import os
import re
import json
import pandas as pd
import gradio as gr
import torch
from PIL import Image
from PIL import ImageFile
import logging

# Configurar el logging para escribir en un archivo log
logging.basicConfig(
    filename="output.log",
    level=logging.INFO,
    format="%(asctime)s - %(levelname)s - %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S"
)

# 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
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True, autoshape=True, trust_repo=True)

def to_json(results):
    detail = []

    results_df = to_dataframe(results)

    for index, row in results_df.iterrows():
        item = {
            "quantity": row['Cantidad'],
            "description": row['Especie']
        }
        detail.append(item)

    data = {
        "image": results.files[0],
        "size": f"{results.s[2]}x{results.s[3]}",
        "detail": detail
    }

    return data, results_df

def to_dataframe(results):
    labels_map = {
        'Aedes': "Aedes",
        'Mosquito': "Mosquito",
        'Mosca': "Mosca",
    }
    labels = list(labels_map.keys())

    columns_name = {'class': 'Cantidad', 'name': 'Especie'}
    results_df = results.pandas().xyxy[0][['class','name']].groupby('name').count().reset_index().rename(columns=columns_name)
    results_df = pd.merge(pd.DataFrame(labels, columns=['Especie']), results_df, how='left', on='Especie').fillna(0)
    results_df['Cantidad'] = results_df['Cantidad'].astype(int)
    results_df['Especie'] = results_df['Especie'].map(labels_map)
    return results_df

def yolo(size, iou, conf, im):
    try:
        '''Wrapper fn for gradio'''
        g = (int(size) / max(im.size))  # gain
        im = im.resize(tuple(int(x * g) for x in im.size), Image.LANCZOS) # resize with antialiasing
        
        model.iou = iou
        model.conf = conf
        
        results2 = model(im)  # inference
        results2.render()  # updates results.imgs with boxes and labels
        lista, lista2 = to_json(results2)
        logging.info(f"Imagen procesada satisfactoriamente: {lista}")
        return Image.fromarray(results2.ims[0]), lista2, lista
    except Exception as err:
        logging.error(f"Error durante la predicción: {err}")
        return None, None, None

#------------ Interface-------------
in1 = gr.inputs.Radio(['640', '1280'], label="Tamaño de la imagen", default='640', type='value')
in2 = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.25, label='NMS IoU threshold')
in3 = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.50, label='Umbral o  threshold')
in4 = gr.inputs.Image(type='pil', label="Original Image")

out2 = gr.outputs.Image(type="pil", label="YOLOv5")
out3 = gr.outputs.Dataframe(label="Cantidad_especie", headers=['Cantidad','Especie'], type="pandas")
out4 = gr.outputs.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',0.25, 0.5,'ejemplo1.jpg'], ['640',0.25, 0.5,'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"],
                        #flagging_callback=hf_writer
                    )

#iface.queue(default_concurrency_limit=5)
iface.launch(enable_queue=True, debug=True, server_port=7860, server_name='0.0.0.0')