async def chat(context): def check(author): def inner_check(message): return message.author == author return inner_check await client.say("Start talking with the bot!") mod = n.nlp() while True: inp = '' #wait for next message as message. msg = await client.wait_for('message', check=check(context.author), timeout=30) inp = msg.content if inp.lower() == "quit": break inp_pr = np.array([mod.bag_of_words(inp, mod.words)]) results = mod.model.predict(inp_pr) results_index = np.argmax(results) tag = mod.labels[results_index] for tg in mod.raw_data["intents"]: if tg['tag'] == tag: responses = tg['responses'] await client.say(client.get_channel(), "RoBot: " + str(random.choice(responses)))
def tokens(s): p = nlp.nlp(s) token = [] for i in p: token.append(i) for i in p: w = i synonyms = wn.synsets(w) l = list() t = [] for synset in synonyms: x = (synset.name().split('.')[0]) j = synset.hyponyms() k = sorted(lemma.name() for m in j for lemma in m.lemmas()) l.append(k) for k1 in k: t.append(k1) t1 = [] for i in t: if i not in t1: t1.append(i) for i in t1: token.append(i) token1 = [] for i in token: if i not in token1: token1.append(i) return token1
def dashboard(): data = request.args.to_dict() print "Recieved data" companyName = data['companyname'] cleanedCompanyName = nlp.nlp(companyName)[0].encode("utf-8") date = data['date'] print cleanedCompanyName print date code = findStock.findCode(cleanedCompanyName) page = findStock.findPage(date) if code is not None: print "Company code found. Sending price data back to client" ''' if (dt.datetime.today().strftime("%Y.%m.%d") == dt.datetime.strptime(date,"%Y.%m.%d").strftime("%Y.%m.%d")): finalData = findStock.todayInfo(code) finalData[0]['date'] = dt.datetime.strptime(date,"%Y.%m.%d").strftime("%Y.%m.%d") return json.dumps(finalData), 200 else: ''' finalData = findStock.pastInfo(code, page) return finalData, 200 else: print "code not found" return "Failure", 404
def pickle_texts(outfile, text): """ pickle the tens of thousands of inscriptions . . . . """ doc = nlp(text) with open(outfile, 'wb') as f: pickle.dump(doc, f)
def extract_code(text): logging.info("Processing result...") time1 = time.time() result = nlp(text) time2 = time.time() logging.info('Processing took %.3f ms' % ((time2 - time1) * 1000)) return parse_result(result)
def plot_wrap(): show = plotstock.plotgraph() sent = nlp() showsent = plotstock.plotsent() return render_template('plot.html', show=show, sent=sent, showsent=showsent)
def text_to_sents(): text = request.json.get("text", "") doc = nlp.nlp(text) nlp.all_sents.update({s.text: s for s in doc.sents}) sents = [s.text for s in doc.sents] return jsonify(sents)
def bin_inscriptions(corpus): """ put the texts into the docbin """ doc_bin = DocBin(attrs=["LEMMA", "TAG", "POS", "DEP", "HEAD"], store_user_data=True) for c in corpus: doc = nlp(c) doc_bin.add(doc) with open('dbg.bin', 'wb') as f: f.write(doc_bin.to_bytes())
def get_news_sentiment(): print("Analyzing news' sentiments...") df = retrieve_client_news() sentiment = [] for idx, row in df.iterrows(): if row.Content == '-----': sentiment.append('-----') else: s = SnowNLP(row.Content) sentences_sen = [] for sentence in s.sentences: ss = SnowNLP(sentence) sentences_sen.append(ss.sentiments) mean_score = np.mean(np.array(sentences_sen)) s = SnowNLP(row.Title) score = TITLE_WEIGHT * s.sentiments + CONTENT_WEIGHT * mean_score sentiment.append(score) df['Sentiment'] = sentiment print("Saving results...") df.to_csv(mp.DIR_DATA_CUSTOMERS + 'customer_related_news.csv', index=False, encoding='utf_8_sig') nlp()
def assess_paragraph_difficulty(self): """ see how many words and how many unique words there are """ assessment = [] for paragraph in text: doc = nlp(paragraph) total_words = 0 unique_words = [] unique_lemmata = [] for token in doc: if not token.is_punct: total_words += 1 if token.text not in unique_words: unique_words.append(token.text) if token.lemma_ not in unique_lemmata: unique_lemmata.append(token.lemma_) assessment.append(f"Total: {total_words}; Unique: {len(unique_words)}; Lemmata: {len(unique_lemmata)}") for a in assessment: print(a)
def formulate_response(self,question): grammar = JSGFParser('speech/hark-sphinx/grammar/NielsSebastiaan.gram') language_parsing = nlp() question = language_parsing.remove_name(language_parsing.remove_opts(question)) if(grammar.findToken(question) != None): #Question responses = {\ 'what time is it' : "The current time is: " + time.strftime("%H:%M:%S") + ".",\ 'what is the oldest most widely used drug on earth' : 'the oldest, most widely used drug on earth is coffee.',\ 'who are your creators' : 'My creators are Niels and Sebastiaan.'} return responses[question] else: words = question.split(' ') if(grammar.findTokenVar(words[0]) == '<verb>'): #command question = question.replace(words[0] + " to the ", "") if(grammar.findTokenVar(question) == '<location>'): return "I am moving to " + question + "." else: #request return "I am approaching the dining table."
