Spaces:
Runtime error
Runtime error
File size: 14,090 Bytes
b654a0b 1dcd702 b654a0b 0c44394 b654a0b b4bfa74 b654a0b e936903 b4bfa74 e936903 b4bfa74 b654a0b df9870a b654a0b e3f7f19 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 |
import gradio as gr
import json
from flask import jsonify
from sentence_transformers import SentenceTransformer, InputExample, util
from codeScripts.utils import save_json, load_json, create_file_path, remove
from plentas import Plentas
import pandas as pd
import zipfile
import os
import shutil
from datetime import datetime
import tablib
from pathlib import Path
def Main(uploadedFile, txtFileInput, orthographyPercentage, syntaxPercentage, semanticPercentage, studentsRange):
error = ""
excelPath = None
copySpanishDictionaries()
try:
if not txtFileInput:
error="Por favor seleccione un archivo con las preguntas y respuestas"
return [error, excelPath]
else:
txtFileInput = txtFileInput.name
configuration = readQATextFile(txtFileInput)
configuration["ortographyPercentage"] = float(orthographyPercentage)
configuration["syntaxPercentage"] = float(syntaxPercentage)
configuration["semanticPercentage"] = float(semanticPercentage)
if studentsRange == "":
studentsRange = "All"
configuration["students"] = studentsRange
if not uploadedFile:
error="Por favor seleccione el .zip con las respuestas de los alumnos"
return [error, excelPath]
else:
uploadedFilePath = uploadedFile.name
config_json = load_json("configV2.json")
answersDict = None
try:
answersDict = answersTodict(uploadedFilePath)
except Exception as ex:
error = "Error in answersTodict: " + str(ex)
return [error, excelPath]
teacherJson = None
try:
teacherJson = createTeacherJson(configuration)
except Exception as ex:
error = "Error in createTeacherJson: " + str(ex)
return [error, excelPath]
try:
# #configuring plentas methodology
response = Plentas(config_json[0], [answersDict, teacherJson])
except Exception as ex:
error = "Error configuring: " + str(ex)
return [error, excelPath]
try:
# # #overwriting the custom settings for the settings from the api
response.setApiSettings(configuration)
except Exception as ex:
error = "Error setting: " + str(ex)
return [error, excelPath]
try:
print("Processing!")
modelResult = response.processApiData()
except Exception as ex:
error = "Error processing: " + str(ex)
return [error, excelPath]
# modelJson = json.dumps(modelResult)
print(modelResult)
excelPath = exportResultToExcelFile(modelResult)
except Exception as ex:
error = "Error exporting to Excel: " + str(e)
return [error, excelPath]
def exportResultToExcelFile(modelResult):
try:
excelData = []
studentsArray = modelResult[0]
index = 0
for item in studentsArray:
#print("ITEM - " + str(item))
studentData = item[index]
excelData.append(studentData)
index+= 1
#tableResults = tablib.Dataset(headers=('ID', 'SimilitudSpacy', 'SimilitudBert', 'NotaSemanticaSpacy', 'NotaSemanticaBert', 'NotaSintaxis', 'NotaOrtografia','NotaTotalSpacy','NotaTotalBert','Feedback'))
tableResults = tablib.Dataset(headers=('ID', 'SumaTotalSpacy', 'SumaTotaldBert', 'NotaSemanticaSpacy', 'NotaSemanticaBert', 'NotaSintaxis', 'NotaOrtografia','NotaTotalSpacy','NotaTotalBert','Feedback'))
tableResults.json=json.dumps(excelData)
tableExport=tableResults.export('xlsx')
outputFilePath = './output/' + str(datetime.now().microsecond) + '_plentas_output.xlsx'
# outputFilePath = './output/plentas_output.xlsx'
with open(outputFilePath, 'wb') as f: # open the xlsx file
f.write(tableExport) # write the dataset to the xlsx file
f.close()
except Exception as ex:
print("Error exportando Excel:"+ str(e))
return outputFilePath
def copySpanishDictionaries():
try:
shutil.copy("./assets/hunspell_dictionaries/es_ES/es_ES.aff", "/home/user/.local/lib/python3.9/site-packages/hunspell/dictionaries/es_ES.aff")
shutil.copy("./assets/hunspell_dictionaries/es_ES/es_ES.dic", "/home/user/.local/lib/python3.9/site-packages/hunspell/dictionaries/es_ES.dic")
except Exception as ex:
print("Error copying dictionaries" + str(ex))
def readQATextFile(qaTextFilePath):
configuration = {}
f = open(qaTextFilePath, 'r')
lines = f.readlines()
count = 0
qCount=1
q = ""
a = ""
while count < len(lines):
if q == "" or q == "\n":
q = lines[count]
count += 1
continue
if a == "" or a == "\n":
a = lines[count]
count += 1
if q != "" and a != "":
configuration["minip" + str(qCount)] = q
configuration["minir" + str(qCount)] = a
qCount += 1
q = ""
a = ""
return configuration
def createTeacherJson(configuration):
"""
This function extracts the information about the subquestions and subanswers and puts them in the correct format.
