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import gradio as gr
from transformers import AutoModel, AutoProcessor
import torch
import requests
from PIL import Image
from io import BytesIO
fashion_items = ['top', 'trousers', 'jumper']
# Load model and processor with CPU device to avoid meta tensor issues
model_name = 'Marqo/marqo-fashionSigLIP'
# Force CPU usage to avoid device mapping issues
device = torch.device('cpu')
# Simple loading approach - let model handle device placement
model = AutoModel.from_pretrained(
model_name,
trust_remote_code=True
).to(device)
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
# Preprocess and normalize text data
with torch.no_grad():
# Ensure truncation and padding are activated
processed_texts = processor(
text=fashion_items,
return_tensors="pt",
truncation=True, # Ensure text is truncated to fit model input size
padding=True # Pad shorter sequences so that all are the same length
)['input_ids']
text_features = model.get_text_features(processed_texts)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
# Prediction function
def predict_from_url(url):
# Check if the URL is empty
if not url:
return {"Error": "Please input a URL"}
try:
image = Image.open(BytesIO(requests.get(url).content))
except Exception as e:
return {"Error": f"Failed to load image: {str(e)}"}
processed_image = processor(images=image, return_tensors="pt")['pixel_values']
with torch.no_grad():
image_features = model.get_image_features(processed_image)
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
text_probs = (100 * image_features @ text_features.T).softmax(dim=-1)
return {fashion_items[i]: float(text_probs[0, i]) for i in range(len(fashion_items))}
# Gradio interface
demo = gr.Interface(
fn=predict_from_url,
inputs=gr.Textbox(label="Enter Image URL"),
outputs=gr.Label(label="Classification Results"),
title="Fashion Item Classifier",
allow_flagging="never"
)
# Launch the interface
demo.launch()
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