Update README.md

README.md

@@ -7,7 +7,18 @@ base_model:
 ---
 
 
-# agent-browse / calendars / human-browse
+# **WebClick-AgentBrowse-SigLIP2**
+
+> **WebClick-AgentBrowse-SigLIP2** is a vision-language encoder model fine-tuned from [`google/siglip2-base-patch16-512`](https://huggingface.co/google/siglip2-base-patch16-512) for **multi-class image classification**.  
+It is trained to detect and classify web UI click regions into three classes: `agentbrowse`, `calendars`, and `humanbrowse`. The model utilizes the `SiglipForImageClassification` architecture.
+
+> \[!note]
+> **SigLIP 2**: *Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*  
+> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
+
+
+> [!note]
+agent-browse / calendars / human-browse
 
 ---
 
@@ -28,6 +39,76 @@ weighted avg     0.9263    0.9219    0.9224      1639
 
 ---
 
+## Label Space: 3 Classes
+
+```
+
+Class 0: agentbrowse
+Class 1: calendars
+Class 2: humanbrowse
+
+````
+
+---
+
+## Install Dependencies
+
+```bash
+pip install -q transformers torch pillow gradio hf_xet
+````
+
+---
+
+## Inference Code
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/webclick-agentbrowse-siglip2"  # Replace with actual HF model repo
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Updated label mapping
+id2label = {
+    "0": "agentbrowse",
+    "1": "calendars",
+    "2": "humanbrowse"
+}
+
+def classify_image(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()
+
+    prediction = {
+        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
+    }
+
+    return prediction
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=3, label="Click Type Classification"),
+    title="WebClick AgentBrowse Classifier",
+    description="Upload a web UI screenshot to classify regions: agentbrowse, calendars, or humanbrowse."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
 ## ID2Label Testing
 
 ```py
@@ -55,4 +136,13 @@ print(id2label)
 {'0': 'agentbrowse', '1': 'calendars', '2': 'humanbrowse'}
 ```
 
----
+---
+
+## Intended Use
+
+**WebClick-AgentBrowse-SigLIP2** is intended for:
+
+* **UI Understanding** – Classify user interaction zones in web interface screenshots.
+* **Multimodal Agents** – Enhance visual perception for agent planning or RPA systems.
+* **Interface Automation** – Facilitate click zone detection for automated agents.
+* **Web Analytics** – Analyze user behavior patterns based on layout interaction predictions.