Delete gym_workout_classification.py

gym_workout_classification.py

@@ -1,48 +0,0 @@
-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()