app.py

# -*- coding: utf-8 -*-
"""app.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1WeNkl1pYnT0qeOTsUFooLFLJ1arRHC00
"""

# %pip install ultralytics -q
# %pip install gradio -q

import cv2
import os
import PIL.Image as Image
import gradio as gr
from huggingface_hub import hf_hub_download
from ultralytics import ASSETS, YOLO

# load trained model
model = YOLO("best.pt")

def predict_image(img, conf_threshold, iou_threshold):
    results = model.predict(
        source=img,
        conf=conf_threshold,
        iou=iou_threshold,
        show_labels=True,
        show_conf=True,
        imgsz=640,
    )

    for r in results:
        im_array = r.plot()
        im = Image.fromarray(im_array[..., ::-1])

    return im

current_directory = "/home/user/app/image" 

iface = gr.Interface(
    fn=predict_image,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold")
    ],
    outputs=gr.Image(type="pil", label="Result"),
    title="Fire Detection using YOLOv8n on Gradio",
    description="Upload images for inference. The Ultralytics YOLOv8n trained model is used for inference.",
    examples=[
        [os.path.join(current_directory, "fire_image_1.jpg"), 0.25, 0.45],
        [os.path.join(current_directory, "fire_image_3.jpg"), 0.25, 0.45],
    ]
)

if __name__ == '__main__':
    iface.launch()