def start_eel(develop):
"""Start Eel with either production or development configuration"""
if develop:
directory = 'src'
app = None
page = {'port': 3000}
flags = ['--auto-open-devtools-for-tabs']
else:
directory = 'build'
app = 'chrome-app'
page = 'index.html'
flags = []
eel.init(directory, ['.tsx', '.ts', '.jsx', '.js', '.html'])
# These will be queued until the first connection is made, but won't be repeated on a page reload
say_hello_py('Python World!')
eel.say_hello_js(
'Python World!'
) # Call a JavaScript function (must be after `eel.init()`)
eel.start(page,
size=(1280, 800),
options={
'mode': app,
'port': 8080,
'host': 'localhost',
'chromeFlags': flags
})
def start_eel(develop):
"""Start Eel with either production or development configuration."""
if develop:
directory = 'src'
app = None
page = {'port': 8080}
else:
directory = 'web'
app = 'chrome-app'
page = 'index.html'
eel.init(directory)
say_hello_py("Python World!")
eel.say_hello_js(
"Python World!"
) # Call a JavaScript function (must be after `eel.init()`)
eel_kwargs = dict(
host="localhost",
port=9000,
# size=(1280, 800),
)
try:
eel.start(**eel_kwargs)
except EnvironmentError:
# If Chrome isn't found, fallback to Microsoft Edge on Win10 or greater
if sys.platform in ["win32", "win64"
] and int(platform.release()) >= 10:
eel.start(page, mode="edge", **eel_kwargs)
else:
raise
def start(self):
"""Start Eel"""
if self.Develop:
# TODO launch browser
directory = 'wwwroot'
app = None
page = {'port': 3000}
flags = ['--auto-open-devtools-for-tabs']
else:
directory = 'wwwroot'
#app = 'chrome-app'
app = None
page = 'index.html'
flags = []
eel.init(directory, ['.tsx', '.ts', '.jsx', '.js', '.html'])
# These will be queued until the first connection is made, but won't be repeated on a page reload
#say_hello_py('Python1')
eel.say_hello_js(
'Python2'
) # Call a JavaScript function (must be after `eel.init()`)
eel.start(page,
size=(1280, 800),
options={
'mode': app,
'port': self.Port,
'host': 'localhost',
'chromeFlags': flags
})
def say_hello_py(query, cutoff, choice):
global doc_freq
if vectors == None or weights == None or documentFreq == None or corpus == None or choice == 0:
eel.say_hello_js("Building Indexes ...")
choice = 0
result = entertain(query, cutoff, choice)
return result
def say_hello_py(query, cutoff, choice):
global doc_freq
if doc_freq == None or choice == 0:
eel.say_hello_js("Building Indexes ...")
choice = 0
result = entertain(query, cutoff, choice)
return result
def start_eel(develop):
"""Start Eel with either production or development configuration."""
if develop:
directory = 'src'
app = None
page = {'port': 3000}
else:
directory = 'build'
app = 'chrome-app'
page = 'index.html'
eel.init(directory, ['.tsx', '.ts', '.jsx', '.js', '.html'])
# These will be queued until the first connection is made, but won't be repeated on a page reload
say_hello_py('Python World!')
eel.say_hello_js(
'Python World!'
) # Call a JavaScript function (must be after `eel.init()`)
eel_kwargs = dict(
host='localhost',
port=8080,
size=(1280, 800),
)
try:
eel.start(page, mode=app, **eel_kwargs)
except EnvironmentError:
# If Chrome isn't found, fallback to Microsoft Edge on Win10 or greater
if sys.platform in ['win32', 'win64'
] and int(platform.release()) >= 10:
eel.start(page, mode='edge', **eel_kwargs)
else:
raise
def predict_text(self, text):
if self.corpus == None or self.documentFreq == None or self.weights == None or self.vectors == None:
eel.say_hello_js("KNN Not Trained Yet!! Please Train First")
return 0
prediction = ""
parsed = parse_Query(text)
results, query_tf, parsed = process_Query(parsed, self.corpus)
for i in range(len(results) - 1, -1,
-1): #Remove words from query which are not in corpus
if (results[i] == -1):
parsed.pop(i)
results.pop(i)
query_tf.pop(i)
result = fetch_docs(parsed, query_tf, results, self.vectors,
self.weights, self.documentFreq, self.corpus)
docs = []
for i, arr in self.vectors.items():
docs.append(i)
sort_doc(result, docs, 0, len(result) - 1)
p = []
for i in range(len(docs) - 1, len(docs) - 4, -1):
p.append(docs[i].split('.')[0])
try:
prediction = statistics.mode(p)
except:
prediction = p[len(p) - 1]
return prediction
def start_eel(develop):
"""Start Eel with either production or development configuration."""
