Metrics voice completes
This commit is contained in:
parent
4b52d4aa91
commit
f7e3913d2e
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@ -8,3 +8,4 @@ databases/storage.db
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.vscode/*
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__pycache__/*
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conf/experiment_config.json
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135
apis.py
135
apis.py
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@ -1,10 +1,10 @@
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import fastapi
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from fastapi import FastAPI, Request
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from fastapi.responses import HTMLResponse
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from fastapi.responses import HTMLResponse,JSONResponse
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from pydantic import BaseModel
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import time
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from fastapi.staticfiles import StaticFiles
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from fastapi import FastAPI, Query, File, UploadFile
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from fastapi import FastAPI, Query, File, UploadFile,HTTPException
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#from fastapi.middleware.cors import CORSMiddleware
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from starlette.middleware.cors import CORSMiddleware
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import main
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@ -13,9 +13,23 @@ from databases import db
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import audioread
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import pandas as pd
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import statistics
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import hashlib
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from datetime import datetime
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import json
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pwd = os.getcwd()
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pathAud="example/audio"
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pathFact="example/factura"
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pathText="example/texto"
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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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app = FastAPI()
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#app.mount("/statics", StaticFiles(directory="statics"), name="statics")
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app.add_middleware(
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@ -30,7 +44,6 @@ class Response(BaseModel):
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"""Structure of data to querry of make post from X or article blog
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"""
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path: str = Query("", description="Style and sentiments of text")
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Trusted: str = Query("", description="Style and sentiments of text")
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model : str = Query("whisper", description="Style and sentiments of text")
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class Response1(BaseModel):
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path: str = Query("", description="path file")
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@ -55,29 +68,95 @@ class Response3(BaseModel):
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Trusted: str = Query("", description="Style and sentiments of text")
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mode : str = Query("whisper", description="Style and sentiments of text")
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#Funcionales
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@app.get("/addTrusted")
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@app.post("/addTrusted")
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def addTrusted(response:Response3):
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"""Api to add information of Trusted data
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Args:
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response (Response3): 3 params:
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path : path of archive on system if is a file OR text if is text.
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Trusted : information Trusted or better information in a process.
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mode: llm_compra,llm_factura,llm_generaciontexto,llm_rag,ocr,voice,
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Returns:
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_type_: _description_
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"""
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path=response.path
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Trusted=response.Trusted
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mode=response.mode
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file_stats = os.stat(path)
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size=file_stats.st_size / (1024 * 1024)
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if mode=="voice":
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last_modified=datetime.now()
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if mode not in mode_list.keys():
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return JSONResponse(
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status_code=404,
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content={"content": "mode no found" }
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)
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if mode == "llm_factura" or mode == "ocr" or mode == "voice":
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if not os.path.isfile(path):
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return JSONResponse(
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status_code=404,
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content={"content": "file no found" }
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)
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if mode_list[mode]=="texto":
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hash1 = hashlib.sha256(path.encode()).hexdigest()+".txt"
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f = open("example/texto/"+hash1, "w")
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f.write(path)
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f.close()
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path=pwd+"/"+pathText+hash1
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length=len(Trusted)
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size=0
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duration=0
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elif mode_list[mode]=="factura":
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file_stats = os.stat(path)
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size=file_stats.st_size / (1024 * 1024)
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length=0
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duration=0
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elif mode_list[mode]=="audio":
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with audioread.audio_open(path) as f:
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duration = f.duration
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else:
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duration = 0
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if db(db.trusted.path == path and db.trusted.mode == mode).count()==0:
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db.trusted.insert(path=path,trusted=Trusted,mode=mode,size=size,duration =duration )
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length=0
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size=0
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if db((db.trusted.path == path)&(db.trusted.mode == mode)).count()==0:
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db.trusted.insert(path=path,trusted=Trusted,mode=mode,size=size,duration=duration,last_modified=last_modified,length=length )
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db.commit()
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return "Add %s in mode %s"%(path,mode)
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else:
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db(db.trusted.path == path and db.trusted.mode == mode).update(trusted=Trusted,size=size,duration =duration )
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item=db((db.trusted.path == path)&(db.trusted.mode == mode)).select().last()
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modification_count=item.modification_count + 1
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db((db.trusted.path == path)&(db.trusted.mode == mode)).update(trusted=Trusted,size=size,duration =duration,length=length,last_modified=last_modified,modification_count= modification_count)
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db.commit()
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return "Update %s in mode %s"%(path,mode)
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@app.get("/EvalVoice")
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@app.post("/EvalVoice")
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def EvalVoice(response:Response):
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path=response.path
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model=response.model
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if db((db.trusted.path == path ) & ( db.trusted.mode == "voice")).count()==0:
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return JSONResponse(
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status_code=404,
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content={"content": "Trusted no found" }
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)
