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

yolov7_weights = hf_hub_download(repo_id="LailaMB/visual_pollution_detection", filename="best_640_rpoch56.pt")

model = torch.hub.load('WongKinYiu/yolov7:main', 'custom', yolov7_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 = "Visual Pollution Detection"
description = """Esse modelo é uma pequena demonstração baseada em uma análise de cerca de 60 imagens somente. Para resultados mais confiáveis e genéricos, são necessários mais exemplos (imagens).
  """
 
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(detect,[gr.Image(type="pil"), gr.Image(type="pil")],description="demo for <a href='https://github.com/WongKinYiu/yolov7' style='text-decoration: underline' target='_blank'>WongKinYiu/yolov7</a> Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors").launch()




gr.Interface(
	fn=object_detection,
    inputs=image,
    outputs=outputs,
    title="Visual Pollution Detection",
    description="Demo for <a href='https://github.com/LailaMB/Smartathon_Visual_Pollution_Detection' style='text-decoration: underline' target='_blank'>Smartathon Visual Pollution Detection Model</a>. The model which was developed by AICAS_KSU team to solve the Theme1 problem of the [Smartathon](https://smartathon.hackerearth.com).,
    examples=[],cache_examples=False).launch()