def word_check(name):
dicti = enchant.PyPWL("words.txt")
word_exists = dicti.check(name)
if word_exists:
print("Already there!!")
return name
if not word_exists:
suggest = dicti.suggest(name)
print("input: ", name)
if len(suggest) > 0:
print("suggested: ", suggest[0])
a = np.zeros([len(suggest[0]) + 1, len(name) + 1])
# print(a)
a[0] = np.arange(len(name) + 1)
a[:, 0] = np.arange(len(suggest[0]) + 1)
# print(a)
for i in range(1, len(suggest[0]) + 1):
#print(i)
for j in range(1, len(name) + 1):
if suggest[0][i - 1] == name[j - 1]:
#print('Same')
a[i, j] = a[i - 1, j - 1]
else:
#print('different')
a[i, j] = min(a[i - 1, j], a[i, j - 1], a[i - 1,
j - 1]) + 1
#print(type(a[-1,-1]))
n = float(a[-1, -1])
#print(len(name))
if n <= len(name) / 2:
print("lol")
if not len(suggest[0]) in range(len(name), len(name) + 2):
print("Suggested won't be implemented")
return name
else:
print("Change accepted")
#str.replace(name,suggest[0])
return suggest[0]
else:
print("change rejected")
return name
else:
return name
df.rename(columns={'Unnamed: 3': 'Total'}, inplace=True)
#check the data
df.head()
df.info()
df.describe()
#spell-checker
import enchant
#write the name of all zillas in a text file and prepare the custom dictionary
allZilla = '\n'.join(zillas for zillas in list(myDF.iloc[:, 2]))
with open("zillaNames.txt", "w") as outfile:
outfile.write(allZilla)
zillaList = enchant.PyPWL("zillaNames.txt")
#create the dictionary of total cases
df.set_index('District/City', inplace=True)
caseDict = df.to_dict(orient='dict')
caseDict = caseDict['Total']
#the name of B. Baria is too tough to map to Brahamanbaria
caseDict['Brahamanbaria'] = caseDict['B. Baria']
del caseDict['B. Baria']
#correct the zilla name in the data according to the administrative name
#I have no clue why "Chapainawabganj" is not in the list of administrative map!
#and I don't know - "ami ekhon oke niye ki korbo? -_-"
for zilla, cases in caseDict.items():
if (zillaList.check(zilla)):
words2 = str.lower(med).split("_")
if word in words1 or word in words2:
words_dict[word][0].append(cnt)
words_dict[word][1][cnt] = 1
return words_dict
def save_hash(total_words: "HASH") -> None:
if os.path.exists(HASH_DUMP_FILE_NAME):
os.remove(HASH_DUMP_FILE_NAME)
with open(HASH_DUMP_FILE_NAME, "a") as f:
f.write("\n".join(list(total_words)))
load_hash = lambda: enchant.PyPWL(HASH_DUMP_FILE_NAME)
def save_data(query_list: "List of queries") -> list:
total_words = hash_build(query_list)
words_dict = inv_hash_build(query_list, total_words)
save_hash(total_words)
tot_words_dict = load_hash()
pickle.dump(
(query_list, total_words, words_dict, tot_words_dict),
open(DATA_STORAGE_FILENAME, "wb"),
)
return query_list, total_words, words_dict, tot_words_dict
import enchant
import sys
#accept the movie name from command line arguments
input_word = sys.argv[1]
#load the personal word list dictionary
my_dict = enchant.PyPWL("ingredientslist.txt")
ingredients_dict = enchant.PyPWL("ingredientslist.txt")
#get suggestions for the input word
suggestions = my_dict.suggest(input_word)
#check if the word exists in the dictionary
word_exists = ingredients_dict.check(input_word)
print("word exists: ", word_exists)
print ("input:", input_word)
print("suggestions:", suggestions)
if not word_exists:
#get suggestions for the input word if the word doesn't exist in the dictionary
suggestions = ingredients_dict.suggest(input_word)
print ("input:", input_word)
print("suggestions:", suggestions)
#spell-checker.py
import enchant
import sys
#accept the movie name from command line arguments
input_movie_name = sys.argv[1]
#load the personal word list dictionary
movies_dict = enchant.PyPWL("Settings/star_name.txt")
#check if the word exists in the dictionary
