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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/Gym-Workout-Classifier-SigLIP2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
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() |