281 lines
10 KiB
Python
281 lines
10 KiB
Python
from taipy.gui import Gui
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import hashlib
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import json
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import codecs, os
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from taipy.gui import Html
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import pandas as pd
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import requests
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import statistics
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from databases import db
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import time
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pwd = os.getcwd()
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def extractConfig(nameModel="SystemData",relPath=os.path.join(pwd,"conf/experiment_config.json"),dataOut="keyantrophics"):
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configPath=os.path.join(os.getcwd(),relPath)
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with open(configPath, 'r', encoding='utf-8') as file:
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config = json.load(file)[nameModel]
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Output= config[dataOut]
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return Output
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mode_list=extractConfig(nameModel="SystemData",dataOut="mode_list")
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def getmetricvoice(model):
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rows = db(db.analitic_voice.model==model).select()
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rows_list = rows.as_list()
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data=pd.DataFrame(rows_list)
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durationL=list()
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for i in rows_list:
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durationL.append(db(db.trusted.path == i["path"] ).select().last().duration)
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duration=statistics.mean(durationL)
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time=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['time'].values[0]
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similarity=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similarity'].values[0]
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similaritypartial=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similaritypartial'].values[0]
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efectivetime=time/duration
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return ({"model":model,"duration":duration,"time":time,"similarity":similarity,"similaritypartial":similaritypartial,"efectivetime":efectivetime})
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def html_getmetricvoice():
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models=list()
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t=time.time()
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for row in db().select(db.analitic_voice.model, distinct=True):
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models.append(row.model)
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data={}
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for model in models:
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data[model]=getmetricvoice(model)
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data=pd.DataFrame(data).T
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data_files={}
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for row in db().select(db.analitic_voice.ALL):
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data_files[row.id]=row.as_dict()
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data_files=pd.DataFrame(data_files).T
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#table = pd.pivot_table(data_files, values=['path', 'similarity','similaritypartial'], index=['path'],
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#columns=['model'], aggfunc="sum")
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html="""
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<h1>Data general de los modelos</h1>
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<taipy:table>{data_voice}</taipy:table>
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<h1>Data de cada muestra</h1>
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<taipy:table filter=True>{data_files_voice}</taipy:table>
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"""
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#<taipy:chart mode="markers" x="x" y[1]="time" y[2]="similarity">{data_files_voice}</taipy:chart>
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return html,data,data_files
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def getmetricllm_compra(model):
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rows = db(db.analitic_llm_compra.model==model).select()
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rows_list = rows.as_list()
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data=pd.DataFrame(rows_list)
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#durationL=list()
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#for i in rows_list:
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#durationL.append(db(db.trusted.path == i["path"] ).select().last().duration)
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#duration=statistics.mean(durationL)
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time=pd.pivot_table(data,values=['time'],index="model")['time'].values[0]
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relevance=pd.pivot_table(data,values=["relevance"],index="model")['relevance'].values[0]
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bias=pd.pivot_table(data,values=["bias"],index="model")['bias'].values[0]
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toxic=pd.pivot_table(data,values=["toxic"],index="model")['toxic'].values[0]
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correctness=pd.pivot_table(data,values=["correctness"],index="model")['correctness'].values[0]
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#similarity=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similarity'].values[0]
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#similaritypartial=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similaritypartial'].values[0]
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#efectivetime=time/duration
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return ({"model":model,"time":time,"relevance":relevance,"bias":bias,"toxic":toxic,"correctness":correctness})
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def html_getmetricllm_compra():
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models=list()
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t=time.time()
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for row in db().select(db.analitic_llm_compra.model, distinct=True):
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models.append(row.model)
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data={}
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for model in models:
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data[model]=getmetricllm_compra(model)
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data=pd.DataFrame(data).T
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data_files={}
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for row in db().select(db.analitic_llm_compra.ALL):
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data_files[row.id]=row.as_dict()
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data_files=pd.DataFrame(data_files).T
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#table = pd.pivot_table(data_files, values=['path', 'similarity','similaritypartial'], index=['path'],
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#columns=['model'], aggfunc="sum")
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html="""
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<h1>Data general de los modelos</h1>
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<taipy:table>{data_llm_compra}</taipy:table>
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<h1>Data de cada muestra</h1>
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<taipy:table filter=True >{data_files_llm_compra}</taipy:table>
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"""
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#<taipy:chart mode="markers" x="x" y[1]="time" y[2]="similarity">{data_files_voice}</taipy:chart>
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return html,data,data_files
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def getmetricllm_generaciontexto(model):
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rows = db(db.analitic_llm_generaciontexto.model==model).select()
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rows_list = rows.as_list()
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data=pd.DataFrame(rows_list)
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#durationL=list()
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#for i in rows_list:
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#durationL.append(db(db.trusted.path == i["path"] ).select().last().duration)
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#duration=statistics.mean(durationL)
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time=pd.pivot_table(data,values=['time'],index="model")['time'].values[0]
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relevance=pd.pivot_table(data,values=["relevance"],index="model")['relevance'].values[0]
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bias=pd.pivot_table(data,values=["bias"],index="model")['bias'].values[0]
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toxic=pd.pivot_table(data,values=["toxic"],index="model")['toxic'].values[0]