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Trusted=db((db.trusted.path == path ) & ( db.trusted.mode == "voice")).select().last().trusted
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print(Trusted)
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if model=="whisper":
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Sal=main.EvalWhisper(path,Trusted)
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else:
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Sal=main.EvalVosk(path,Trusted)
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Sal["last_modified"]=datetime.now()
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if db(db.analitic_voice.path == Sal["path"] and db.analitic_voice.model == Sal["model"]).count()==0:
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db.analitic_voice.insert(**Sal)
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db.commit()
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else:
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db(db.analitic_voice.path == Sal["path"] and db.analitic_voice.model == Sal["model"]).update(similarity= Sal["similarity"],similaritypartial= Sal["similaritypartial"],last_modified=Sal["last_modified"])
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db.commit()
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return Sal
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def list2tablehtml(listdata,model):
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html="""<h2>Table of {0}</h2>
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<table style="width:100%">
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@ -223,30 +302,6 @@ display:flex;
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return HTMLResponse(content=html, status_code=200)
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@app.get("/EvalVoice")
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@app.post("/EvalVoice")
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def EvalVoice(response:Response):
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path=response.path
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Trusted=response.Trusted
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model=response.model
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if Trusted=="":
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row=db(db.trusted.path == path and db.trusted.mode == "voice").select().first()
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try:
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Trusted=row.trusted
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except:
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pass
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if model=="whisper":
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Sal=main.EvalWhisper(path,Trusted)
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else:
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Sal=main.EvalVosk(path,Trusted)
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if db(db.analitic_voice.path == Sal["path"] and db.analitic_voice.model == Sal["model"]).count()==0:
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db.analitic_voice.insert(**Sal)
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db.commit()
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else:
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db(db.analitic_voice.path == Sal["path"] and db.analitic_voice.model == Sal["model"]).update(similarity= Sal["similarity"],similaritypartial= Sal["similaritypartial"])
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db.commit()
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return Sal
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@app.get("/EvalFact")
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@app.post("/EvalFact")
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@ -334,9 +389,7 @@ def EvalVoicehtml():
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</select>
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<br>
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<input type="text" id="texto2" placeholder="Trusted">
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<br>
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<select id="texto3">
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<select id="texto2">
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<option value="whisper">whisper</option>
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<option value="vosk">vosk</option>
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</select>
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@ -348,11 +401,9 @@ def EvalVoicehtml():
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function enviarPeticion() {
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const texto1 = document.getElementById('texto1').value;
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const texto2 = document.getElementById('texto2').value;
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const texto3 = document.getElementById('texto3').value;
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const datos = {
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path: texto1,
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Trusted: texto2,
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model: texto3
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model: texto2
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};
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fetch('/EvalVoice', {
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54
databases.py
54
databases.py
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@ -5,8 +5,11 @@ db.define_table(
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Field("path"),
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Field("mode"),
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Field("trusted"),
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Field("duration",type="double"),
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Field("size",type="double")
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Field("duration",type="double",default=0),#audio
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Field("sizeMB",type="double",default=0),# audio,factura
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Field("length",type="integer",default=0),#texto
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Field('last_modified', 'datetime'),
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Field('modification_count', 'integer', default=0)
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)
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db.define_table(
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"analitic_voice",
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@ -16,7 +19,8 @@ db.define_table(
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Field("time", type="double"),
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Field("path"),
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Field("similarity", type="double"),
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Field("similaritypartial", type="double")
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Field("similaritypartial", type="double"),
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Field('last_modified', 'datetime')
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)
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db.define_table(
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@ -28,16 +32,54 @@ db.define_table(
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Field("path"),
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Field("similarity", type="double"),
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Field("similaritypartial", type="double"),
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Field("jsonok" ,type="integer")
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Field("jsonok" ,type="integer"),
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Field('last_modified', 'datetime')
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)
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db.define_table(
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"analitic_llm",
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"analitic_llm_compra",
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Field("content"),
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Field("trusted"),
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Field("model"),
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Field("time", type="double"),
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Field("path"),
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Field("similarity", type="double"),
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Field("similaritypartial", type="double")
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Field("similaritypartial", type="double"),
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Field('last_modified', 'datetime')
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)
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db.define_table(
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"analitic_llm_factura",
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Field("content"),
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Field("trusted"),
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Field("model"),
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Field("time", type="double"),
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Field("path"),
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Field("similarity", type="double"),
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Field("similaritypartial", type="double"),
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Field('last_modified', 'datetime')
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)
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db.define_table(
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"analitic_llm_generaciontexto",
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Field("content"),
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Field("trusted"),
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Field("model"),
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Field("time", type="double"),
