app.py

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

app_title = "Detect defects in bird nest jar"
models_ids = ['linhcuem/defects_nest_jar_yolov5']

current_model_id = models_ids
model = yolov5.load(current_model_id)

examples = [['test_images/16823291638707408-a2A2448-23gmBAS_40174045.jpg', 0.25, 'linhcuem/defects_nest_jar_yolov5'], ['test_images/16823292102253310-a2A2448-23gmBAS_40174046.jpg', 0.25, 'linhcuem/defects_nest_jar_yolov5'], ['test_images/16823291808953550-a2A2448-23gmBAS_40174048.jpg', 0.25, 'linhcuem/defects_nest_jar_yolov5'], ['test_images/16823291801532480-a2A2448-23gmBAS_40174048.jpg', 0.25, 'linhcuem/defects_nest_jar_yolov5']]

def predict(image, threshold=0.3, 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="DO ANH DAT",
    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 Fale,
).launch(enable_queue=True)