Create gym_workout_classification.py

gym_workout_classification.py

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+import gradio as gr
+import spaces
+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/Gym-Workout-Classifier-SigLIP2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+@spaces.GPU
+def workout_classification(image):
+    """Predicts workout exercise 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": "barbell biceps curl", "1": "bench press", "2": "chest fly machine", "3": "deadlift",
+        "4": "decline bench press", "5": "hammer curl", "6": "hip thrust", "7": "incline bench press",
+        "8": "lat pulldown", "9": "lateral raises", "10": "leg extension", "11": "leg raises",
+        "12": "plank", "13": "pull up", "14": "push up", "15": "romanian deadlift",
+        "16": "russian twist", "17": "shoulder press", "18": "squat", "19": "t bar row",
+        "20": "tricep dips", "21": "tricep pushdown"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=workout_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Gym Workout Classification",
+    description="Upload an image to classify the workout exercise."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()