def __test__(dp, model, fbid):
"Using for quick test a crawled account"
# Create a convertor and convert files in a account to vector
convertor = Convertor("data")
profile = convertor.read_profile(fbid)
profile = pd.DataFrame([profile])
# Load datapreprocessing object and nomalizing vector
datapreprocessing = load(dp)
profile = datapreprocessing.convert(profile)
# Load model and predict result
randomforest = load(model)
result = randomforest.predict_proba(profile)[0]
print(result)
def __solver__(conn, addr, data, **kwargs):
"Resolve a package 'data' come from client"
# Change data from string to json
data = json.loads(data)
# Get parameter server
if "server" not in kwargs:
raise Exception("Expected server parameter")
server = kwargs["server"]
if "uid" in data:
uid = data["uid"]
else:
uid = None
# Solve data have key fb_id
if "fb_id" in data:
fb_id = scrape_utils.__create_original_link__("https://",
data["fb_id"])
while True:
# Get email and password from server object
email = server.__email__[server.__current_account__]
password = server.__password__[server.__current_account__]
# Create scraper and start scraping facebook account
try:
scraper = Scraper2(email, password, verbose="file", sender=uid)
bSuccess = scraper(fb_id)
except Exception as e:
print(str(e))
scraper.__driver__.close()
conn.close()
return
# Not success if the crawler account is banned
if bSuccess is not False:
break
content = json.dumps({
"kind": "notify",
"data": "Error in crawling, restart crawling...",
"level": None,
"end": "\n"
})
__print__(content, verbose=server.__verbose__, file=uid)
# Switch account
server.__current_account__ = (server.__current_account__ +
1) % len(server.__email__)
content = json.dumps({
"kind": "notify",
"data": "Converting crawled data to vector......",
"level": 0,
"end": ""
})
__print__(content, verbose=server.__verbose__, file=uid)
# Create convertor and convert crawled data to vector
convertor = Convertor("data")
profile = convertor.read_profile(fb_id.split("/")[-1])
profile = pd.DataFrame([profile])
content = json.dumps({
"kind": "notify",
"data": "Done",
"level": None,
"end": "\n"
})
__print__(content, verbose=server.__verbose__, file=uid)
content = json.dumps({
"kind": "notify",
"data": "Preprocessing data......",
"level": 0,
"end": ""
})
__print__(content, verbose=server.__verbose__, file=uid)
# Load datapreprocessing object and normalizing vector
datapreprocessing = load("pkg/DataPreprocessingremove.dp")
profile = datapreprocessing.convert(profile)
content = json.dumps({
"kind": "notify",
"data": "Done",
"level": None,
"end": "\n"
})
__print__(content, verbose=server.__verbose__, file=uid)
content = json.dumps({
"kind": "notify",
"data": "Predicting using Random forest......",
"level": 0,
"end": ""
})
__print__(content, verbose=server.__verbose__, file=uid)
# Load model and predict result
randomforest = load("pkg/overRandomForestremove.model")
result = randomforest.predict_proba(profile)[0][0] > 0.6
content = json.dumps({
"kind": "notify",
"data": "Done",
"level": None,
"end": "\n"
})
__print__(content, verbose=server.__verbose__, file=uid)
result = "real" if result == True else "fake"
folder = os.path.join(os.getcwd(), "data")
target_dir = os.path.join(folder, fb_id.split("/")[-1])
filename = os.path.join(target_dir, "result.txt")
with open(filename, mode="w") as f:
f.write(result)
content = json.dumps({
"kind": "result",
"data": result,
"level": None,
"end": "\n"
})
__print__(content, verbose=server.__verbose__, file=uid)
conn.close()
def __solver__(conn, addr, data, **kwargs):
"Resolve a package 'data' come from client"
# Change data from string to json
data = json.loads(data)
# Get parameter server
if "server" not in kwargs:
raise Exception("Expected server parameter")
server = kwargs["server"]
# Solve data have key fb_id
if "fb_id" in data:
while True:
# Get email and password from server object
email = server.__email__[server.__current_account__]
password = server.__password__[server.__current_account__]
# Create scraper and start scraping facebook account
try:
scraper = Scraper2(email, password, verbose= "send", sender= conn)
bSuccess = scraper(data["fb_id"])
except Exception as e:
print(str(e))
scraper.__driver__.close()
conn.close()
return
# Not success if the crawler account is banned
if bSuccess:
break
content = json.dumps({
"kind": "notify",
"data": "Error in crawling, restart crawling...",
"level": None,
"end": "\n"
})
conn.send(content.encode())
# Switch account
server.__current_account__ = (server.__current_account__ + 1) % len(server.__email__)
content = json.dumps({"kind": "notify", "data": "Converting crawled data to vector......", "level": 0, "end": ""})
conn.send(content.encode())
# Create convertor and convert crawled data to vector
convertor = Convertor("data")
profile = convertor.read_profile(data["fb_id"].split("/")[-1])
profile = pd.DataFrame([profile])
content = json.dumps({"kind": "notify", "data": "Done", "level": None, "end": "\n"})
conn.send(content.encode())
content = json.dumps({"kind": "notify", "data": "Preprocessing data......", "level": 0, "end": ""})
conn.send(content.encode())
# Load datapreprocessing object and normalizing vector
datapreprocessing = load("pkg/DataPreprocessingremove.dp")
profile = datapreprocessing.convert(profile)
content = json.dumps({"kind": "notify", "data": "Done", "level": None, "end": "\n"})
conn.send(content.encode())
content = json.dumps({"kind": "notify", "data": "Predicting using Random forest......", "level": 0, "end": ""})
conn.send(content.encode())
# Load model and predict result
randomforest = load("pkg/overRandomForestremove.model")
result = randomforest.predict_proba(profile)[0][0] > 0.6
content = json.dumps({"kind": "notify", "data": "Done", "level": None, "end": "\n"})
conn.send(content.encode())
result = "real" if result == True else "fake"
content = json.dumps({"kind": "result", "data": result, "level": None, "end": "\n"})
conn.send(content.encode())
conn.close()
