Update README.md

README.md

@@ -1,6 +1,10 @@
 ---
 license: apache-2.0
 ---
+# **Gender-Classifier-Mini**
+
+> **Gender-Classifier-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 images based on gender using the **SiglipForImageClassification** architecture.  
+
 ```py
 Accuracy: 0.9720
 F1 Score: 0.9720
@@ -17,3 +21,64 @@ weighted avg     0.9721    0.9720    0.9720      5000
 ```
 
 ![Untitled.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/MNO7bk_1wr5lvfyTDnhjF.png)
+
+The model categorizes images into two classes:
+- **Class 0:** "Female ♀"
+- **Class 1:** "Male ♂"
+
+# **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/Gender-Classifier-Mini"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def gender_classification(image):
+    """Predicts gender 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": "Female ♀", "1": "Male ♂"}
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=gender_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Gender Classification",
+    description="Upload an image to classify its gender."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **Gender-Classifier-Mini** model is designed to classify images into gender categories. Potential use cases include:  
+
+- **Demographic Analysis:** Assisting in understanding gender distribution in datasets.
+- **Face Recognition Systems:** Enhancing identity verification processes.
+- **Marketing & Advertising:** Personalizing content based on demographic insights.
+- **Healthcare & Research:** Supporting gender-based analysis in medical imaging.