word_exists = movies_dict.check(input_movie_name)
print("word exists: ", word_exists)
if not word_exists:
#get suggestions for the input word
suggestions = movies_dict.suggest(input_movie_name)
print("input:", input_movie_name)
print("suggestions:", suggestions)
def run(self, dispatcher: CollectingDispatcher, tracker: Tracker,
domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:
response = requests.get(
"https://api.covid19india.org/data.json").json()
state_name = str((tracker.latest_message)['text'])
state_name = state_name.lower()
print(state_name)
states_dict = enchant.PyPWL("states.txt")
word_exists = states_dict.check(state_name)
print("word exists: ", word_exists)
if not word_exists:
#get suggestions for the input word
suggestions = states_dict.suggest(state_name)
print("input:", state_name)
print("suggestions:", suggestions)
if (len(suggestions) != 0):
state_name = suggestions[0]
#state = None
entities = tracker.latest_message['entities']
print(entities)
#for e in entities:
# if(e['entity'] == "state"):
# state = e['value']
#message = "Enter proper state name"
for data in response["statewise"]:
if data["state"] == state_name.title():
print("Active = " + data["active"] + " Confirmed = " +
data["confirmed"] + " Deaths = " + data["deaths"] +
" Recovered = " + data["recovered"])
message = "Active = " + data["active"] + " Confirmed = " + data[
"confirmed"] + " Deaths = " + data[
"deaths"] + " Recovered = " + data["recovered"]
active = int(data["active"])
confirmed = int(data["confirmed"])
deaths = int(data["deaths"])
recovered = int(data["recovered"])
print(active)
labels = ['Active', 'Confirmed', 'Deaths', 'Recovered']
backgroundColor = [
"#36a2eb", "#ffcd56", "#ff6384", "#009688", "#c45850"
]
#chartsData = [555,242,145,23]
chartsData = []
chartsData.extend((active, confirmed, deaths, recovered))
data = {
"title": state_name.upper(),
"labels": labels,
"backgroundColor": backgroundColor,
"chartsData": chartsData,
"chartType": "pie",
"displayLegend": "true"
}
#dispatcher.utter_message(state_name)
#dispatcher.utter_message(message)
dispatcher.utter_custom_json({"payload": "chart", "data": data})
return []
#df.rename( columns={'Unnamed: 0':'Total'}, inplace=True )
#check the data
df.head()
df.info()
df.describe()
#spell-checker
import enchant
#write the name of all zillas in a text file and prepare the custom dictionary
allZilla = '\n'.join(zillas for zillas in list(myDF['zilla']))
with open("zillaNames_mine.txt", "w") as outfile:
outfile.write(allZilla)
zillaList = enchant.PyPWL("zillaNames_mine.txt")
#create the dictionary of total cases
df.set_index('District/City', inplace=True)
caseDict = df.to_dict(orient='dict')
caseDict = caseDict['Total']
#the name of B. Baria is too tough to map to Brahamanbaria
caseDict['Brahamanbaria'] = caseDict['B. Baria']
del caseDict['B. Baria']
#correct the zilla name in the data according to the administrative name
for zilla, cases in caseDict.items():
if (zillaList.check(zilla)):
print(zilla, cases)
else:
import enchant
import sys
#accept the movie name from command line arguments
input_movie_name = sys.argv[1]
#load the personal word list dictionary
movies_dict = enchant.PyPWL("movies.txt")
#check if the word exists in the dictionary
word_exists = movies_dict.check(input_movie_name)
print("word exists: ", word_exists)
if not word_exists:
#get suggestions for the input word if the word doesn't exist in the dictionary
suggestions = movies_dict.suggest(input_movie_name)
print("input:", input_movie_name)
print("suggestions:", suggestions)
def execute(img):
List1 = []
List2 = []
endpoint = 'https://handwrittingrecognition.cognitiveservices.azure.com/'
text_recognition_url = endpoint + "vision/v2.1/read/core/asyncBatchAnalyze"
