app.py

import supervision as sv
import gradio as gr
from ultralytics import YOLO
import sahi
import numpy as np


# Images
sahi.utils.file.download_from_url(
    "https://transform.roboflow.com/zD7y6XOoQnh7WC160Ae7/48174c7c26c2cbca52b084ebbb03d215/thumb.jpg",
    "f2.jpg",
)
sahi.utils.file.download_from_url(
    "https://transform.roboflow.com/zD7y6XOoQnh7WC160Ae7/3d1f22e387164a6719995aa0d9dc16a1/thumb.jpg",
    "f3.jpg",
)




annotatorbbox = sv.BoxAnnotator()
annotatormask=sv.MaskAnnotator()


def yolov8_inference(
    image: gr.inputs.Image = None,
    conf_threshold: gr.inputs.Slider = 0.5,
    iou_threshold: gr.inputs.Slider = 0.45,
):

    image=image[:, :, ::-1].astype(np.uint8)
    model = YOLO("https://huggingface.co/spaces/devisionx/Second_demo/blob/main/best.pt")
    results = model(image,imgsz=320)[0]
    image=image[:, :, ::-1].astype(np.uint8)
    detections = sv.Detections.from_yolov8(results)
    annotated_image = annotatormask.annotate(scene=image, detections=detections)
    annotated_image = annotatorbbox.annotate(scene=annotated_image , detections=detections)




    return annotated_image

image_input = gr.inputs.Image()  # Adjust the shape according to your requirements

inputs = [
    gr.inputs.Image(label="Input Image"),
    gr.Slider(
        minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"
    ),
    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
]

outputs = gr.Image(type="filepath", label="Output Image")
title = "Fire Smoke Demo"
import os
examples = [
    ["f2.jpg", 0.25, 0.45],
    ["f3.jpg", 0.25, 0.45],
]
demo_app = gr.Interface(examples=examples,
    fn=yolov8_inference,
    inputs=inputs,
    outputs=outputs,
    title=title,
    cache_examples=True,
    theme="default",
)
demo_app.launch(debug=False, enable_queue=True)