# -*- coding: utf-8 -*-
"""Deploy Barcelo demo.ipynb
Automatically generated by Colaboratory.
Original file is located at
    https://colab.research.google.com/drive/1FxaL8DcYgvjPrWfWruSA5hvk3J81zLY9
![   ](https://www.vicentelopez.gov.ar/assets/images/logo-mvl.png)
# Modelo
YOLO es una familia de modelos de detección de objetos a escala compuesta entrenados en COCO dataset, e incluye una funcionalidad simple para Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite.
## Gradio Inferencia
![](https://i.ibb.co/982NS6m/header.png)
Este Notebook se acelera opcionalmente con un entorno de ejecución de GPU
----------------------------------------------------------------------
 YOLOv5 Gradio demo
*Author: Ultralytics LLC and Gradio*
# Código
"""

#!pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt gradio # install dependencies

import os
import re
import json
import numpy as np
import pandas as pd
import gradio as gr
import torch
from PIL import Image
from ultralytics import YOLO

# Images
torch.hub.download_url_to_file('https://i.pinimg.com/originals/7f/5e/96/7f5e9657c08aae4bcd8bc8b0dcff720e.jpg', 'ejemplo1.jpg')
torch.hub.download_url_to_file('https://i.pinimg.com/originals/c2/ce/e0/c2cee05624d5477ffcf2d34ca77b47d1.jpg', 'ejemplo2.jpg')

# Model
class YOLODetect():
    def __init__(self, modelo):
        self.modelo = modelo

    def predecir(self, url):
        # conf	float	0.25	umbral de confianza del objeto para la detección
        # iou	float	0.7	umbral de intersección sobre unión (IoU) para NMS
        self.source = url
        self.results = self.modelo.predict(source=self.source, save=True, imgsz=640, conf=0.5, iou=0.40)
        return self.results

    def show(self):
        results = self.results[0]
        render = None #render_result(model=self.modelo, image=self.source, result=self.results[0])
        render = Image.open(f"runs/detect/predict/{results.path}")
        return render

    def to_json(self):
        results = self.results[0]
        img_size = results.orig_shape
        img_name = results.path
        array_numpy = results.boxes.cls.cpu().numpy().astype(np.int32)
        
        # Definir las clases y sus nombres correspondientes
        clases = {
            0: "Aedes",
            1: "Mosquitos",
            2: "Moscas"
        }

        # Contabilizar las clases
        conteo_clases = np.bincount(array_numpy)

        self.json_result = [{'Especie': clases[i], 'Cantidad': str(conteo_clases[i]) if i < len(conteo_clases) else str(0)} for i in range(len(clases))]
        # Crear un diccionario con los elementos necesarios
        result_dict = {
            "image": str(img_name),
            "size": str(img_size),
            "detail": self.json_result
        }
        
        # Convertir el diccionario a una cadena JSON
        result_dict = json.dumps(result_dict)

        # Convertir la cadena JSON a un objeto Python (diccionario)
        result_dict = json.loads(result_dict)

        return result_dict

    def to_dataframe(self):
        return pd.DataFrame(self.json_result)

modelo_yolo = YOLO('best.pt')

def yolo(size, iou, conf, im):
    '''Wrapper fn for gradio'''
    g = (int(size) / max(im.size))  # gain
    im = im.resize((int(x * g) for x in im.size), Image.LANCZOS) # resize with antialiasing

    print(type(im))

    source = im#Image.open(im)
    model = YOLODetect(modelo_yolo)
    results = model.predecir(source)

    result_json = model.to_json()
    print(result_json)
    result_df = model.to_dataframe()
    print(result_df)
    result_img = model.show()
    
    return result_img, result_df, result_json
    
#------------ Interface-------------

in1 = gr.Radio(['640', '1280'], label="Tamaño de la imagen", type='value')
in2 = gr.Slider(minimum=0, maximum=1, step=0.05, label='NMS IoU threshold')
in3 = gr.Slider(minimum=0, maximum=1, step=0.05, label='Umbral o  threshold')
in4 = gr.Image(type='pil', label="Original Image")

out2 = gr.Image(type="pil", label="YOLOv5")
out3 = gr.Dataframe(label="Cantidad_especie", headers=['Cantidad','Especie'], type="pandas")  
out4 = gr.JSON(label="JSON")
#-------------- Text-----
title = 'Trampas Barceló'
description = """
<p>
<center>
Sistemas de Desarrollado por Subsecretaría de Modernización del Municipio de Vicente López. Advertencia solo usar fotos provenientes de las trampas Barceló, no de celular o foto de internet.
<img src="https://www.vicentelopez.gov.ar/assets/images/logo-mvl.png" alt="logo" width="250"/>
</center>
</p>
"""
article ="<p style='text-align: center'><a href='https://docs.google.com/presentation/d/1T5CdcLSzgRe8cQpoi_sPB4U170551NGOrZNykcJD0xU/edit?usp=sharing' target='_blank'>Para mas info, clik para ir al white paper</a></p><p style='text-align: center'><a href='https://drive.google.com/drive/folders/1owACN3HGIMo4zm2GQ_jf-OhGNeBVRS7l?usp=sharing ' target='_blank'>Google Colab Demo</a></p><p style='text-align: center'><a href='https://github.com/Municipalidad-de-Vicente-Lopez/Trampa_Barcelo' target='_blank'>Repo Github</a></p></center></p>"
          
examples = [['640',0.45, 0.75,'ejemplo1.jpg'], ['640',0.45, 0.75,'ejemplo2.jpg']]

iface = gr.Interface(yolo, 
                    inputs=[in1, in2, in3, in4], 
                    outputs=[out2,out3,out4], title=title, 
					description=description, 
					article=article, 
					examples=examples, 
					analytics_enabled=False, 
					allow_flagging="manual",
					flagging_options=["Correcto", "Incorrecto", "Casi correcto", "Error", "Otro"]
                    )

iface.queue()
iface.launch(debug=True)

"""For YOLOv5 PyTorch Hub inference with **PIL**, **OpenCV**, **Numpy** or **PyTorch** inputs please see the full [YOLOv5 PyTorch Hub Tutorial](https://github.com/ultralytics/yolov5/issues/36).
## Citation
[![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686)
"""