import os

os.system('pip install insightface==0.5')


import gradio as gr
import numpy as np
import insightface
from insightface.app import FaceAnalysis
from insightface.data import get_image as ins_get_image
from PIL import Image

import PIL

app = FaceAnalysis(name="buffalo_sc", providers=['CPUExecutionProvider'], allowed_modules=['detection'])

article="<p style='text-align: center'><a href='' target='_blank'>Face Detection</a></p>"
description = "This Face Detection Project uses InsightFace Library (https://insightface.ai/). We use RetinaFace-500MF model for the Face Detection. Upload an image or click an example image to use."

def show_preds(input_image, detection_threshold=0.2):

  if detection_threshold<0.05 or detection_threshold==None: detection_threshold = 0.10

  app.prepare(ctx_id=0, det_size=(640, 640), det_thresh=detection_threshold)

  img = PIL.Image.fromarray(input_image, 'RGB')
  basewidth = 900
  wpercent = (basewidth/float(img.size[0]))
  hsize = int((float(img.size[1])*float(wpercent)))
  img = img.resize((basewidth,hsize), Image.ANTIALIAS)

  #display(img)
  faces = app.get(np.array(img))
  detected = app.draw_on(np.array(img), faces)
  return detected

detection_threshold_slider = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.2, label="Detection Threshold")
outputs = gr.outputs.Image(type="pil")

examples = [['example1.jpg',0.2], ['example2.jpg',0.2]]

gr_interface = gr.Interface(fn=show_preds, inputs=["image", detection_threshold_slider], outputs=outputs, title='Face Detection App', article=article,description=description, examples=examples, analytics_enabled = True, enable_queue=True)
gr_interface.launch(inline=False, share=False, debug=True)