Update README.md

README.md

@@ -18,6 +18,10 @@ tags:
 
 ![dfvbd.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/SaNubya9pBi7MhLhR8VVL.png)
 
+# **Hand-Gesture-2-Robot**  
+
+> **Hand-Gesture-2-Robot** 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 recognize hand gestures and map them to specific robot commands using the **SiglipForImageClassification** architecture.  
+
 ```py
 Classification Report:
                         precision    recall  f1-score   support
@@ -45,4 +49,99 @@ Classification Report:
           weighted avg     0.9972    0.9972    0.9972     13552
 ```
 
-![download (2).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/d7PmqrxLjLfGqQCNmqnB3.png)
+![download (2).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/d7PmqrxLjLfGqQCNmqnB3.png)
+
+The model categorizes hand gestures into 17 different robot commands:  
+- **Class 0:** "rotate anticlockwise"  
+- **Class 1:** "increase"  
+- **Class 2:** "release"  
+- **Class 3:** "switch"  
+- **Class 4:** "look up"  
+- **Class 5:** "Terminate"  
+- **Class 6:** "decrease"  
+- **Class 7:** "move backward"  
+- **Class 8:** "point"  
+- **Class 9:** "rotate clockwise"  
+- **Class 10:** "grasp"  
+- **Class 11:** "pause"  
+- **Class 12:** "move forward"  
+- **Class 13:** "Confirm"  
+- **Class 14:** "look down"  
+- **Class 15:** "move left"  
+- **Class 16:** "move right"  
+
+# **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-2-Robot"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def gesture_classification(image):
+    """Predicts the robot command from a hand gesture 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": "rotate anticlockwise", 
+        "1": "increase", 
+        "2": "release", 
+        "3": "switch", 
+        "4": "look up", 
+        "5": "Terminate", 
+        "6": "decrease", 
+        "7": "move backward", 
+        "8": "point", 
+        "9": "rotate clockwise", 
+        "10": "grasp", 
+        "11": "pause", 
+        "12": "move forward", 
+        "13": "Confirm", 
+        "14": "look down", 
+        "15": "move left", 
+        "16": "move right"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=gesture_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Hand Gesture to Robot Command",
+    description="Upload an image of a hand gesture to predict the corresponding robot command."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```  
+
+# **Intended Use:**  
+
+The **Hand-Gesture-2-Robot** model is designed to classify hand gestures into corresponding robot commands. Potential use cases include:  
+
+- **Human-Robot Interaction:** Enabling intuitive control of robots using hand gestures.  
+- **Assistive Technology:** Helping individuals with disabilities communicate commands.  
+- **Industrial Automation:** Enhancing robotic operations in manufacturing.  
+- **Gaming & VR:** Providing gesture-based controls for immersive experiences.  
+- **Security & Surveillance:** Implementing gesture-based access control.