Update README.md

README.md

@@ -15,6 +15,12 @@ tags:
 - experimental
 ---
 
+![GHJ.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/RA1GurybmVjo3m8qO6ABG.png)
+
+# **x-bot-profile-detection**
+
+> **x-bot-profile-detection** is a SigLIP2-based classification model designed to detect **profile authenticity types on social media platforms** (such as X/Twitter). It categorizes a profile image into four classes: **bot**, **cyborg**, **real**, or **verified**. Built on `google/siglip2-base-patch16-224`, the model leverages advanced vision-language pretraining for robust image classification.
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -29,4 +35,85 @@ Classification Report:
 weighted avg     0.9609    0.9559    0.9557     10000
 ```
 
-![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/P0MispJOjkolSsYPbYuhC.png)
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/P0MispJOjkolSsYPbYuhC.png)
+
+---
+
+## **Label Classes**
+
+The model predicts one of the following profile types:
+
+```
+0: bot        → Automated accounts  
+1: cyborg     → Partially automated or suspiciously mixed behavior  
+2: real       → Genuine human users  
+3: verified   → Verified accounts or official profiles
+```
+
+---
+
+## **Installation**
+
+```bash
+pip install transformers torch pillow gradio
+```
+
+---
+
+## **Example 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/x-bot-profile-detection"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Define class mapping
+id2label = {
+    "0": "bot",
+    "1": "cyborg",
+    "2": "real",
+    "3": "verified"
+}
+
+def detect_profile_type(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
+
+# Create Gradio UI
+iface = gr.Interface(
+    fn=detect_profile_type,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=4, label="Predicted Profile Type"),
+    title="x-bot-profile-detection",
+    description="Upload a social media profile picture to classify it as Bot, Cyborg, Real, or Verified using a SigLIP2 model."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Use Cases**
+
+* Social media moderation and automation detection
+* Anomaly detection in public discourse
+* Botnet analysis and influence operation research
+* Platform integrity and trust verification