def main_logic(): temp_text = say_str_queue.get() global_fuck = 0 # 作假变量 while temp_text is not None: func_num, *args = nlp(thu1, temp_text) # if global_fuck == 0: # start_obstacle_recognition() # 障碍物是什么 # global_fuck = 1 # elif global_fuck == 1: # start_general_recognition() # 前方有什么 # global_fuck = 2 # elif global_fuck == 2: # start_object_recognition(*args) # 人在那里 # global_fuck = 3 # elif global_fuck == 3: # start_limited_meter_object_recognition(*args) # 1米内有什么 # global_fuck = 4 # else: speak_str = direction('南邮广场') # if speak_str is not None: # logging.debug('室外导航开始播报: {}'.format(speak_str)) text_to_audio.put(speak_str) # if func_num == 0: # start_object_recognition(*args) # elif func_num == 1: # start_general_recognition() # elif func_num == 2: # start_indoor_navigation(*args) # elif func_num == 3: # start_outdoor_navigation(*args) # elif func_num == 4: # 米数图像识别 # start_limited_meter_object_recognition(*args) # elif func_num == 5: # 障碍物识别 # start_obstacle_recognition() temp_text = say_str_queue.get()
def analyze(): return nlp(request.data.decode('utf-8'))
def tokenize_magically(text): return [tok.text for tok in nlp(text)]
import discord from discord.ext import commands import os import asyncio import nlp as n import numpy as np import random token = "YOUR KEY HERE" #client = commands.Bot(command_prefix='?', description='A bot that greets the user back.') client = discord.Client() mod = n.nlp() context = {} @client.event async def on_ready(): activity = discord.Game(name="with 3D husbandos! owo") await client.change_presence(status=discord.Status.idle, activity=activity) print("Logged in as " + client.user.name) #servers = list(client.guilds) #print("Connected on " + str(len(client.guilds)) + " servers:") #for x in range(len(servers)): # print(' ' + servers[x-1].name) @client.event async def on_message(message): if message.channel.name == "bot_commands": if not message.author == client.user:
#! /usr/bin/env python # -*- coding: utf-8 -*- import nlp if __name__ == "__main__": nlp.nlp()
#coding: utf-8 #util from data import * from read_conf import config from optparse import OptionParser import csv import cPickle as pickle from operator import itemgetter from nlp import nlp from itertools import combinations import sys mnlp = nlp() dp = config("../conf/dp.conf") #rake from rake import Rake rake = Rake() #nltk import nltk #math import math from math import log print "读入数据文件" f = open(dp["word_tag"],"rb") word_tag = pickle.load(f)
def start(body): # Get the user, or create one if one does not exist. cellNumber = body['cellNumber'] currentUser = users.find_one({'cellNumber' : cellNumber}) if (currentUser == None): currentUser = newUser(cellNumber) # Load the question text question = body['question'].lower() # Check for 'help' if (question.lower() == 'help'): answer = "For a specific fact, ask a question like 'what is the population of"\ " London'\n" \ "For general information, ask for a description with 'describe "\ "London'\n" \ "If the contents is trimmed and you want more, send 'more'\n" \ "If an answer is of low quality, help improve the database by "\ "sending 'poor'" elif ('rate' in question): lastQuestion = currentUser['lastQuestion'] # Establish whether the rating is of the correct form splitQuestion = question.split(' ') if not (len(splitQuestion) == 