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import os
import gradio as gr
import PIL.Image as Image
from ultralytics import YOLO
model = YOLO("best.pt")
def predict_image(img, conf_threshold, iou_threshold, image_size):
"""Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
results = model.predict(
source=img,
conf=conf_threshold,
iou=iou_threshold,
show_labels=True,
show_conf=True,
imgsz=image_size,
)
for r in results:
im_array = r.plot()
im = Image.fromarray(im_array[..., ::-1])
return im
example_list = [["examples/" + example] for example in os.listdir("examples")]
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"),
gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
],
outputs=gr.Image(type="pil", label="Result"),
title="YOLOv10: Real-Time Fire and Smoke Detection",
description="This project utilizes the YOLOv10 model to detect Fire and Smoke in Real-Time. Adjust the confidence and IoU thresholds for optimal detection performance. Upload an image to see the detection results.\n [Github](https://github.com/X-Men01/YOLOv10-Fire-and-Smoke-Detection)",
examples=[
[example_list[0][0], 0.25, 0.45, 640],
[example_list[1][0], 0.25, 0.45, 960],
[example_list[2][0], 0.25, 0.45, 640],
],
allow_flagging="never",
submit_btn="Run Inference",
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
)
if __name__ == "__main__":
iface.launch(show_error=True)
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