Inputs:
config: The configured info from the api.
Outputs:
teachersJson: The generated dictionary with the subquestions.
"""
teachersJson = {"enunciado": "", "minipreguntas":[], "keywords":""}
#5 is the maximum number of permitted subquestions in the configuration2 page
for i in range(5):
try:
teachersJson["minipreguntas"].append({
"minipregunta": configuration["minip" + str(i+1)],
"minirespuesta": configuration["minir" + str(i+1)]
})
except:
break
return teachersJson
def extractZipData(ruta_zip):
"""
This function extracts the students's answers from the zip file (the one the teacher has in the task section).
Inputs:
ruta_zip: The path inherited from answersTodict
"""
#defining the path where the extracted info is to be stored
ruta_extraccion = create_file_path("StudentAnswers/", doctype= 1)
#extracting the info
archivo_zip = zipfile.ZipFile(ruta_zip, "r")
try:
archivo_zip.extractall(pwd=None, path=ruta_extraccion)
#archivo_zip.extract(pwd=None, path=ruta_extraccion)
except:
pass
archivo_zip.close()
def removeHtmlFromString(string):
"""
This function removes the html tags from the student's response.
Inputs:
-string: The student's response
Outputs:
-new_string: The filtered response
"""
string = string.encode('utf-8', 'replace')
string = string.decode('utf-8', 'replace')
new_string = ""
skipChar = 0
for char in string:
if char == "<":
skipChar = 1
elif char == ">":
skipChar = 0
else:
if not skipChar:
new_string = new_string+char
new_string = new_string.encode('utf-8', 'replace')
new_string = new_string.decode('utf-8', 'replace')
return new_string
def answersTodict(zip_path):
"""
This function extracts the students's answers and stacks them in one specific format so that it can be processed next.
Inputs:
ruta_zip: The path where the zip file is stored
Outputs:
studentAnswersDict: The dictionary with all the responses
"""
# path
remove('api/StudentAnswers/')
#extracting the data
extractZipData(zip_path)
studentAnswersDict = []
indx2=0
#stacking the information of each extracted folder
for work_folder in os.listdir(create_file_path("StudentAnswers/", doctype= 1)):
#print("work_folder: " + work_folder)
for student, indx in zip(os.listdir(create_file_path("StudentAnswers/" + work_folder, doctype= 1)), range(len(os.listdir(create_file_path("StudentAnswers/" + work_folder, doctype= 1))))):
student_name = student.split("(")
student_name = student_name[0]
print("student: " + str(student) + " - index: " + str(indx))
try:
#opening the file
#fichero1 = create_file_path("StudentAnswers/" + work_folder + "/" + student+ "/" + 'Adjuntos del envio/', doctype= 1)
fichero1 = create_file_path("StudentAnswers/" + work_folder + "/" + student + "/" + student+'_submissionText.html', doctype= 1)
#where the actual response is
if os.path.exists(fichero1):
print("Fichero: "+str(fichero1))
#fichero = open(create_file_path("StudentAnswers/" + work_folder + "/" + student + "/" + 'Adjuntos del envio/Respuesta enviada', doctype= 1), encoding='utf-8')
#fichero = create_file_path("StudentAnswers/" + work_folder + "/" + student + "/" + 'Adjuntos del envio/Respuesta enviada', doctype= 1)
#if os.path.exists(fichero):
# fichero = open(create_file_path("StudentAnswers/" + work_folder + "/" + student + "/" + 'Adjuntos del envio/Respuesta enviada', doctype= 1), encoding='utf-8')
#else:
