Update README.md

README.md

@@ -2,6 +2,10 @@
 license: apache-2.0
 ---
 
+# **Fashion-Product-Season**
+
+> **Fashion-Product-Season** 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 suitable **season of use**.
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -14,4 +18,76 @@ Classification Report:
     accuracy                         0.7121     44072
    macro avg     0.7742    0.6965    0.7289     44072
 weighted avg     0.7174    0.7121    0.7103     44072
-```
+```
+The model predicts one of the following **season categories**:
+
+- **0:** Fall  
+- **1:** Spring  
+- **2:** Summer  
+- **3:** Winter
+
+---
+
+# **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-Season"  # Replace with your actual model path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    0: "Fall",
+    1: "Spring",
+    2: "Summer",
+    3: "Winter"
+}
+
+def classify_season(image):
+    """Predicts the most suitable season 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))}
+    predictions = dict(sorted(predictions.items(), key=lambda item: item[1], reverse=True))
+    return predictions
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_season,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Season Prediction Scores"),
+    title="Fashion-Product-Season",
+    description="Upload a fashion product image to predict its most suitable season (Fall, Spring, Summer, Winter)."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use**
+
+This model can be used for:
+
+- **Seasonal tagging in fashion e-commerce**  
+- **Improved recommendations for seasonal shopping trends**  
+- **Inventory planning based on product seasonality**  
+- **Data labeling for fashion-specific recommendation engines**