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()