Create emotion_classification.py

emotion_classification.py

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+import gradio as gr
+import spaces
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+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)
+
+@spaces.GPU
+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
+
+if __name__ == "__main__":
+    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."
+    )
+    iface.launch()