Update README.md

README.md

@@ -1,6 +1,11 @@
 ---
 license: apache-2.0
 ---
+
+# **BrainTumor-Classification-Mini**
+> **BrainTumor-Classification-Mini** 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 brain tumor images using the **SiglipForImageClassification** architecture.  
+
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -17,3 +22,67 @@ weighted avg     0.9918    0.9918    0.9918      5712
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/AzW7SbNOuh9cxrV-8Kq8q.png)
 
+The model categorizes images into the following 4 classes:  
+- **Class 0:** "No Tumor"  
+- **Class 1:** "Glioma"  
+- **Class 2:** "Meningioma"  
+- **Class 3:** "Pituitary"  
+
+# **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/BrainTumor-Classification-Mini"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def brain_tumor_classification(image):
+    """Predicts brain tumor category for 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": "No Tumor", "1": "Glioma", "2": "Meningioma", "3": "Pituitary"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=brain_tumor_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Brain Tumor Classification",
+    description="Upload an image to classify it into one of the 4 brain tumor categories."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **BrainTumor-Classification-Mini** model is designed for brain tumor image classification. It helps categorize MRI images into predefined tumor types. Potential use cases include:  
+
+- **Medical Diagnosis Assistance:** Supporting radiologists in preliminary tumor classification.  
+- **AI-Assisted Healthcare:** Enhancing automated tumor detection in medical imaging.  
+- **Research & Development:** Facilitating studies in AI-driven medical imaging solutions.  
+- **Educational Purposes:** Helping students and professionals learn about tumor classification using AI.