImageClassification** architecture.
+
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -16,4 +34,83 @@ Classification Report:
 weighted avg     0.8778    0.8774    0.8773      8000
 ```
 
-![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/mrelJZ86Pt-NBce0Ty9Re.png)
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/mrelJZ86Pt-NBce0Ty9Re.png)
+
+---
+
+## **Label Space: 2 Classes**
+
+The model classifies each image into one of the following categories:
+
+```
+Class 0: "food"
+Class 1: "not-food"
+```
+
+---
+
+## **Install Dependencies**
+
+```bash
+pip install -q transformers torch pillow gradio
+```
+
+---
+
+## **Inference Code**
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Food-or-Not-SigLIP2"  # Replace with your model path if different
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "food",
+    "1": "not-food"
+}
+
+def classify_food(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()
+
+    prediction = {
+        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
+    }
+
+    return prediction
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify_food,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=2, label="Food Classification"),
+    title="Food-or-Not-SigLIP2",
+    description="Upload an image to detect if it contains food or not."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use**
+
+**Food-or-Not-SigLIP2** can be used for:
+
+* **Dietary Apps** – Automatically classify images for food detection.
+* **Retail & E-commerce** – Filter food vs non-food products visually.
+* **Content Moderation** – Flag content containing food items.
+* **Dataset Curation** – Separate food-related images for training or filtering.