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

app_title = "Aerial Sheep Object Detection"
models_ids = ['keremberke/yolov5n-aerial-sheep', 'keremberke/yolov5s-aerial-sheep', 'keremberke/yolov5m-aerial-sheep']
article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>model</a> | <a href='https://huggingface.co/keremberke/aerial-sheep-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/DJI_0039_MOV-252_jpg.rf.a9d3f531dc347711c06539af59ca7329.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/DJI_0040_MOV-141_jpg.rf.b2b23a4bd86ee5f50ff4a063ab4671ca.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/DJI_0043_MOV-102_jpg.rf.4f0018c8c5de23731256755050f0819a.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/DJI_0043_MOV-161_jpg.rf.a2197218b8c9f58272e59d7a8c6cf493.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/DJI_0043_MOV-84_jpg.rf.22ea78648b21f64c276ab348ba82cf49.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep'], ['test_images/img_373_jpg.rf.494e557cd96f79f20750ab7942c9d9c5.jpg', 0.25, 'keremberke/yolov5m-aerial-sheep']]


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)