def window():
global lastFilename
name,ext=os.path.splitext(request.args.get('type'))
filenamenew=name+ext
lastFilename=filenamenew
status=1
if ext==".pdf":
# creating a pdf file object
pdfFileObj = open(UPLOAD_FOLDER+"/"+filenamenew, 'rb')
# creating a pdf reader object
pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
# printing number of pages in pdf file
print(pdfReader.numPages)
#print(pdfReader.getDocumentInfo())
#print(pdfReader.getIsEncrypted())
# creating a page object
bundle=""
for i in range(1,pdfReader.numPages):
pageObj = pdfReader.getPage(i)
# extracting text from page
#print(pageObj.extractText())
bundle+=pageObj.extractText()
#print(bundle)
# closing the pdf file object
pdfFileObj.close()
#Auto tagging
t = AutoTagify()
t.text = bundle
#print(t.tag_list())
e_words = list(dict.fromkeys(t.tag_list()))
#print(e_words)
else:
file = open(UPLOAD_FOLDER+"/"+filenamenew,"r+")
#print(type(file.read()))
t = AutoTagify()
t.text = file.read()
#print(len(t.tag_list()))
e_words = list(dict.fromkeys(t.tag_list()))
#print(e_words)
file.close()
#Summarization
summary=generate_summary(UPLOAD_FOLDER+"/"+filenamenew,5)
conn = sqlite3.connect('TAGS.db')
#c = conn.cursor()
# Insert a row of data
conn.execute('''INSERT INTO Tag (Filename,Auto_tag,Manual_tag,Summary,status) VALUES (?,?,?,?,?)''',(filenamenew, str(e_words),str([]),str(summary),status))
# Save (commit) the changes
conn.commit()
conn.close()
return render_template('window.html',F=filenamenew,L=e_words)
def getsummary():
if request.method == "POST":
topic = request.form['topic']
text = request.form["text"]
data = generate_summary(text)
sd.add_data(text,topic,data)
all_data = sd.get_all_records()
return render_template("getchapsum.html",data= data,text=text,all_data = all_data)
return render_template("getchapsum.html")
def get_metadata(path):
# Checks if Scanned PDF (Needs to be added)
text = ""
curr_page = 0
with open(path, 'rb') as f:
pdf = PdfFileReader(f)
info = pdf.getDocumentInfo()
number_of_pages = pdf.getNumPages()
while curr_page < number_of_pages:
page = pdf.getPage(curr_page)
curr_page += 1
text += page.extractText()
metadata = {}
metadata['author'] = info.author
metadata['creator'] = info.creator
metadata['producer'] = info.producer
metadata['subject'] = info.subject
metadata['title'] = info.title
metadata['numpages'] = number_of_pages
metadata['summary'] = generate_summary(text)
return metadata
def pipeline(progargs):
""" Performs sequentially the steps of the pipeline that have been
requested.
Args:
progargs: Program arguments.
"""
# Magnitudes calculated.
mag = None
stars, filters, header_fields = get_pipeline_parameters(progargs)
# This step organizes the images in directories depending on the type of
# image: bias, flat or data.
if progargs.organization_requested or progargs.all_steps_requested:
logging.info("* Step 1 * Organizing image files in directories.")
orgfits.organize_files(progargs, stars, header_fields, filters)
anything_done = True
else:
logging.info("* Step 1 * Skipping the organization of image files in directories. Not requested.")
# This step reduces the data images applying the bias and flats.
if progargs.reduction_requested or progargs.all_steps_requested:
logging.info("* Step 2 * Reducing images.")
reduction.reduce_images(progargs)
anything_done = True
else:
logging.info("* Step 2 * Skipping the reduction of images. Not requested.")
# This step find objects in the images. The result is a list of x,y and
# AR,DEC coordinates.
if progargs.astrometry_requested or progargs.all_steps_requested:
logging.info("* Step 3 * Performing astrometry of the images.")
astrometry.do_astrometry(progargs, stars, header_fields)
anything_done = True
else:
logging.info("* Step 3 * Skipping astrometry. Not requested.")
