Update README.md

README.md

@@ -1,6 +1,9 @@
 ---
 license: apache-2.0
 ---
+# **Fashion-Product-Usage**
+
+> **Fashion-Product-Usage** is a vision-language model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies fashion product images based on their intended usage context.
 
 ```py
 Classification Report:
@@ -18,4 +21,84 @@ Smart Casual     0.0000    0.0000    0.0000        67
     accuracy                         0.8458     44072
    macro avg     0.3912    0.2762    0.3024     44072
 weighted avg     0.8300    0.8458    0.8159     44072
-```
+```
+
+The model predicts one of the following usage categories:
+
+- **0:** Casual  
+- **1:** Ethnic  
+- **2:** Formal  
+- **3:** Home  
+- **4:** Party  
+- **5:** Smart Casual  
+- **6:** Sports  
+- **7:** Travel
+
+---
+
+# **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-Usage"  # Replace with your actual model path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    0: "Casual",
+    1: "Ethnic",
+    2: "Formal",
+    3: "Home",
+    4: "Party",
+    5: "Smart Casual",
+    6: "Sports",
+    7: "Travel"
+}
+
+def classify_usage(image):
+    """Predicts the usage type of 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_usage,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Usage Prediction Scores"),
+    title="Fashion-Product-Usage",
+    description="Upload a fashion product image to predict its intended usage (Casual, Formal, Party, etc.)."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use**
+
+This model can be used for:
+
+- **Product tagging in e-commerce catalogs**  
+- **Context-aware product recommendations**  
+- **Fashion search optimization**  
+- **Data annotation for training recommendation engines**