app.py

import gradio as gr
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import torch
import matplotlib.pyplot as plt


feature_extractor = AutoFeatureExtractor.from_pretrained("brendenc/my-segmentation-model")
model = AutoModelForImageClassification.from_pretrained("brendenc/my-segmentation-model")

labels = dataset['train'].features['labels'].names

def classify(im):
  inputs = extractor(images=im, return_tensors="pt").to("cuda")
  outputs = model(**inputs)
  logits = outputs.logits
  classes = logits[0].detach().cpu().numpy().argmax(axis=0)
  colors = np.array([[128,0,0], [128,128,0], [0, 0, 128], 	[128,0,128], [0, 0, 0]])
  return colors[classes]

example_imgs = [f"example_{i}.jpg" for i in range(3)]
interface = gr.Interface(classify,
                         inputs="image",
                         outputs="image",
                         examples = example_imgs,
                         title = "Street Image Segmentation",
                         description = """Below is a simple app for image segmentation. This model was trained using""")

interface.launch(debug=True)