# This step calculates the photometry of the objects detected doing the
# astrometry.
if progargs.photometry_requested or progargs.all_steps_requested:
logging.info("* Step 4 * Performing photometry of the stars.")
photometry.calculate_photometry(progargs)
anything_done = True
else:
logging.info("* Step 4 * Skipping photometry. Not requested.")
# This step process the magnitudes calculated for each object and
# generates a file that associate to each object all its measures.
if progargs.magnitudes_requested or progargs.all_steps_requested:
logging.info("* Step 5 * Calculating magnitudes of stars.")
mag = magnitude.process_magnitudes(stars, progargs.target_dir,
progargs.light_directory)
anything_done = True
else:
logging.info("* Step 5 * Skipping the calculation of magnitudes of stars. Not requested.")
# This step process the magnitudes calculated for each object and
# generates a light curves.
if progargs.light_curves_requested or progargs.all_steps_requested:
logging.info("* Step 6 * Generating light curves.")
curves.generate_curves(stars, progargs.target_dir)
anything_done = True
else:
logging.info("* Step 6 * Skipping the generation of light curves. Not requested.")
# Generates a summary if requested and some task has been indicated.
if anything_done and progargs.summary_requested:
summary.generate_summary(progargs, stars, mag)
import speech_recognition as sr
import summary as s
print('hi')
r = sr.Recognizer()
with sr.Microphone() as source:
audio = r.listen(source)
print('hi')
try:
command = r.recognize_google(audio)
with open('gen.txt', 'w') as f:
print(command, file=f)
except:
print('Soryy')
s.generate_summary('gen.txt')
def pipeline(progargs):
""" Performs sequentially the steps of the pipeline that have been
requested.
Args:
progargs: Program arguments.
"""
# Magnitudes calculated.
mag = None
stars, filters, header_fields = get_pipeline_parameters(progargs)
# This step organizes the images in directories depending on the type of
# image: bias, flat or data.
if progargs.organization_requested or progargs.all_steps_requested:
logging.info("* Step 1 * Organizing image files in directories.")
orgfits.organize_files(progargs, stars, header_fields, filters)
anything_done = True
else:
logging.info(
"* Step 1 * Skipping the organization of image files in directories. Not requested."
)
# This step reduces the data images applying the bias and flats.
if progargs.reduction_requested or progargs.all_steps_requested:
logging.info("* Step 2 * Reducing images.")
reduction.reduce_images(progargs)
anything_done = True
else:
logging.info(
"* Step 2 * Skipping the reduction of images. Not requested.")
# This step find objects in the images. The result is a list of x,y and
# AR,DEC coordinates.
if progargs.astrometry_requested or progargs.all_steps_requested:
logging.info("* Step 3 * Performing astrometry of the images.")
astrometry.do_astrometry(progargs, stars, header_fields)
anything_done = True
else:
logging.info("* Step 3 * Skipping astrometry. Not requested.")
# This step calculates the photometry of the objects detected doing the
# astrometry.
if progargs.photometry_requested or progargs.all_steps_requested:
logging.info("* Step 4 * Performing photometry of the stars.")
photometry.calculate_photometry(progargs)
anything_done = True
else:
logging.info("* Step 4 * Skipping photometry. Not requested.")
# This step process the magnitudes calculated for each object and
# generates a file that associate to each object all its measures.
if progargs.magnitudes_requested or progargs.all_steps_requested:
logging.info("* Step 5 * Calculating magnitudes of stars.")
mag = magnitude.process_magnitudes(stars, progargs.target_dir,
progargs.light_directory)
anything_done = True
else:
logging.info(
"* Step 5 * Skipping the calculation of magnitudes of stars. Not requested."
)
# This step process the magnitudes calculated for each object and
# generates a light curves.
if progargs.light_curves_requested or progargs.all_steps_requested:
logging.info("* Step 6 * Generating light curves.")
curves.generate_curves(stars, progargs.target_dir)
anything_done = True
else:
logging.info(
"* Step 6 * Skipping the generation of light curves. Not requested."
)
# Generates a summary if requested and some task has been indicated.
if anything_done and progargs.summary_requested:
summary.generate_summary(progargs, stars, mag)