2\ and splitQuestion[0] == 'rate'\ and splitQuestion[1].isdigit()\ and int(splitQuestion[1]) in range(1,6)): answer = "Feedback unrecognised. Send 'Rate' followed by a quality "\ "out of 5. E.g, for a bad quality answer, send 'Rate 1' "\ "or for a good quality answer, send 'Rate 5'" # Check that the criteria for rating the previous question is good, then process elif (lastQuestion['givenProperty'] != None)\ and (lastQuestion['returnedProperty'] != None)\ and (lastQuestion['receivedFeedback'] == False)\ and (lastQuestion['question'] != None)\ and (lastQuestion['answer'] != None):# TODO - this line and the line above - correct? False?! successful = adjustRanking( lastQuestion['question'], lastQuestion['answer'], lastQuestion['givenProperty'], lastQuestion['returnedProperty'], int(splitQuestion[1])) if (successful): currentUser['lastQuestion']['receivedFeedback'] = True updateUser(currentUser) answer = "Thank you for your feedback - it has been recorded." else: answer = "Feedback unrecognised. Send 'Rate' followed by a quality "\ "out of 5. E.g, for a bad quality answer, send 'Rate 1' "\ "or for a good quality answer, send 'Rate 5'" else: answer = "Feedback already received or not expected for this question." else: # Process the natural language in the question parsedQuestion = nlp.nlp(question) if parsedQuestion['success'] == False: answer = 'No answer was found' print 'NLP Failed' else: property = parsedQuestion['property'] placeDict = parsedQuestion['place'] wikiPlaceName = placeDict['wikiName'] realName = placeDict['realName'] print 'Finding argument ', property, ' on page ', wikiPlaceName answer, keyUsed = sourceProcessor.findArgumentOnPage(property,wikiPlaceName) updateUserWithLastQuestion(currentUser, wikiPlaceName, property, keyUsed, answer) return answer
def __init__(self, string): string = toolkit.ensure_unicode(string) self._doc = nlp.nlp(string) for ent in reversed(self._doc.ents): ent.merge(ent.root.tag_, ent.root.lemma_, ent.label_) self.tokens = [MutableToken(self, token.i) for token in self._doc]
def server(): return nlp.nlp(request.forms.get('story'))
c.Username = USERNAME c.Limit = 1 c.Hide_output = True c.Store_object = True # Run twint.run.Search(c) print("DONE INITIAL SEARCH") # Store output tweets = twint.output.tweets_list latest_tweet_date = " ".join(tweets[0].datetime.split(" ")[:2]) #print("Latest tweet: " + tweets[0].tweet) # Track the Ticker from the latest tweet result_list = nlp.nlp(tweets[0].tweet) for word in result_list: ticker = get_ticker(word) #print("Found ticker: {}".format(ticker)) q.put(ticker) t = PrettyTable( ['Tweet ', ' Detected Ticker']) t.align['Tweet '] = 'l' t.align[' Detected Ticker'] = 'r' t.hrules = 1 print(t) t.add_row([tweets[0].tweet, ticker]) print("\n".join(t.get_string().splitlines()[-2:]))
def train(self, data1, tdata, cdata): enl = tdata[0] enr = tdata[2] anl = tdata[4] anr = tdata[6] gnl = tdata[8] gnr = tdata[10] oll = tdata[12] olr = tdata[14] orl = tdata[16] orr = tdata[18] y_train = tdata[20] y_test = tdata[21] ohtr = tdata[22] tr_len = tdata[23] ohte = tdata[24] te_len = tdata[25] data = np.argmax(data1, 1) src = {0, 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} for i in range(2000): arr = random.sample(range(self.sjnum), self.sjnum - 1) a = [] b = [] c = [] d = [] e = [] f = [] a1 = [] b1 = [] c1 = [] d1 = [] e1 = [] f1 = [] g = [] j = 0 num = 0 totacc=0 while num < self.batch_size: if data[arr[j]] in src: a.append(enl[arr[j]]) a1.append(enr[arr[j]]) b.append(anl[arr[j]]) b1.append(anr[arr[j]]) c.append(gnl[arr[j]]) c1.append(gnr[arr[j]]) d.append(oll[arr[j]]) d1.append(olr[arr[j]]) e.append(orl[arr[j]]) e1.append(orr[arr[j]]) f.append(y_train[arr[j]]) f1.append(ohtr[arr[j]]) g.append(tr_len[arr[j]]) num = num + 1 j = j + 1 self.sess.run(self.optim, feed_dict={self.emgl: a, self.emgr: a1, self.accl: b, self.accr: b1, self.gyrl: c, self.gyrr: c1, self.oll: d, self.olr: d1, self.oril: e, self.orir: e1, self.target: f, self.label: f1, self.target_len: g, self.dropout: 0.5}) totacc = 0 a = [] b = [] c = [] f = [] a1 = [] b1 = [] c1 = [] f1 = [] d = [] e = [] d1 = [] e1 = [] aa = np.zeros(36) bb = np.zeros(36) g = [] da = [] num = 0 znum = 0 for j in range(self.sjnum): if data[j] in src: a.append(enl[j]) a1.append(enr[j]) b.append(anl[j]) b1.append(anr[j]) c.append(gnl[j]) c1.append(gnr[j]) d.append(oll[j]) d1.append(olr[j]) e.append(orl[j]) e1.append(orr[j]) f.append(y_train[j]) f1.append(ohtr[j]) g.append(tr_len[j]) da.append(data[j]) num = num + 1 znum = znum + 1 if num == self.batch_size: prob, rloss = self.sess.run([self.pprobs, self.ploss], feed_dict={self.emgl: a, self.emgr: a1, self.accl: b, self.accr: b1, self.gyrl: c, self.gyrr: c1, self.oll: d, self.olr: d1, self.oril: e, self.orir: e1, self.target: f, self.label: f1, self.target_len: g, self.dropout: 0.5}) for k in range(self.batch_size): nl = nlp.nlp(prob[k][:self.word_em - 1]) c = nl.getans() hb_maxa = self.hb(c, len(c)) aq ,_,_= self.lcs(f[k][1:], hb_maxa, g[k] - 1, len(hb_maxa)) aa[da[k]] = aa[da[k]] + aq bb[da[k]] = bb[da[k]] + 1 totacc = totacc + aq num = 0 a = [] b = [] c = [] d=[] e=[] f = [] a1 = [] b1 = [] c1 = [] e1 =[] d1=[] f1 = [] da = [] g = [] for j in range(36): print('seq ', j, '\'sacc:', aa[j] / bb[j], ' ', aa[j], ' ', bb[j]) totacc = totacc / (znum - num) print('epoch ', i, '\'s acc', totacc) totacc = 0 totir=0 totdr=0 for start, end in zip(range(0, self.sdnum, self.batch_size), range(self.batch_size, self.sdnum + 1, self.batch_size)): a = [] b = [] c = [] f = [] a1 = [] b1 = [] c1 = [] f1 = [] d=[] e=[] d1=[] e1=[] g = [] for j in range(start, end): a.append(cdata[0][j]) a1.append(cdata[1][j]) b.append(cdata[2][j]) b1.append(cdata[3][j]) c.append(cdata[4][j]) c1.append(cdata[5][j]) f.append(y_test[j]) f1.append(ohte[j]) d.append(cdata[6][j]) d1.append(cdata[7][j]) e.append(cdata[8][j]) e1.append(cdata[9][j]) g.append(te_len[j]) prob, rloss= self.sess.run([self.pprobs, self.ploss], feed_dict={self.emgl: a, self.emgr: a1, self.accl: b, self.accr: b1, self.gyrl: c, self.gyrr: c1, self.oll: d, self.olr: d1, self.oril: e, self.orir: e1, self.target: f, self.label: f1, self.target_len: g, self.dropout: 0.5}) for k in range(self.batch_size): nl = nlp.nlp(prob[k][:self.word_em - 1]) c = nl.getans() hb_maxa = self.hb(c, len(c)) aq,isr , idr = self.lcs(f[k][1:], hb_maxa, g[k] - 1, len(hb_maxa)) totacc = totacc + aq totir=totir + isr totdr=totdr +idr totacc = totacc / end totir = totir /end totdr = totdr /end print('epoch ', i, ' test\'s acc', totacc, ' test\'s ir', totir, ' test\'s dr', totdr) if totacc > self.bacc: self.bacc = totacc self.saver.save(self.sess, "Model_mxpool-ztd/model.ckpt") print('newest bacc:', self.bacc) return 0
