Update README.md

README.md

@@ -2,8 +2,26 @@
 license: apache-2.0
 datasets:
 - prithivMLmods/Brain3-Anomaly-Classification
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- brain
+- tumor
+- biology
+- chemistry
+- medical
 ---
 
+![b3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LvOpYoEOjWPr9bozOAaBE.png)
+
+# **Brain3-Anomaly-SigLIP2**
+
+> **Brain3-Anomaly-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-class medical image classification**. It is trained to distinguish between different types of **brain anomalies** using the **SiglipForImageClassification** architecture.
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -18,3 +36,84 @@ weighted avg     0.9652    0.9647    0.9647      6000
 ```
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-YBXtGneTQ6BrB9o5YiZ-.png)
+
+---
+
+## **Label Space: 3 Classes**
+
+The model classifies each image into one of the following categories:
+
+```
+0: brain_glioma
+1: brain_menin
+2: brain_tumor
+```
+
+---
+
+## **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/Brain3-Anomaly-SigLIP2"  # Replace with your model path if different
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "brain_glioma",
+    "1": "brain_menin",
+    "2": "brain_tumor"
+}
+
+def classify_brain_anomaly(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_brain_anomaly,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=3, label="Brain Anomaly Classification"),
+    title="Brain3-Anomaly-SigLIP2",
+    description="Upload a brain scan image to classify it as glioma, meningioma, or tumor."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use**
+
+**Brain3-Anomaly-SigLIP2** can be used for:
+
+* **Medical Diagnostics Support** – Assisting radiologists in identifying brain anomalies from MRI or CT images.
+* **Academic Research** – Supporting experiments in brain tumor classification tasks.
+* **Medical AI Prototyping** – Useful for healthcare AI pipelines involving limited anomaly classes.
+* **Dataset Annotation** – Pre-label brain images for manual review or semi-supervised learning.