directory = 'src'
eel.init(directory)
say_hello_py('Python World!')
eel.say_hello_js(
'Python World!'
) # Call a JavaScript function (must be after `eel.init()`)
eel_kwargs = dict(
host='localhost',
port=9000,
# size=(1280, 800),
)
try:
# eel.start(page, mode=app, **eel_kwargs)
eel.start(**eel_kwargs)
except EnvironmentError:
# If Chrome isn't found, fallback to Microsoft Edge on Win10 or greater
if sys.platform in ['win32', 'win64'
] and int(platform.release()) >= 10:
eel.start(page, mode='edge', **eel_kwargs)
else:
raise
def run_KNN(self):
if self.corpus == None or self.documentFreq == None or self.weights == None or self.vectors == None:
eel.say_hello_js("KNN Not Trained Yet!! Please Train First")
return
try:
file = open('./KNN/testDocs.txt', 'r')
test = json.load(file)
file.close()
except:
eel.say_hello_js("KNN Not Trained Yet!! Please Train First")
prediction = []
test_label = []
print("Running KNN")
for label, arr in test.items():
for filename in arr:
test_label.append(label)
file = open(filename, 'r')
text = file.read()
file.close()
parsed = parse_Query(text)
results, query_tf, parsed = process_Query(parsed, self.corpus)
for i in range(
len(results) - 1, -1,
-1): #Remove words from query which are not in corpus
if (results[i] == -1):
parsed.pop(i)
results.pop(i)
query_tf.pop(i)
result = fetch_docs(parsed, query_tf, results, self.vectors,
self.weights, self.documentFreq,
self.corpus)
docs = []
for i, arr in self.vectors.items():
docs.append(i)
sort_doc(result, docs, 0, len(result) - 1)
p = []
for i in range(len(docs) - 1, len(docs) - 4, -1):
p.append(docs[i].split('.')[0])
try:
prediction.append(statistics.mode(p))
except:
prediction.append(p[len(p) - 1])
return accuracy_score(test_label, prediction), confusion_matrix(
test_label, prediction)
def say_hello_py(x):
"""Print message from JavaScript on app initialization, then call a JS function."""
print('Hello from %s' % x)
eel.say_hello_js('Python {from within say_hello_py()}!')
@eel.expose # Expose this function to Javascript
def save_map_image(folder, filename, img_type):
"""
Сохраняет изображение, помещенное в Clipboard, в файл на диске
folder - имя папки с отчетом
filename - имя файла с изображением
img_type - тип изображения: 'JPEG' или 'PNG'
"""
dst_path = os.path.join(settings.REPORTS_PATH, folder, 'images')
print(f'>>> Путь к папке с картинками: {dst_path}')
if os.path.exists(dst_path):
img_file = os.path.join(dst_path, filename)
print(f'save_map_image: файл {img_file} \n')
time.sleep(1) # без этой задержки берет из clipboard предыдущее содержимое
img = ImageGrab.grabclipboard() # from PIL import ImageGrab
# eel.addOutput(f'save_map_image: файл {img_file} \n')
img.save(img_file, img_type) # img_type: 'JPEG' или 'PNG'
else:
print(f'*** save_map_image: папка {dst_path} не существует\n')
eel.addOutput(f'*** save_map_image: папка {dst_path} не существует\n')
logging.debug(f'*** save_map_image: папка {dst_path}не существует\n')
eel.print_build_js(f'v {app.version} Build: {app.build}') # Call a Javascript function
logging.info(f'******************** Starting Cadastron v{app.version} Build: {app.build} ********************')
eel.say_hello_js('connected!') # Call a Javascript function
eel.print_to_textarea_js('') # Call a Javascript function
eel.start('main-uikit.html', size=(800, 1050)) # Start
import eel
#暴露给js调用
@eel.expose
def hello_world():
return "Hello from python"
# 定义html文件所在文件夹名称
eel.init('dist')
#py调用js 必须在eel.init之后调用
eel.say_hello_js("i am py")
# 启动的函数调用放在最后,port=0表示使用随机端口,size=(宽,高)
eel.start('index.html', port=0, size=(600, 300))
import eel
eel.init('web') # Give folder containing web files
@eel.expose # Expose this function to Javascript
def say_hello_py(x):
print('Hello from %s' % x)
say_hello_py('Python World!')