from nlp import nlp from preprocessing import preprocess from scraper import scrape import plotly import plotly.express as px import plotly.graph_objs as go import json import get_db_data sentiment = nlp() df_stocks = get_db_data.get_stock_prices(sentiment) df_sent = get_db_data.get_14_day_sentiment(sentiment) def plotgraph(dfstocks=df_stocks): data = [go.Scatter(x=dfstocks["Date"], y=dfstocks["Close"])] graphJSON = json.dumps(data, cls=plotly.utils.PlotlyJSONEncoder) #fig = px.line(dfstocks,x="Date", y="Close") return graphJSON def plotsent(df_sent=df_sent): datasent = [go.Scatter(x=df_sent["index"], y=df_sent["sentiment"])] graphsentJSON = json.dumps(datasent, cls=plotly.utils.PlotlyJSONEncoder) #fig = px.line(dfstocks,x="Date", y="Close") return graphsentJSON
import translator import nlp import composit_image #import getimage import getimage_bing prime_sent=input("input a sentence: ") nlp_list=nlp.nlp(prime_sent) print(nlp_list) noun_list=[] for key,value in nlp_list[0].items(): if "Na" in value: noun_list.append(key) elif "Nb" in value: noun_list.append(key) elif "Nc" in value: noun_list.append(key) print(noun_list) trans_list=translator.trans(noun_list)
import os import re from itertools import combinations #rake from rake import Rake rake = Rake() #nltk import nltk from nltk.util import clean_html from nltk.util import clean_url #nlp from nlp import nlp mnlp = nlp() tag_re = re.compile(r"<p>(.+?)</p>", re.DOTALL) dp = config("../conf/dp.conf") #这个函数的作用是去重 #先读取title,然后和test的title相对比,看看有没有重的 def remove_duplicate(): dup = open(dp["dup_test"], "w") other = open(dp["other_test"], "w") w_dup = csv.writer(dup) w_other = csv.writer(other) #读取训练文件
def lemmatize(text): tokens = nlp(text) return [ token.lemma_.lower() for token in tokens if token.is_alpha and not token.pos_ == "PRON" ]
# %% import pickle from sklearn.feature_extraction.text import TfidfVectorizer import pandas as pd from nlp import nlp as nlp from collections import Counter from fuzzywuzzy import fuzz import Levenshtein as lev import spacy Spnlp = spacy.load("en_core_web_sm") from spacy.matcher import PhraseMatcher matcher = PhraseMatcher(Spnlp.vocab) import matplotlib.pyplot as plt from wordcloud import WordCloud LangProcessor = nlp() # %% #load the job description with open('identity.txt') as job: text = job.read() # %% #load cv with open('cv') as cv: cvtext = cv.read()
def train(self, data1, tdata, cdata): enl = tdata[0] enr = tdata[2] anl = tdata[4] anr = tdata[6] gnl = tdata[8] gnr = tdata[10] oll = tdata[12] olr = tdata[14] orl = tdata[16] orr = tdata[18] y_train = tdata[20] y_test = tdata[21] ohtr = tdata[22] tr_len = tdata[23] ohte = tdata[24] te_len = tdata[25] data = np.argmax(data1, 1) src = {0, 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} for i in range(2000): arr = random.sample(range(self.sjnum), self.sjnum - 1) a = [] b = [] c = [] d = [] e = [] f = [] a1 = [] b1 = [] c1 = [] d1 = [] e1 = [] f1 = [] g = [] j = 0 num = 0 totacc=0 while num < self.batch_size: if data[arr[j]] in src: a.append(enl[arr[j]]) a1.append(enr[arr[j]]) b.append(anl[arr[j]]) b1.append(anr[arr[j]]) c.append(gnl[arr[j]]) c1.append(gnr[arr[j]]) d.append(oll[arr[j]]) d1.append(olr[arr[j]]) e.append(orl[arr[j]]) e1.append(orr[arr[j]]) f.append(y_train[arr[j]]) f1.append(ohtr[arr[j]]) g.append(tr_len[arr[j]]) num = num + 1 j = j + 1 self.sess.run(self.optim, feed_dict={self.emgl: a, self.emgr: a1, self.accl: b, self.accr: b1, self.gyrl: c, self.gyrr: c1, self.oll: d, self.olr: d1, self.oril: e, self.orir: e1, self.target: f, self.label: f1, self.target_len: g, self.dropout: 0.5}) totacc = 0 a = [] b = [] c = [] f = [] a1 = [] b1 = [] c1 = [] f1 = [] d = [] e = [] d1 = [] e1 = [] aa = np.zeros(36) bb = np.zeros(36) g = [] da = [] num = 0 znum = 0 for j in range(self.sjnum): if data[j] in src: a.append(enl[j]) a1.append(enr[j]) b.append(anl[j]) b1.append(anr[j]) c.append(gnl[j]) c1.append(gnr[j]) d.append(oll[j]) d1.append(olr[j]) e.append(orl[j]) e1.append(orr[j]) f.append(y_train[j]) f1.append(ohtr[j]) g.append(tr_len[j]) da.append(data[j]) num = num + 1 znum = znum + 1 if num == self.batch_size: prob, rloss = self.sess.run([self.pprobs, self.ploss], feed_dict={self.emgl: a, self.emgr: a1, self.accl: b, self.accr: b1, self.gyrl: c, self.gyrr: c1, self.oll: d, self.olr: d1, self.oril: e, self.orir: e1, self.target: f, self.label: f1, self.target_len: g, self.dropout: 0.5}) for k in range(self.batch_size): nl = nlp.nlp(prob[k][:self.word_em - 1]) c = nl.getans() hb_maxa = self.hb(c, len(c)) aq = self.lcs(f[k][1:], hb_maxa, g[k] - 1, len(hb_maxa)) aa[da[k]] = aa[da[k]] + aq bb[da[k]] = bb[da[k]] + 1 totacc = totacc + aq num = 0 a = [] b = [] c = [] d=[] e=[] f = [] a1 = [] b1 = [] c1 = [] e1 =[] d1=[] f1 = [] da = [] g = [] for j in range(36): print('seq ', j, '\'sacc:', aa[j] / bb[j], ' ', aa[j], ' ', bb[j]) totacc = totacc / (znum - num) print('epoch ', i, '\'s acc', totacc) totacc = 0 for start, end in zip(range(0, self.sdnum, self.batch_size), range(self.batch_size, self.sdnum + 1, self.batch_size)): a = [] b = [] c = [] f = [] a1 = [] b1 = [] c1 = [] f1 = [] d=[] e=[] d1=[] e1=[] g = [] for j in range(start, end): a.append(cdata[0][j]) a1.append(cdata[1][j]) b.append(cdata[2][j]) b1.append(cdata[3][j]) c.append(cdata[4][j]) c1.append(cdata[5][j]) f.append(y_test[j]) f1.append(ohte[j]) d.append(cdata[6][j]) d1.append(cdata[7][j]) e.append(cdata[8][j]) e1.append(cdata[9][j]) g.append(te_len[j]) prob, rloss = self.sess.run([self.pprobs, self.ploss], feed_dict={self.emgl: a, self.emgr: a1, self.accl: b, self.accr: b1, self.gyrl: c, self.gyrr: c1, self.oll: d, self.olr: d1, self.oril: e, self.orir: e1, self.target: f, self.label: f1, self.target_len: g, self.dropout: 0.5}) for k in range(self.batch_size): nl = nlp.nlp(prob[k][:self.word_em - 1]) c = nl.getans() hb_maxa = self.hb(c, len(c)) aq = self.lcs(f[k][1:], hb_maxa, g[k] - 1, len(hb_maxa)) totacc = totacc + aq totacc = totacc / end print('epoch ', i, ' test\'s acc', totacc) if totacc > self.bacc: self.bacc = totacc self.saver.save(self.sess, "Model_biLSTM_fc/model.ckpt") print('newest bacc:', self.bacc) return 0
