import gradio as gr
import yolov7
from yolov7.models.common import autoShape
from yolov7.models.experimental import attempt_load
from yolov7.utils.google_utils import attempt_download_from_hub, attempt_download
from yolov7.utils.torch_utils import TracedModel
YOLO_MODEL_FILE_NAME="kadirnar/yolov7-v0.1"


# YOLO_MODEL_FILE_NAME="kadirnar/yolov7-tiny-v0.1"
def yolov7_inference(
    image: gr.inputs.Image = None,
    image_size: gr.inputs.Slider = 640,
    conf_threshold: gr.inputs.Slider = 0.25,
    iou_threshold: gr.inputs.Slider = 0.45,        
):
    model = yolov7.load(YOLO_MODEL_FILE_NAME, device="cpu", hf_model=True, trace=False)
    model.conf = conf_threshold
    model.iou = iou_threshold
    results = model([image], size=image_size)
    return results.render()[0]

inputs = [
    gr.inputs.Image(type="pil", label="Input Image"),

    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]

outputs = gr.outputs.Image(type="filepath", label="Output Image")
title = "Yolov7: evaluation yolov7.pt"

examples = [['car.jpeg', 640, 0.5, 0.75], 
            ['horse.jpeg', 640, 0.5, 0.75]]
demo_app = gr.Interface(
    fn=yolov7_inference,
    inputs=inputs,
    outputs=outputs,
    title=title,
    examples=examples,
    cache_examples=True,
)
demo_app.launch(debug=True, enable_queue=True)