# Set image_path to the local path of an image that you want to analyze.
# image = img
# img2 = image.copy()
# # detecting red color
# image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# lower = np.array([155, 25, 0])
# upper = np.array([179, 255, 255])
# mask = cv2.inRange(image, lower, upper)
# thresh = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)[1]
# # apply canny edge detection
# edges = cv2.Canny(thresh, 100, 200)
# # get hough line segments
# threshold = 100
# minLineLength = 50
# maxLineGap = 20
# lines = cv2.HoughLinesP(thresh, 1, np.pi / 360, threshold, minLineLength, maxLineGap)
# # draw lines
# linear = np.zeros_like(thresh)
# for [line] in lines:
# # print(line)
# x1 = line[0]
# y1 = line[1]
# x2 = line[2]
# y2 = line[3]
# cv2.line(linear, (x1, y1), (x2, y2), (255), 1)
# # get bounds of white pixels
# white = np.where(linear == 255)
# xmin, ymin, xmax, ymax = np.min(white[1]), np.min(white[0]), np.max(white[1]), np.max(white[0])
# # draw bounding box on input
# bounds = img2.copy()
# cv2.rectangle(bounds, (xmin, ymin), (xmax, ymax), (0, 0, 255))
# crop the image at the bounds
#crop = img2[ymin:ymax, xmin:xmax]
crop=img
shp=img.shape
# cv2.imwrite("crop.jpg", crop)
#mp = ((xmax - xmin) / 1.714, (ymax - ymin) / 2)
mp=(shp[0]/1.714,shp[1]/2)
# image_path = "crop.jpg"
# # Read the image into a byte array
# image_data = open(image_path, "rb").read()
# print(image_data)
# crop = cv2.cvtColor(crop, cv2.COLOR_BGR2RGB)
# image_data=Image.fromarray(crop)
is_success, im_buf_arr = cv2.imencode(".jpg", crop)
image_data = im_buf_arr.tobytes()
# image_data=crop
headers = {'Ocp-Apim-Subscription-Key': subscription_key,
'Content-Type': 'application/octet-stream'}
response = requests.post(
text_recognition_url, headers=headers, data=image_data)
response.raise_for_status()
# Extracting text requires two API calls: One call to submit the
# image for processing, the other to retrieve the text found in the image.
# Holds the URI used to retrieve the recognized text.
operation_url = response.headers["Operation-Location"]
# The recognized text isn't immediately available, so poll to wait for completion.
analysis = {}
poll = True
notes_text = ""
specialization = ""
medicine_text = ""
m=0
l=0
lab_text = ""
while (poll):
response_final = requests.get(
response.headers["Operation-Location"], headers=headers)
analysis = response_final.json()
# print(analysis)
# Print the detected text, line by line
if analysis["status"] == TextOperationStatusCodes.succeeded:
for text_result in analysis["recognitionResults"]:
for line in text_result["lines"]:
if ("Specialization" in line["text"]):
specialization = line["text"][17:]
elif ("Dr. Name" not in line["text"]):
for word in line["words"]:
if (word["boundingBox"][0] + word["boundingBox"][2]) / 2 > mp[1]:
if (word["boundingBox"][1] + word["boundingBox"][7]) / 2 < mp[0]:
medicine_text = medicine_text + word["text"] + " "
m=1
else:
lab_text = lab_text + word["text"] + " "
l=1
else:
notes_text = notes_text + word["text"] + " "
if(m==1):
medicine_text=medicine_text+","
if(l==1):
lab_text=lab_text+","
time.sleep(1)
if ("recognitionResults" in analysis):
poll = False
if ("status" in analysis and analysis['status'] == 'Failed'):
poll = False
# Word Suggestions
# #print(os.path.curdir, "medicine/", specialization, ".txt")
medicine_lines=medicine_text.split(',')
medicine_dict = enchant.PyPWL(os.path.join(scriptdir,specialization+".txt"))
for medicine_line in medicine_lines:
words = medicine_line.split()
# print(words)
value=" "
for input_medicine_name in words:
suggestions = medicine_dict.suggest(input_medicine_name)
if(len(suggestions)!=0):
#print(suggestions[0])
value=value+suggestions[0]+" "
else:
value=value+input_medicine_name
if(List1!=' '):
List1.append(value)
#print('line end')
test_lines = lab_text.split(',')
test_dict = enchant.PyPWL(os.path.join(scriptdir, "tests.txt"))
for test_line in test_lines:
words=test_line.split()
value=" "
for input_test_name in words:
suggestions = test_dict.suggest(input_test_name)
if(len(suggestions)!=0):
#print(suggestions[0])
value=value+suggestions[0]+" "
else:
value=value+input_test_name
if(value!=' '):
List2.append(value)
#print('test end')
# print(words)
# List1.rstrip()
# List2.rstrip()
List1 = remove_values_from_list(List1, ' ')
List2 = remove_values_from_list(List2, ' ')
dictionary={"name":"Hamza",
"medicine":List1,
"tests":List2,
"notes":notes_text}
print(dictionary)
return dictionary
def __init__(self, wordlist):
SpChecker.__init__(self, wordlist)
self.spdict = enchant.PyPWL()
self.chkr = EnchantSpCheck("en_US")
for w in self.wordlist:
self.spdict.add(w)
def __init__(self, word_list_path, no_suggestions=5):
self.no_suggestions = no_suggestions
self.word_list_path = word_list_path
self.medical_dict = enchant.PyPWL(self.word_list_path)