Update README.md

README.md

@@ -2,7 +2,25 @@
 license: apache-2.0
 datasets:
 - anson-huang/mirage-news
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- Fake
+- Real
+- SigLIP2
+- Mirage
 ---
+
+![zdfgsdfz.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jlEXmQDn1tBgBCHjO3ytD.png)
+
+# **Mirage-Photo-Classifier**
+
+> **Mirage-Photo-Classifier** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a binary image authenticity classification task. It is designed to determine whether an image is real or AI-generated (fake) using the **SiglipForImageClassification** architecture.
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -16,3 +34,73 @@ weighted avg     0.9484    0.9464    0.9463     10000
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/FwEjat-T3wv1v1Idiu8Qm.png)
+
+The model categorizes images into two classes:
+
+- **Class 0:** Real  
+- **Class 1:** Fake  
+
+---
+
+# **Run with Transformers 🤗**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Mirage-Photo-Classifier"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+labels = {
+    "0": "Real",
+    "1": "Fake"
+}
+
+def classify_image_authenticity(image):
+    """Predicts whether the image is real or AI-generated (fake)."""
+    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 = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_image_authenticity,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Mirage Photo Classifier",
+    description="Upload an image to determine if it's Real or AI-generated (Fake)."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use**
+
+The **Mirage-Photo-Classifier** model is designed to detect whether an image is genuine (photograph) or synthetically generated. Use cases include:
+
+- **AI Image Detection:** Identifying AI-generated images in social media, news, or datasets.  
+- **Digital Forensics:** Helping professionals detect image authenticity in investigations.  
+- **Platform Moderation:** Assisting content platforms in labeling generated content.  
+- **Dataset Validation:** Cleaning and verifying training data for other AI models.