fichero = open(create_file_path("StudentAnswers/" + work_folder + "/" + student + "/" + student+'_submissionText.html', doctype= 1), encoding='utf-8')
print("fichero abierto")
#reading it
lineas = fichero.readlines()
textoFichero = ""
if (len(lineas) > 0):
textoFichero = lineas[0]
print("texto: " + textoFichero)
#removing html
textoFichero = removeHtmlFromString(textoFichero)
#saving it
studentAnswersDict.append({"respuesta":textoFichero, "hashed_id":student_name, "TableIndex":indx})
print("fichero procesado")
elif os.path.exists(create_file_path("StudentAnswers/" + work_folder, doctype= 1)) :
#print("Entra por acá2")
student_name2 = work_folder.split("_")
student_name = student_name2[0]
student_id2=student_name2[1]
student_assingsubmission = student_name2[2]
student_response = student_name2[3]
#print("Sigue acá2")
#print("StudentResponse"+str(student_response))
if student_response=='onlinetext':
# print("Fichero: " + "StudentAnswers/" + work_folder+"/onlinetext.html")
fichero = open(create_file_path("StudentAnswers/" + work_folder+"/onlinetext.html", doctype= 1), encoding='utf-8')
lineas = fichero.readlines()
#removing html
lineas[0] = removeHtmlFromString(lineas[0])
#saving it
indx2+=1
studentAnswersDict.append({"respuesta":lineas[0], "hashed_id":student_name, "TableIndex":indx2})
#break
#elif student_response!='file' or None:
# print("Fichero no encontrado")
# studentAnswersDict.append({"respuesta":'', "hashed_id":student_name, "TableIndex":indx2})
except:
#studentAnswersDict.append({"respuesta":"", "hashed_id":student_name, "TableIndex":indx})
print("Error buscando ficheros:"+fichero)
#print("DICT" + json.dumps(studentAnswersDict))
#saving the final dictionary
save_json(create_file_path('ApiStudentsDict.json', doctype= 1),studentAnswersDict)
return studentAnswersDict
zipFileInput = gr.inputs.File(label="1. Selecciona el .ZIP con las respuestas de los alumnos")
txtFileInput = gr.inputs.File(label="2. Selecciona el .txt con las preguntas y respuestas correctas. Escriba una pregunta en una sola línea y debajo la respuesta en la línea siguiente.")
orthographyPercentage = gr.inputs.Textbox(label="Ortografía",lines=1, placeholder="0",default=0.1, numeric=1)
syntaxPercentage = gr.inputs.Textbox(label="Sintaxis",lines=1, placeholder="0",default=0.1,numeric=1)
semanticPercentage = gr.inputs.Textbox(label="Semántica",lines=1, placeholder="0",default=0.8, numeric=1)
studentsRange = gr.inputs.Textbox(label="Estudiantes a evaluar",lines=1, placeholder="Dejar vacío para evaluar todos")
#dataFrameOutput = gr.outputs.Dataframe(headers=["Resultados"], max_rows=20, max_cols=None, overflow_row_behaviour="paginate", type="pandas", label="Resultado")
labelOutput = gr.outputs.Label(num_top_classes=None, type="auto", label="Output")
labelError = gr.outputs.Label(num_top_classes=None, type="auto", label="Errores")
downloadExcelButton = gr.outputs.File('Resultados')
iface = gr.Interface(fn=Main
, inputs=[zipFileInput, txtFileInput, orthographyPercentage, syntaxPercentage, semanticPercentage, studentsRange]
, outputs=[labelError, downloadExcelButton]
, title = "PLENTAS"
)
#iface.launch(share = False,enable_queue=True, show_error =True, server_port= 7861)
iface.launch(share = False,enable_queue=True, show_error =True) |