app.py

import numpy as np
import gradio as gr
from detection import detect_objects
from config import PASCAL_CLASSES


def inference(
    image: np.ndarray,
    iou_thresh: float, thresh: float,
    enable_grad_cam: str,
    transparency: float,
):
    infer_output = detect_objects(image, iou_thresh, thresh, enable_grad_cam, transparency)
    return infer_output


title = "YoloV3 for Pascal VOC Dataset"
description = f"Pytorch Implementation of YoloV3 model trained on Pascal VOC dataset with GradCAM \n Classes in pascol voc are: {', '.join(PASCAL_CLASSES)}"
example_images = [
    ["images/001114.jpg", 0.7, 0.5, True, 0.6],
    ["images/001133.jpg", 0.6, 0.5, True, 0.6],
    ["images/001142.jpg", 0.65, 0.45, True, 0.6],
    ["images/001147.jpg", 0.6, 0.5, True, 0.6],
    ["images/001155.jpg", 0.7, 0.7, True, 0.6],
]

demo = gr.Interface(
    inference,
    inputs=[
        gr.Image(label="Input Image"),
        gr.Slider(0, 1, value=0.5, label="IOU Threshold"),
        gr.Slider(0, 1, value=0.4, label="Threshold"),
        gr.Checkbox(label="Show Grad Cam"),
        gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"),
    ],
    outputs=[
        gr.Gallery(rows=2, columns=1),
    ],
    title=title,
    description=description,
    examples=example_images,
)

demo.launch(debug=True)