eel.say_hello_js('Python World!') # Call a Javascript function
eel.start('hello.html', size=(300, 200)) # Start
import eel
eel.init('web', allowed_extensions=['.js', '.html'])
@eel.expose
def say_hello_py(x):
print('Hello from %s' % x)
say_hello_py('Python World!')
eel.say_hello_js('Python World!')
eel.start('helloworld.html', size=(300, 200))
import eel
import datetime as dt
import json
import subprocess
eel.init('web')
@eel.expose
def getTime():
now = str(dt.datetime.now())
#return json.dumps({'now':now})
return now
#eel.js_random()(print)
def testThread():
#below should be replaced by an electron app call if necessary.
#maybe have a json file and regular checks from the javascript side to update values.
subprocess.call(['touch', 'other.html'])
eel.spawn(testThread)
eel.start('main.html')
random_number_from_javascript = eel.js_random()()
print(random_number_from_javascript)
eel.say_hello_js('javascript')
def say_hello_py(x):
# Print to Python console
print('Hello from %s' % x)
# Call a JavaScript function
eel.say_hello_js('Python {from within say_hello_py()}!')
import eel
eel.init('web') # Give folder containing web files
@eel.expose # Expose this function to Javascript
def handleinput(x):
print('%s' % x)
eel.say_hello_js('connected!') # Call a Javascript function
eel.start('main.html', size=(500, 200)) # Start
def say_hello_py(text, func):
knn = KNN()
kmean = Kmeans()
if func == 'trainKNN':
eel.say_hello_js("Generating Vectors From 70% Data ...")
knn.train_KNN()
eel.say_hello_js("Vectors Generated Successfully")
time.sleep(1)
file = open('./KNN/testDocs.txt', 'r')
test = json.load(file)
file.close()
text = "<h3><span style='color:yellow'>Seperated Test Files</span></h3><br/>"
for key, value in test.items():
text += "<h5 style='color:#F9008C'>{}</h5 style='color:#00A1F9'><p style='color:yellow'>{}</p>".format(
key, value)
return text
elif func == 'testKNN':
eel.say_hello_js("Working on 30% Test Data ...")
accuracy, confusion = knn.run_KNN()
eel.say_hello_js("Testing Completed. Fetching Results")
time.sleep(1)
text = "<div style='color:rgb(137, 202, 211);font-size:large;'>Accuracy Score = {}</div>".format(
accuracy)
text += "<div style='color:rgb(137, 202, 211);font-size:large;'>Atual on x, Predicted on y</div>"
text += "<div style='color:rgb(137, 202, 211);'> At Cr Ft Rg Tn</div>"
text += "<div style='color:aliceblue;'>{}</div>".format(confusion)
return text
elif func == 'predictKNN':
eel.say_hello_js("Working On Your Document")
time.sleep(1)
pred = knn.predict_text(text)
if pred == 0:
return
eel.say_hello_js("Here We Go...")
text = "<div style='font-size: larger;font-weight: bolder; color:yellow'>Your Document Belongs To {} Class</div>".format(
pred)
return text
elif func == 'trainKmeans':
eel.say_hello_js1("Generating Vectors For Kmean Clustering")
kmean.train_Kmean()
eel.say_hello_js1("Kmeans Vectors Genrated")
file = open('./Kmean/vector.json')
fsize = json.load(file)
file.close()
for key, value in fsize.items():
eel.setFeatureSize("Feauture Set Size = {}".format(len(value)))
break
return
elif func == 'testKmeans':
eel.say_hello_js1("Genrating Clusters Please Wait..")
