Update README.md

README.md

@@ -16,6 +16,12 @@ tags:
 - Vision-Encoder
 ---
 
+![15.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/JBoqEwRBoOQwik0aRYeGw.png)
+
+# **Hand-Gesture-19**  
+
+> **Hand-Gesture-19** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify hand gesture images into different categories using the **SiglipForImageClassification** architecture.  
+
 ```py
 Classification Report:
                  precision    recall  f1-score   support
@@ -46,3 +52,103 @@ two_up_inverted     0.9855    0.9871    0.9863      6967
 ```
 
 ![download (2).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/BhwQi6V5Qzl3g33OvRsWz.png)
+
+
+The model categorizes images into nineteen hand gestures:  
+- **Class 0:** "call"  
+- **Class 1:** "dislike"  
+- **Class 2:** "fist"  
+- **Class 3:** "four"  
+- **Class 4:** "like"  
+- **Class 5:** "mute"  
+- **Class 6:** "no_gesture"  
+- **Class 7:** "ok"  
+- **Class 8:** "one"  
+- **Class 9:** "palm"  
+- **Class 10:** "peace"  
+- **Class 11:** "peace_inverted"  
+- **Class 12:** "rock"  
+- **Class 13:** "stop"  
+- **Class 14:** "stop_inverted"  
+- **Class 15:** "three"  
+- **Class 16:** "three2"  
+- **Class 17:** "two_up"  
+- **Class 18:** "two_up_inverted"  
+
+# **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 transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Hand-Gesture-19"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def hand_gesture_classification(image):
+    """Predicts the hand gesture category from an 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()
+    
+    labels = {
+        "0": "call", 
+        "1": "dislike", 
+        "2": "fist", 
+        "3": "four", 
+        "4": "like", 
+        "5": "mute", 
+        "6": "no_gesture", 
+        "7": "ok", 
+        "8": "one", 
+        "9": "palm", 
+        "10": "peace", 
+        "11": "peace_inverted", 
+        "12": "rock", 
+        "13": "stop", 
+        "14": "stop_inverted", 
+        "15": "three", 
+        "16": "three2", 
+        "17": "two_up", 
+        "18": "two_up_inverted"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=hand_gesture_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Hand Gesture Classification",
+    description="Upload an image to classify the hand gesture."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```  
+
+# **Intended Use:**  
+
+The **Hand-Gesture-19** model is designed to classify hand gesture images into different categories. Potential use cases include:  
+
+- **Human-Computer Interaction:** Enabling gesture-based controls for devices.  
+- **Sign Language Interpretation:** Assisting in recognizing sign language gestures.  
+- **Gaming & VR:** Enhancing immersive experiences with hand gesture recognition.  
+- **Robotics:** Facilitating gesture-based robotic control.  
+- **Security & Surveillance:** Identifying gestures for access control and safety monitoring.