from taipy.gui import Gui
import hashlib
import json
import codecs, os
from taipy.gui import Html
import pandas as pd
import requests
import statistics
from databases import db
import time
pwd = os.getcwd()

def extractConfig(nameModel="SystemData",relPath=os.path.join(pwd,"conf/experiment_config.json"),dataOut="keyantrophics"):
    configPath=os.path.join(os.getcwd(),relPath)
    with open(configPath, 'r', encoding='utf-8') as file:
        config = json.load(file)[nameModel]
    Output= config[dataOut]
    return Output
mode_list=extractConfig(nameModel="SystemData",dataOut="mode_list")


def getmetricvoice(model):
    rows = db(db.analitic_voice.model==model).select()
    rows_list = rows.as_list()
    data=pd.DataFrame(rows_list)
    durationL=list()
    for i in rows_list:
        durationL.append(db(db.trusted.path == i["path"] ).select().last().duration)
    duration=statistics.mean(durationL)
    time=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['time'].values[0]
    similarity=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similarity'].values[0]
    similaritypartial=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similaritypartial'].values[0]
    efectivetime=time/duration
    return ({"model":model,"duration":duration,"time":time,"similarity":similarity,"similaritypartial":similaritypartial,"efectivetime":efectivetime})

def html_getmetricvoice():
    models=list()
    t=time.time()
    for row in db().select(db.analitic_voice.model, distinct=True):
        models.append(row.model)
    data={}
    for model in models:
        data[model]=getmetricvoice(model)
    data=pd.DataFrame(data).T
    data_files={}
    for row in db().select(db.analitic_voice.ALL):
        data_files[row.id]=row.as_dict()
    #print(datafiles)
    data_files=pd.DataFrame(data_files).T

    #table = pd.pivot_table(data_files, values=['path', 'similarity','similaritypartial'], index=['path'],
                       #columns=['model'], aggfunc="sum")
    #print(table,table.columns)

    html="""
    <h1>Data general de los modelos</h1>
    <taipy:table>{data_voice}</taipy:table>
    <h1>Data de cada muestra</h1>
    <taipy:table filter=True>{data_files_voice}</taipy:table>
    

    """
    #<taipy:chart mode="markers" x="x" y[1]="time" y[2]="similarity">{data_files_voice}</taipy:chart>
    print(time.time()-t)
    return html,data,data_files

def getmetricllm_compra(model):
    rows = db(db.analitic_llm_compra.model==model).select()
    rows_list = rows.as_list()
    data=pd.DataFrame(rows_list)
    durationL=list()
    for i in rows_list:
        durationL.append(db(db.trusted.path == i["path"] ).select().last().duration)
    duration=statistics.mean(durationL)
    time=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['time'].values[0]
    similarity=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similarity'].values[0]
    similaritypartial=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similaritypartial'].values[0]
    efectivetime=time/duration
    return ({"model":model,"duration":duration,"time":time,"similarity":similarity,"similaritypartial":similaritypartial,"efectivetime":efectivetime})

def html_getmetricllm_compra():
    models=list()
    t=time.time()
    for row in db().select(db.analitic_llm_compra.model, distinct=True):
        models.append(row.model)
    data={}
    for model in models:
        data[model]=getmetricllm_compra(model)
    data=pd.DataFrame(data).T
    data_files={}
    for row in db().select(db.analitic_llm_compra.ALL):
        data_files[row.id]=row.as_dict()
    #print(datafiles)
    data_files=pd.DataFrame(data_files).T

    #table = pd.pivot_table(data_files, values=['path', 'similarity','similaritypartial'], index=['path'],
                       #columns=['model'], aggfunc="sum")
    #print(table,table.columns)

    html="""
    <h1>Data general de los modelos</h1>
    <taipy:table>{data_voice}</taipy:table>
    <h1>Data de cada muestra</h1>
    <taipy:table filter=True>{data_files_voice}</taipy:table>
    

    """
    #<taipy:chart mode="markers" x="x" y[1]="time" y[2]="similarity">{data_files_voice}</taipy:chart>
    print(time.time()-t)
    return html,data,data_files


def on_init(state):
    state.html_page_getmetricsvoice,state.data_voice,state.data_files_voice=html_getmetricvoice()
    pass
    
    

html_page_getmetricsvoice,data_voice,data_files_voice=html_getmetricvoice()
# mode="voice"
# modetypedata="audio"
# file="id2"
# def changemenu(mode):
#     if mode_list[mode]=="audio":
#         pathori="example/audio"
#     if mode_list[mode]=="factura":
#         pathori="example/factura"
#     if mode_list[mode]=="texto":
#         pathori="example/texto"
#     seltypedata=mode_list[mode]  
#     dir_list = os.listdir(pathori)
    
#     return pathori,seltypedata,dir_list




data=pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})

pages = {
  "getmetricsvoice": Html(html_page_getmetricsvoice),
}

app = Gui(pages=pages)
app.on_init=on_init
if __name__=="__main__":
    app.run(use_reloader=True,port=7882)#state.imageActive2,