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from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
import gradio as gr
from PIL import Image
# Load the model and feature extractor
model = SegformerForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
feature_extractor = SegformerFeatureExtractor.from_pretrained("mattmdjaga/segformer_b2_clothes")
def predict(image):
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
# Decode outputs and return results as needed
return "Segmentation output placeholder" # Replace with actual processing
def segmentation_interface(image):
return predict(image)
# Create a Gradio interface for image segmentation
gr.Interface(fn=segmentation_interface, inputs="image", outputs="text").launch()
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