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correctness=pd.pivot_table(data,values=["correctness"],index="model")['correctness'].values[0]
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#similarity=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similarity'].values[0]
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#similaritypartial=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similaritypartial'].values[0]
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#efectivetime=time/duration
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return ({"model":model,"time":time,"relevance":relevance,"bias":bias,"toxic":toxic,"correctness":correctness})
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def html_getmetricllm_generaciontexto():
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models=list()
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t=time.time()
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for row in db().select(db.analitic_llm_generaciontexto.model, distinct=True):
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models.append(row.model)
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data={}
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for model in models:
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data[model]=getmetricllm_generaciontexto(model)
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data=pd.DataFrame(data).T
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data_files={}
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for row in db().select(db.analitic_llm_generaciontexto.ALL):
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data_files[row.id]=row.as_dict()
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data_files=pd.DataFrame(data_files).T
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#table = pd.pivot_table(data_files, values=['path', 'similarity','similaritypartial'], index=['path'],
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#columns=['model'], aggfunc="sum")
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html="""
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<h1>Data general de los modelos</h1>
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<taipy:table>{data_llm_generaciontexto}</taipy:table>
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<h1>Data de cada muestra</h1>
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<taipy:table filter=True >{data_files_llm_generaciontexto}</taipy:table>
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"""
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#<taipy:chart mode="markers" x="x" y[1]="time" y[2]="similarity">{data_files_voice}</taipy:chart>
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return html,data,data_files
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def getmetricllm_factura(model):
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rows = db(db.analitic_llm_factura.model==model).select()
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rows_list = rows.as_list()
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data=pd.DataFrame(rows_list)
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#durationL=list()
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#for i in rows_list:
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#durationL.append(db(db.trusted.path == i["path"] ).select().last().duration)
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#duration=statistics.mean(durationL)
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time=pd.pivot_table(data,values=['time'],index="model")['time'].values[0]
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relevance=pd.pivot_table(data,values=["relevance"],index="model")['relevance'].values[0]
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bias=pd.pivot_table(data,values=["bias"],index="model")['bias'].values[0]
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toxic=pd.pivot_table(data,values=["toxic"],index="model")['toxic'].values[0]
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correctness=pd.pivot_table(data,values=["correctness"],index="model")['correctness'].values[0]
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#similarity=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similarity'].values[0]
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#similaritypartial=pd.pivot_table(data,values=['time','similarity', 'similaritypartial'],index="model")['similaritypartial'].values[0]
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#efectivetime=time/duration
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return ({"model":model,"time":time,"relevance":relevance,"bias":bias,"toxic":toxic,"correctness":correctness})
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def html_getmetricllm_factura():
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models=list()
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t=time.time()
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for row in db().select(db.analitic_llm_factura.model, distinct=True):
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models.append(row.model)
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data={}
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for model in models:
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data[model]=getmetricllm_factura(model)
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data=pd.DataFrame(data).T
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data_files={}
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for row in db().select(db.analitic_llm_factura.ALL):
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data_files[row.id]=row.as_dict()
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data_files=pd.DataFrame(data_files).T
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#table = pd.pivot_table(data_files, values=['path', 'similarity','similaritypartial'], index=['path'],
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#columns=['model'], aggfunc="sum")
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html="""
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<h1>Data general de los modelos</h1>
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<taipy:table>{data_llm_factura}</taipy:table>
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<h1>Data de cada muestra</h1>
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<taipy:table filter=True >{data_files_llm_factura}</taipy:table>
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"""
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#<taipy:chart mode="markers" x="x" y[1]="time" y[2]="similarity">{data_files_voice}</taipy:chart>
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return html,data,data_files
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def on_init(state):
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state.html_page_getmetricsvoice,state.data_voice,state.data_files_voice=html_getmetricvoice()
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state.html_page_getmetricsllm_compra,state.data_llm_compra,state.data_files_llm_compra=html_getmetricllm_compra()
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state.html_page_getmetricsllm_generaciontexto,state.data_llm_generaciontexto,state.data_files_llm_generaciontexto=html_getmetricllm_generaciontexto()
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state.html_page_getmetricsllm_factura,state.data_llm_factura,state.data_files_llm_factura=html_getmetricllm_factura()
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pass
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html_page_getmetricsvoice,data_voice,data_files_voice=html_getmetricvoice()
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html_page_getmetricsllm_compra,data_llm_compra,data_files_llm_compra=html_getmetricllm_compra()
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html_page_getmetricsllm_generaciontexto,data_llm_generaciontexto,data_files_llm_generaciontexto=html_getmetricllm_generaciontexto()
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html_page_getmetricsllm_factura,data_llm_factura,data_files_llm_factura=html_getmetricllm_factura()
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# mode="voice"
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# modetypedata="audio"
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# file="id2"
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# def changemenu(mode):
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# if mode_list[mode]=="audio":
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# pathori="example/audio"
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# if mode_list[mode]=="factura":
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# pathori="example/factura"
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# if mode_list[mode]=="texto":
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# pathori="example/texto"
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# seltypedata=mode_list[mode]
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# dir_list = os.listdir(pathori)
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# return pathori,seltypedata,dir_list
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pages = {
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"getmetricsvoice": Html(html_page_getmetricsvoice),
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"getmetricsllm_compra": Html(html_page_getmetricsllm_compra),
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"getmetricsllm_generaciontexto": Html(html_page_getmetricsllm_generaciontexto),
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"getmetricsllm_factura": Html(html_page_getmetricsllm_factura)
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}
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app = Gui(pages=pages)
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app.on_init=on_init
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if __name__=="__main__":
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app.run(use_reloader=True,port=7882)#state.imageActive2, |