from flask import Flask # Pour créer le service
from flask import request # Pour faire des jolis "POST"
import requests # Pour "request" un server "autre"
import numpy as np

import json #return json : json.dumps(dico)
import torch
import os
from PIL import Image

#MS

app = Flask(__name__)

#app.config.from_object('config')




#TODO :Charger le model (ou le bon model si on complexifie)
model = torch.hub.load('.', 'custom', path='ai.pt', source='local') # load the AI from a local source


@app.route('/prediction', methods=['GET','POST'])
def prediction():
  card = request.json["card"]

  # Transform card in PIL Image
  img = Image.fromarray(np.uint8(card))
  
  prediction = model(img)  # infere with a PIL image
  #print("img7 predictions (pandas)")
  print(prediction.pandas().xyxy[0])  # img1 predictions (pandas)
  # Create a list of label of each image in the card
  labels = []

  res = prediction.pandas().xyxy[0].to_numpy()
  for i in res:
    # xmin ymin xmax ymax confidence class name
    labels.append(i[-1]) #  
  print(labels)

  nb = len(labels)
  coords = [ [i[-7], i[-5], i[-6], i[-4]] for i in res ]

  myJson = {
      "nb" : nb,
      "coord" : coords,
      "label" : labels
  }

  return myJson



if( __name__ == "__main__"):
    app.run(host="0.0.0.0", port=50001, debug=True)