Update README.md

README.md

@@ -1,6 +1,11 @@
 ---
 license: apache-2.0
 ---
+# **Facial-Emotion-Detection-SigLIP2**
+
+> **Facial-Emotion-Detection-SigLIP2** 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 different facial emotions using the **SiglipForImageClassification** architecture.  
+
+
 ```py
 Classification Report:
               precision    recall  f1-score   support
@@ -17,3 +22,71 @@ Classification Report:
 weighted avg     0.8703    0.8665    0.8663     15453
 ```
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/j29921aYUCg9a5ZqXQ8P2.png)
+
+    The model categorizes images into 6 facial emotion classes:
+    
+        Class 0: "Ahegao"
+        Class 1: "Angry"
+        Class 2: "Happy"
+        Class 3: "Neutral"
+        Class 4: "Sad"
+        Class 5: "Surprise"
+
+```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/Facial-Emotion-Detection-SigLIP2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def emotion_classification(image):
+    """Predicts facial emotion classification for 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": "Ahegao", "1": "Angry", "2": "Happy", "3": "Neutral",
+        "4": "Sad", "5": "Surprise"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=emotion_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Facial Emotion Detection",
+    description="Upload an image to classify the facial emotion."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **Facial-Emotion-Detection-SigLIP2** model is designed to classify different facial emotions based on images. Potential use cases include:  
+
+- **Mental Health Monitoring:** Detecting emotional states for well-being analysis.
+- **Human-Computer Interaction:** Enhancing user experience by recognizing emotions.
+- **Security & Surveillance:** Identifying suspicious or aggressive behaviors.
+- **AI-Powered Assistants:** Supporting AI-based emotion recognition for various applications.
+