app.py

from transformers import pipeline
import gradio as gr
import io
import matplotlib.pyplot as plt
from PIL import Image
from random import choice

COLORS = ["#ff7f7f", "#ff7fbf", "#ff7fff", "#bf7fff",
            "#7f7fff", "#7fbfff", "#7fffff", "#7fffbf",
            "#7fff7f", "#bfff7f", "#ffff7f", "#ffbf7f"]

def get_figure(in_pil_img, in_results):
  plt.figure(figsize=(16, 10))
  plt.imshow(in_pil_img)
  ax = plt.gca()

  for prediction in in_results:
    selected_color = choice(COLORS)

    x, y = prediction['box']['xmin'], prediction['box']['ymin'],
    w, h = prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']

    ax.add_patch(plt.Rectangle((x, y), w, h, fill=False, color=selected_color, linewidth=3))
    ax.text(x, y - 3, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontdict={
      "family" : "Arial",
      "size" : 20,
      "color" : selected_color,
      "weight" : "bold",
    })

  plt.axis("off")

  return plt.gcf()

def classify(in_pil_img):
    detector = pipeline("object-detection", "facebook/detr-resnet-50")
    results = detector(in_pil_img, { "threshold": 0.9 })

    figure = get_figure(in_pil_img, results)

    buf = io.BytesIO()
    figure.savefig(buf, bbox_inches='tight')
    buf.seek(0)
    output_pil_img = Image.open(buf)

    return output_pil_img

demo = gr.Interface(classify,
  inputs=gr.Image(type="pil"),
  outputs=gr.Image(type="pil"),
  title="Object Detection",
  examples=["https://iili.io/JgN38oQ.jpg", "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg"]
)

demo.launch()