app.py

import os
import gradio as gr
import cv2

from ultralytics import YOLO

"""
input_dir = '/home/student/PycharmProjects/pythonProjectYoloObjectPredict/test_images'
output_dir = '/home/student/PycharmProjects/pythonProjectYoloObjectPredict/'

image_files = [f for f in os.listdir(input_dir) if f.lower().endswith('.jpg')]

for image_file in image_files:
    image_path = os.path.join(input_dir, image_file)
    output_path = os.path.join(output_dir, f'{os.path.splitext(image_file)[0]}_result.jpg')

    results = model.predict(image_path, conf=0.25, save=True, save_crop=True)
    print(f'Predictions for {image_file}: {results}')
"""

# Provide the directory path where your images are located
image_directory = '/content/drive/MyDrive/Work/yolo/images/val'

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

# Create a list of full paths to the JPG files
path = [os.path.join(image_directory, filename) for filename in jpg_files]

model = YOLO('/content/drive/MyDrive/Work/yolo/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)