app.py

import json
import gradio as gr
import yolov5
from PIL import Image
from huggingface_hub import hf_hub_download

app_title = "Smoke Object Detection"
models_ids = ['keremberke/yolov5n-smoke', 'keremberke/yolov5s-smoke', 'keremberke/yolov5m-smoke']
article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>model</a> | <a href='https://huggingface.co/keremberke/smoke-object-detection'>dataset</a> | <a href='https://github.com/keremberke/awesome-yolov5-models'>awesome-yolov5-models</a> </p>"

current_model_id = models_ids[-1]
model = yolov5.load(current_model_id)

examples = [['test_images/H_00902_png.rf.127931e9be51d3943ee7fb8a49d6cfa1.jpg', 0.25, 'keremberke/yolov5m-smoke'], ['test_images/H_09986_png.rf.0aeb1695f5989b9adeaa82baaecc65e1.jpg', 0.25, 'keremberke/yolov5m-smoke'], ['test_images/L_00261_png.rf.497e30c8474732bde3c12c31309c774c.jpg', 0.25, 'keremberke/yolov5m-smoke'], ['test_images/L_04459_png.rf.deeec1f4ef32d2d26881c275f71ba2b9.jpg', 0.25, 'keremberke/yolov5m-smoke'], ['test_images/M_00194_png.rf.a2157843f797aab94a8e26b5733c2402.jpg', 0.25, 'keremberke/yolov5m-smoke'], ['test_images/M_00848_png.rf.ec61e10aa03fb5d4f4cd3a4b615c77ad.jpg', 0.25, 'keremberke/yolov5m-smoke']]


def predict(image, threshold=0.25, model_id=None):
    # update model if required
    global current_model_id
    global model
    if model_id != current_model_id:
        model = yolov5.load(model_id)
        current_model_id = model_id

    # get model input size
    config_path = hf_hub_download(repo_id=model_id, filename="config.json")
    with open(config_path, "r") as f:
        config = json.load(f)
    input_size = config["input_size"]

    # perform inference
    model.conf = threshold
    results = model(image, size=input_size)
    numpy_image = results.render()[0]
    output_image = Image.fromarray(numpy_image)
    return output_image


gr.Interface(
    title=app_title,
    description="Created by 'keremberke'",
    article=article,
    fn=predict,
    inputs=[
        gr.Image(type="pil"),
        gr.Slider(maximum=1, step=0.01, value=0.25),
        gr.Dropdown(models_ids, value=models_ids[-1]),
    ],
    outputs=gr.Image(type="pil"),
    examples=examples,
    cache_examples=True if examples else False,
).launch(enable_queue=True)