import torch
import gradio as gr
from huggingface_hub import hf_hub_download
from PIL import Image

REPO_ID = "rgp/Street-View-Detection/"
FILENAME = "best.pt"

yolov5_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)

model = torch.hub.load('ultralytics/yolov5', 'custom', path=yolov5_weights, force_reload=True)  # local repo

def object_detection(im, size=640):
    results = model(im)  # inference
    #results.print()  # print results to screen
    #results.show()  # display results
    #results.save()  # save as results1.jpg, results2.jpg... etc.
    results.render()  # updates results.imgs with boxes and labels
    return Image.fromarray(results.imgs[0])

title = "Identificação de Pedestres e meios de locomoção nas ruas"
description = """Esse modelo é uma pequena demonstração baseada em uma análise de cerca de 680 imagens somente.
"""

image = gr.inputs.Image(shape=(640, 640), image_mode="RGB", source="upload", label="Imagem", optional=False)
outputs = gr.outputs.Image(type="pil", label="Output Image")

gr.Interface(
    fn=object_detection,
    inputs=image,
    outputs=outputs,
    title=title,
    description=description,
    examples=[["sample_images/IMG_0125.JPG"], ["sample_images/IMG_0129.JPG"], 
              ["sample_images/IMG_0157.JPG"], ["sample_images/IMG_0158.JPG"], ["sample_images/IMG_012.JPG"]],
).launch()