def single_panel(prime_sent, return_list): noun_list = nlp.nlp(prime_sent) trans_list = translator.trans(noun_list) print(trans_list) image_path_list = [] for word in trans_list: if word == "up" or word == "down" or word == "left" or word == "right" or word == "plus": path = firebase.get_arrow(word) else: path = firebase.get_icon(word) if (path == None): path = getimage_bing.crawler_bing(word) firebase.post_address(path, word) path = str(path).replace('C:\\Users\\a1235\\Desktop\\P\\', "./") path = str(path).replace('\\', "/") image_path_list.append(path) print(image_path_list) # composite_image_path = composit_image.composit_icon(image_path_list) # step_panel = Image.new('RGB', (394, 493), (255, 255, 255)) # step_image = Image.open(composite_image_path) # step_border = ImageDraw.Draw(step_panel) # step_border.line([(0, 101), (389, 101)], fill=(117, 0, 0), width=5) # # set border # # x-top # step_border.line([(0, 2.5), (394, 2.5)], fill=(117, 0, 0), width=5) # # x-bottom # step_border.line([(0, 490.5), (394, 490.5)], fill=(117, 0, 0), width=5) # # y-left # step_border.line([(2.5, 0), (2.5, 493)], fill=(117, 0, 0), width=5) # # y-right # step_border.line([(391.5, 0), (391.5, 493)], fill=(117, 0, 0), width=5) # font = ImageFont.truetype("microblack.ttf", 35) # step_text = ImageDraw.Draw(step_panel) # step_text.text((5, 5), prime_sent, font=font, fill=(0, 0, 0), align="center") # step_panel.paste(step_image, (5, 104)) # theTime = datetime.datetime.now() # str_time = str(theTime).replace(".", "_") # str_time = str_time.replace(":", "_") # folder_path = "./panel_image/"+str_time # if(os.path.exists(folder_path) == False): # os.makedirs(folder_path) # image_path = folder_path+"/merge.jpg" # step_panel.save(image_path) return_list.append(image_path_list)
def test(self,tdata,emgl,emgr,accl,accr,gyrl,gyrr,oril,orir,ol,or1): enl = tdata[0] enr = tdata[2] anl = tdata[4] anr = tdata[6] gnl = tdata[8] gnr = tdata[10] oll = tdata[12] olr = tdata[14] orl = tdata[16] orr = tdata[18] y_train = tdata[20] y_test = tdata[21] ohtr = tdata[22] tr_len = tdata[23] ohte = tdata[24] te_len = tdata[25] a = [] b = [] c = [] d = [] e = [] f = [] a1 = [] b1 = [] c1 = [] d1 = [] e1 = [] f1 = [] g = [] j = 0 num = 0 totacc=0 a.append(emgl) a1.append(emgr) b.append(accl) b1.append(accr) c.append(gyrl) c1.append(gyrr) d.append(ol) d1.append(or1) e.append(oril) e1.append(orir) f.append(y_train[0]) f1.append(ohtr[0]) g.append(tr_len[0]) for j in range(self.batch_size-1): a.append(enl[j]) a1.append(enr[j]) b.append(anl[j]) b1.append(anr[j]) c.append(gnl[j]) c1.append(gnr[j]) d.append(oll[j]) d1.append(olr[j]) e.append(orl[j]) e1.append(orr[j]) f.append(y_train[j]) f1.append(ohtr[j]) g.append(tr_len[j]) prob,loss=self.sess.run([self.pprobs, self.ploss], feed_dict={self.emgl: a, self.emgr: a1, self.accl: b, self.accr: b1, self.gyrl: c, self.gyrr: c1, self.oll: d, self.olr: d1, self.oril: e, self.orir: e1, self.target: f, self.label: f1, self.target_len: g, self.dropout: 0.5}) nl = nlp.nlp(prob[0][:self.word_em - 1]) c = nl.getans() hb_maxa = self.hb(c, len(c)) result=[mp[a] for a in hb_maxa] print('result is:',result) return ''.join(result)
file = "output_audio.mp3" tts.save(file) playsound.playsound(file) os.remove(file) # speech recognition def get_audio(): with sr.Microphone() as source: audio = r.listen(source) voice_data = '' try: voice_data = r.recognize_google(audio) except sr.UnknownValueError: pass except sr.RequestError: print("Sorry, My services are down") return voice_data time.sleep(1) # calling these functions login() ava("How can I help you?") while 1: # runs this infinitely voice_data = get_audio() nlp(voice_data) # print what the user said print(voice_data)
def parse(m): prs = nlp(m) return (prs['sentences'][0]['parse'])