app.py

import os
import gradio as gr
import cv2

from ultralytics import YOLO

image_directory = '/home/user/app/some_flat_plans'

jpg_files = [file for file in os.listdir(image_directory) if file.lower().endswith('.jpg')]

path = [os.path.join(image_directory, filename) for filename in jpg_files]

model = YOLO('/home/user/app/best.pt')

inputs_image = [
    gr.components.Image(type="filepath", label="Input Image"),
]
outputs_image = [
    gr.components.Image(type="numpy", label="Output Image"),
]

def show_preds_image(image_path):
    image = cv2.imread(image_path)
    outputs = model.predict(source=image_path)
    results = outputs[0].cpu().numpy()
    for i, det in enumerate(results.boxes.xyxy):
        cv2.rectangle(
            image,
            (int(det[0]), int(det[1])),
            (int(det[2]), int(det[3])),
            color=(0, 0, 255),
            thickness=2,
            lineType=cv2.LINE_AA
        )
    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

interface_image = gr.Interface(
  fn=show_preds_image,
  inputs=inputs_image,
  outputs=outputs_image,
  title="Floor Plan Detector",
  examples=path,
  cache_examples=False,
)

gr.TabbedInterface(
    [interface_image],
    tab_names=['Image Inference'],
).queue().launch(debug=True)