Update README.md

README.md

@@ -1,6 +1,21 @@
 ---
 license: apache-2.0
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- gender
+- fashion
+- product
 ---
+
+# **Fashion-Product-Gender**
+
+> **Fashion-Product-Gender** is a vision model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies fashion product images into one of five gender categories.
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -14,4 +29,78 @@ Classification Report:
     accuracy                         0.7407     44072
    macro avg     0.5933    0.3438    0.3480     44072
 weighted avg     0.7240    0.7407    0.7130     44072
-```
+```
+
+The model predicts one of the following gender categories for fashion products:
+
+- **0:** Boys  
+- **1:** Girls  
+- **2:** Men  
+- **3:** Unisex  
+- **4:** Women
+
+---
+
+# **Run with Transformers 🤗**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Fashion-Product-Gender"  # Replace with your actual model path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    0: "Boys",
+    1: "Girls",
+    2: "Men",
+    3: "Unisex",
+    4: "Women"
+}
+
+def classify_gender(image):
+    """Predicts the gender category for a fashion product."""
+    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()
+
+    predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))}
+    return predictions
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_gender,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Gender Prediction Scores"),
+    title="Fashion-Product-Gender",
+    description="Upload a fashion product image to predict the target gender category (Boys, Girls, Men, Unisex, Women)."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use**
+
+This model is best suited for:
+
+- **Fashion E-commerce tagging and search**  
+- **Personalized recommendations based on gender**  
+- **Catalog organization and gender-based filters**  
+- **Retail analytics and demographic insights**