Update README.md

README.md

@@ -1,6 +1,10 @@
 ---
 license: apache-2.0
 ---
+# **WBC-Type-Classifier**
+
+> **WBC-Type-Classifier** 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 different types of white blood cells (WBCs) using the **SiglipForImageClassification** architecture.  
+
 ```py
 Accuracy: 0.9891
 F1 Score: 0.9893
@@ -23,3 +27,73 @@ weighted avg     0.9891    0.9891    0.9891     17092
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ohM3P0nwh1LxVr7VNuQz2.png)
+
+The model categorizes images into eight classes:
+- **Class 0:** "Basophil"
+- **Class 1:** "Eosinophil"
+- **Class 2:** "Erythroblast"
+- **Class 3:** "IG"
+- **Class 4:** "Lymphocyte"
+- **Class 5:** "Monocyte"
+- **Class 6:** "Neutrophil"
+- **Class 7:** "Platelet"
+
+# **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/WBC-Type-Classifier"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def wbc_classification(image):
+    """Predicts WBC type for a given blood cell 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": "Basophil", "1": "Eosinophil", "2": "Erythroblast", "3": "IG", 
+        "4": "Lymphocyte", "5": "Monocyte", "6": "Neutrophil", "7": "Platelet"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=wbc_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="WBC Type Classification",
+    description="Upload a blood cell image to classify its WBC type."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **WBC-Type-Classifier** model is designed to classify different types of white blood cells from blood smear images. Potential use cases include:  
+
+- **Medical Diagnostics:** Assisting pathologists in identifying different WBC types for diagnosis.
+- **Hematology Research:** Supporting studies related to blood cell morphology and disease detection.
+- **Automated Blood Analysis:** Enhancing automated diagnostic tools for rapid blood cell classification.
+- **Educational Purposes:** Providing insights and training data for medical students and researchers.