Update README.md

README.md

@@ -13,6 +13,9 @@ tags:
 - Classcode
 - '10'
 ---
+# **Gameplay-Classcode-10**
+
+> **Gameplay-Classcode-10** is a vision-language model fine-tuned from **google/siglip2-base-patch16-224** using the **SiglipForImageClassification** architecture. It classifies gameplay screenshots or thumbnails into one of ten popular video game titles.
 
 ```py
 Classification Report:
@@ -35,3 +38,88 @@ Genshin Impact     0.9831    0.9890    0.9860      1000
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/mI7DFpu3kJ6V3EOiJII39.png)
+
+The model predicts one of the following **game categories**:
+
+- **0:** Among Us  
+- **1:** Apex Legends  
+- **2:** Fortnite  
+- **3:** Forza Horizon  
+- **4:** Free Fire  
+- **5:** Genshin Impact  
+- **6:** God of War  
+- **7:** Minecraft  
+- **8:** Roblox  
+- **9:** Terraria
+
+---
+
+# **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/Gameplay-Classcode-10"  # Replace with your actual model path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    0: "Among Us",
+    1: "Apex Legends",
+    2: "Fortnite",
+    3: "Forza Horizon",
+    4: "Free Fire",
+    5: "Genshin Impact",
+    6: "God of War",
+    7: "Minecraft",
+    8: "Roblox",
+    9: "Terraria"
+}
+
+def classify_game(image):
+    """Predicts the game title based on the gameplay 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()
+
+    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_game,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Game Prediction Scores"),
+    title="Gameplay-Classcode-10",
+    description="Upload a gameplay screenshot or thumbnail to identify the game title (Among Us, Fortnite, Minecraft, etc.)."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use**
+
+This model can be used for:
+
+- **Automatic tagging of gameplay content for streamers and creators**  
+- **Organizing gaming datasets**  
+- **Enhancing searchability in gameplay video repositories**  
+- **Training AI systems for game-related content moderation or recommendations**