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Field("path"),
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Field("similarity", type="double"),
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Field("similaritypartial", type="double"),
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Field('last_modified', 'datetime')
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)
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db.define_table(
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"analitic_llm_rag",
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Field("content"),
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Field("trusted"),
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Field("model"),
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Field("time", type="double"),
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Field("path"),
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Field("similarity", type="double"),
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Field("similaritypartial", type="double"),
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Field('last_modified', 'datetime')
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)
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@ -0,0 +1,153 @@
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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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pwd = os.getcwd()
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HTML = os.path.join(pwd,"html", "index.html")
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file_read = codecs.open(HTML, "r", "utf-8")
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index = file_read.read()
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html_page_index = Html(index)
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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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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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datafiles={}
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for row in db().select(db.analitic_voice.ALL):
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datafiles[row.id]=row.as_dict()
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datafiles=pd.DataFrame(datafiles).T
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html="""
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<taipy:table>{data_voice}</taipy:table>
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<taipy:table filter=True>{data_files_voice}</taipy:table>
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"""
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return html,data,datafiles
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html_page_getmetricsvoice,data_voice,data_files_voices=html_getmetricvoice()
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def evalVoicehtml():
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pathAud="example/audio"
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dir_list = os.listdir(pathAud)
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Sal=""
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t=1
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for i in dir_list:
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temp="""<option value="%s">Opción %s, %s</option>
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"""%(str(pwd+"/"+pathAud+"/"+i),str(t),str(i))
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Sal=Sal+temp
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t=t+1
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html="""<!DOCTYPE html>
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<html lang="es">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Evaluacion de modelos voice2txt</title>
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<style>
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body {
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font-family: Arial, sans-serif;
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margin: 20px;
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}
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input, button {
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margin: 10px 0;
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padding: 5px;
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}
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#respuesta {
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margin-top: 20px;
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padding: 10px;
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border: 1px solid #ccc;
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background-color: #f9f9f9;
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}
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</style>
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</head>
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<body>
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<h1>Petición POST a API</h1>
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<select id="texto1">
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%s
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</select>
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<br>
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<select id="texto2">
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<option value="whisper">whisper</option>
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<option value="vosk">vosk</option>
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</select>
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<br>
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<button onclick="enviarPeticion()">Enviar petición</button>
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<div id="respuesta"></div>
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<script>
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function enviarPeticion() {
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const texto1 = document.getElementById('texto1').value;
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const texto2 = document.getElementById('texto2').value;
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const datos = {
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path: texto1,
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model: texto2
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};
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fetch('/EvalVoice', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json'
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},
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body: JSON.stringify(datos)
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})
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.then(response => response.json())
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.then(data => {
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document.getElementById('respuesta').innerHTML = JSON.stringify(data, null, 2);
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})
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.catch(error => {
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document.getElementById('respuesta').innerHTML = 'Error: ' + error;
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});
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}
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</script>
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</body>
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</html>
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"""%(Sal)
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return html
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html_page_evalvoice = Html(evalVoicehtml())
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HTML = os.path.join(pwd,"html", "index.html")
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file_read = codecs.open(HTML, "r", "utf-8")
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index = file_read.read()
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html_page_index = Html(index)
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data=pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
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pages = {
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"/": html_page_index ,
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"getmetricsvoice": Html(html_page_getmetricsvoice),
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"evalvoice":html_page_evalvoice
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}
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app = Gui(pages=pages)
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if __name__=="__main__":
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app.run(use_reloader=True,port=7882, change_delay=1600)#state.imageActive2,
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@ -0,0 +1,22 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>CIDITEL AI Playground</title>
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<link rel="stylesheet" tyle="text-decoration: none;" href="https://cdn.jsdelivr.net/npm/bulma@0.9.4/css/bulma.min.css">
|
||||
<link rel="stylesheet" tyle="text-decoration: none;" href="https://fonts.googleapis.com/css2?family=Material+Symbols+Outlined:opsz,wght,FILL,GRAD@24,400,0,0" />
|
||||
<link rel="shortcut icon" tyle="text-decoration: none;" href="statics/icons/favicon.svg" type="image/x-icon">
|
||||
<link rel="stylesheet" tyle="text-decoration: none;" href="statics/css/style.css">
|
||||
<link rel="stylesheet" tyle="text-decoration: none;" href="statics/css/media-queries.css">
|
||||
<link rel="preconnect" tyle="text-decoration: none;" href="https://fonts.googleapis.com">
|
||||
<link rel="preconnect" tyle="text-decoration: none;" href="https://fonts.gstatic.com" crossorigin>
|
||||
<link tyle="text-decoration: none;" href="https://fonts.googleapis.com/css2?family=Kanit:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,500;1,600;1,700;1,800;1,900&display=swap" rel="stylesheet">
|
||||
</head>
|
||||
<body>
|
||||
|
||||
|
||||
<taipy:table>{data}</taipy:table>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
Loading…
Reference in New Issue