param = text.split(',')
iteration = int(param[0], base=10)
no_cluster = int(param[1], base=10)
print(iteration)
print(no_cluster)
cluster = kmean.run_K_mean(iteration, no_cluster)
if cluster == 0:
eel.say_hello_js1(
"Kmeans Vectors Not Found. Generate Vectors First")
return
eel.say_hello_js1("Here We Go..")
text = "<div style='color:yellow'>{}</div>".format(cluster)
return text
elif func == 'seeList':
try:
file = open('./KNN/testDocs.txt', 'r')
data = json.load(file)
file.close()
except:
eel.say_hello_js("No Data Found. Please Run KNN")
return
text = "<h3><span style='color:yellow'>Seperated Test Files</span></h3><br/>"
for key, value in data.items():
text += "<h5 style='color:#F9008C'>{}</h5 style='color:#00A1F9'><p style='color:yellow'>{}</p>".format(
key, value)
return text
elif func == 'featureSize':
try:
file = open('./Kmean/vector.json')
fsize = json.load(file)
file.close()
for key, value in fsize.items():
eel.setFeatureSize("Feauture Set Size = {}".format(len(value)))
break
except:
eel.setFeatureSize("Vectors Not Generated Yet")
elif func == 'seeCluster':
try:
file = open('./Kmean/latestCluster.txt', 'r')
text1 = file.read()
file.close()
return text1
except:
eel.say_hello_js1("No Clusters Generated Yet")
elif func == 'predictKMean':
eel.say_hello_js1("Working On Your Document")
time.sleep(1)
pred = kmean.predict_text(text)
if pred == 0:
eel.say_hello_js1("Clusters Not Generated Yet")
return
eel.say_hello_js1("Here We Go...")
text = "<div style='font-size: larger;font-weight: bolder; color:yellow'>Your Document Belongs To {} Cluster</div>".format(
pred)
return text
if domain in server.keys():
return server[domain]
else:
ip = None
prefix = ['mail.', 'smtp.', 'smtp.mail.', 'smtp-mail.']
for i in prefix:
try:
connection = socket.create_connection(
(i + complete_domain, 465), timeout=2)
if connection:
ip = i + complete_domain
connection.close()
return ip
except:
pass
if ip:
return ip
else:
return False
if __name__ == "__main__":
say_hello_py("Server started.")
eel.say_hello_js("Server connected.") # Call a Javascript function
template = check_if_user_exists()
eel.start(template, mode="electron") # Start
conn.close()
import eel
eel.init('web')
@eel.expose
def handle_input(x):
print('%s' % x)
eel.say_hello_js('connected!')
eel.start('input.html', size=(500, 200))
def entertain(text, cutoff, choice):
parsed = parse_Query(text)
if (choice == 1):
print("Using Preprocessed Data")
pass
else:
print("Running From Scratch") #Run from scratch if flag is on
tokenizer()
Processor()
print(parsed)
eel.say_hello_js("Processing Query ...") #Send current status to frontend
results, query_tf, parsed = process_Query(parsed)
for i in range(len(results) - 1, -1,
-1): #Remove words from query which are not in corpus
if (results[i] == -1):
parsed.pop(i)
results.pop(i)
query_tf.pop(i)
result = fetch_docs(parsed, query_tf, results)
docs = []
print("Result Of Cosine Calculation")
for i in range(56):
docs.append(i)
sort_doc(result, docs, 0, len(result) - 1)
print(list(zip(docs, result)))
ans = []
counter = 0
print("Cutoff", end='->')
print(float(cutoff))
for i in range(len(result)):
real_query = copy.deepcopy(parsed)
text = ""
if (result[i] >= float(cutoff)):
text += "<div style='color:blue'>Doc#{}--{}</div>\n".format(
docs[i], result[i])
counter += 1
#Fetch Summary ###########This Part Is Extra And is done via python builtin methods
file = open('./Trump Speechs/speech_{}.txt'.format(docs[i]))
data = file.read().split('.')
