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| import pyjokes | |
| import gradio as gr | |
| import numpy as np | |
| from nltk.corpus import wordnet as wn | |
| from sentence_transformers import SentenceTransformer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| import nltk | |
| nltk.download('all') | |
| import string | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| import random | |
| # import fastai | |
| def similarity(input, joke): | |
| return cosine_similarity(input, joke) | |
| def get_best(input): | |
| model = SentenceTransformer('bert-base-nli-mean-tokens') | |
| max_similarity = -1 | |
| max_idx = 0 | |
| jokes = pyjokes.get_jokes(language='en', category='all') | |
| jokes_embedding = model.encode(jokes) | |
| input_embedding = model.encode(input) | |
| for idx, joke_embedding in enumerate(jokes_embedding): | |
| sim = similarity(joke_embedding.reshape(-1, 1), | |
| input_embedding.reshape(-1, 1)) | |
| if(np.sum(sim) > np.sum(max_similarity)): | |
| max_idx = idx | |
| max_similarity = sim | |
| if(np.sum(max_similarity) != -1): | |
| return jokes[max_idx]+'😁🤣' | |
| else: | |
| return None | |
| def generate_list(input): | |
| result = [] | |
| n = len(input) | |
| for Len in range(2, n + 1): | |
| for i in range(n - Len + 1): | |
| j = i + Len - 1 | |
| tem = "" | |
| for k in range(i, j + 1): | |
| tem += input[k] | |
| result.append(tem) | |
| return result | |
| def pattern(input): | |
| response = input | |
| for substr in generate_list(input): | |
| try: | |
| syn = wn.synsets(substr)[1].hypernyms()[0].hyponyms()[ | |
| 0].hyponyms()[0].lemmas()[0].name() | |
| except: | |
| continue | |
| if(syn != None): | |
| response = response.replace(substr, syn.upper()) | |
| break | |
| if(input == response): | |
| return None | |
| else: | |
| return response+'??😁🤣' | |
| lemmer = nltk.stem.WordNetLemmatizer() | |
| def LemTokens(tokens): | |
| return [lemmer.lemmatize(token) for token in tokens] | |
| remove_punct_dict= dict((ord(punct), None) for punct in string.punctuation) | |
| def LemNormalize(text): | |
| return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict))) | |
| def NLTK(input): | |
| f = open('corpus.txt', errors='strict') | |
| data = f.read() | |
| data = data.lower() | |
| data = data + input.lower() | |
| sent_tokens = nltk.sent_tokenize(data) | |
| return bot(sent_tokens, input) | |
| def bot(sent_tokens, input): | |
| robo1_response = '' | |
| TfidfVec = TfidfVectorizer(tokenizer = LemNormalize, stop_words='english') | |
| tfidf = TfidfVec.fit_transform(sent_tokens) | |
| vals = cosine_similarity(tfidf[-1], tfidf) | |
| idx = random.randint(0, len(vals.argsort()[0])) | |
| flat = vals.flatten() | |
| flat.sort() | |
| req_tfidf = flat[-1] | |
| if (req_tfidf == 0): | |
| robo1_response= robo1_response+"I could not answer this right now but you can contact the head of our dept (PUSPHA RAJ)." # add the dept recommendation engine and contact details | |
| return robo1_response | |
| else: | |
| robo1_response = robo1_response+sent_tokens[idx] | |
| return robo1_response | |
| def generator(input=None): | |
| response = [] | |
| if input: | |
| out1 = NLTK(input) | |
| if(out1): | |
| response.append(out1) | |
| out2 = pattern(input) | |
| if(out2): | |
| response.append(out2) | |
| out3 = get_best(input) | |
| if(out3): | |
| response.append(out3) | |
| else: | |
| out1 = NLTK("Hi, what's the matter") | |
| if(out1): | |
| response.append(out1) | |
| out2 = pyjokes.get_joke(language='en', category='all') | |
| if(out2): | |
| response.append(out2) | |
| return response # think of doing this | |
| iface = gr.Interface(fn=generator, inputs="text", outputs="text") | |
| iface.launch() | |