counter1 = 0
flag = 0
for line in data:
for k in range(len(real_query) - 1, -1, -1):
if real_query[k] in line.lower() or lemmatizer.lemmatize(
real_query[k]) in line.lower():
text += line
text += '\n'
text += "<div style='color:blue'> -----------------------------------</div>"
text += '\n'
real_query.pop(k)
counter1 += 1
if (counter1 == 2):
flag = 1
break
if (flag == 1):
break
text += "\n\n"
#ans.append("(doc{}-{})\n".format(docs[i],result[i]))
ans.append(text)
ans.reverse()
print(counter)
res = "Lenght = {}\n\n".format(counter)
for a in ans:
print(a)
res += a
res += "<div style='color:magenta'> ************************************************************</div>\n"
return res
import eel
eel.init('web/') # Give folder containing web files
@eel.expose # Expose this function to Javascript
def say_hello_py(x):
print('Hello from %s' % x)
say_hello_py('Python World!')
eel.say_hello_js('Python World!') # Call a Javascript function
eel.start('templates/hello.html', size=(300, 200),
templates='templates') # Start
def entertain(text, cutoff, choice):
global vectors
global weights
global documentFreq
global corpus
parsed = parse_Query(text)
if (choice == 1 and vectors != None and weights != None
and documentFreq != None and corpus != None):
print("Using Preprocessed Data")
pass
else:
print("Running From Scratch") #Run from scratch if flag is on
doc_vocab, doc_freq = tokenizer()
Processor(doc_vocab, doc_freq)
file = open('./Data/Corpus.txt', 'r')
corpus = file.read().split(',')
file.close()
file = open('./Data/DocumentFrequency.txt', 'r')
documentFreq = file.read().split(',')
file.close()
file = open('./Data/Weights.txt', 'r')
weights = file.read().split(',')
file.close()
file = open('./Data/Vectors.txt', 'r')
vectors = json.load(file)
file.close()
print(parsed)
eel.say_hello_js("Processing Query ...") #Send current status to frontend
results, query_tf, parsed = process_Query(parsed, corpus)
for i in range(len(results) - 1, -1,
-1): #Remove words from query which are not in corpus
if (results[i] == -1):
parsed.pop(i)
results.pop(i)
query_tf.pop(i)
result = fetch_docs(parsed, query_tf, results, vectors, weights,
documentFreq)
docs = []
print("Result Of Cosine Calculation")
for i in range(56): #print answer and related docs
docs.append(i)
sort_doc(result, docs, 0, len(result) - 1)
print(list(zip(docs, result)))
ans = []
counter = 0
print("Cutoff", end='->')
print(float(cutoff))
for i in range(len(result)):
real_query = copy.deepcopy(parsed) #Copying Orijinal Query
text = ""
if (result[i] >= float(cutoff)):
text += "<div style='color:blue'>Doc#{}--{}</div>\n".format(
docs[i], result[i])
counter += 1
#Fetch Summary ###########This Part Is Extra And is done via python builtin methods
file = open('./Trump Speechs/speech_{}.txt'.format(docs[i]))
data = file.read().split('.')
counter1 = 0
flag = 0
for line in data: #Find if query word is in this line
for k in range(len(real_query) - 1, -1, -1):
if real_query[k] in line.lower() or lemmatizer.lemmatize(
real_query[k]) in line.lower():
text += line
text += '\n'
text += "<div style='color:blue'> -----------------------------------</div>"
text += '\n'
real_query.pop(k)
counter1 += 1
if (counter1 == 2):
flag = 1
break
if (flag == 1):
break
text += "\n\n"
#ans.append("(doc{}-{})\n".format(docs[i],result[i]))
ans.append(text)
ans.reverse()
print(counter)
res = "<div style='color:red;text-decoration:underline'>Lenght = {}\n\n</div>".format(
counter)
for a in ans:
print(a)
res += a
res += "<div style='color:magenta'> ************************************************************</div>\n"
return res
