Spaces:
Runtime error
Runtime error
| import nltk | |
| import numpy as np | |
| import string # to process standard python strings | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| f=open('skin cancer.txt','r',errors = 'ignore') | |
| raw=f.read() | |
| raw=raw.lower()# converts to lowercase | |
| sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences | |
| word_tokens = nltk.word_tokenize(raw)# converts to list of words | |
| 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))) | |
| GREETING_INPUTS = ("hello", "hi", "greetings", "sup", "what's up","hey","ai","next") | |
| GREETING_RESPONSES = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me","You're welcome, this is my job","You'd better talk with the doctor and you need further treatment"] | |
| def greeting(sentence): | |
| for word in sentence.split(): | |
| for i in range(len(GREETING_INPUTS)): | |
| if word.lower() == GREETING_INPUTS[i]: | |
| return GREETING_RESPONSES[i] | |
| def response(user_response): | |
| robo_response='' | |
| sent_tokens.append(user_response) | |
| TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english') | |
| tfidf = TfidfVec.fit_transform(sent_tokens) | |
| vals = cosine_similarity(tfidf[-1], tfidf) | |
| idx=vals.argsort()[0][-2] | |
| flat = vals.flatten() | |
| flat.sort() | |
| req_tfidf = flat[-2] | |
| if(req_tfidf==0): | |
| robo_response=robo_response+"I am sorry! I don't understand you" | |
| return robo_response | |
| else: | |
| robo_response = robo_response+sent_tokens[idx] | |
| return robo_response | |
| # cnn part | |
| import numpy as np | |
| import pandas as pd | |
| from tensorflow.keras import models | |
| from tensorflow.keras.preprocessing import image | |
| from PIL import Image, UnidentifiedImageError | |
| # load the model | |
| loaded_model = models.load_model('728cnn.h5') | |
| def get_class(str): | |
| try: | |
| test_image = Image.open(str) | |
| Image.open | |
| except BaseException: | |
| return 'false' | |
| else: | |
| test_image = test_image.resize((28, 28)) | |
| test_image = image.img_to_array(test_image) | |
| test_image = test_image.reshape(-1, 28, 28, 3) | |
| test_image = test_image/255 | |
| # predict the result | |
| result = loaded_model.predict(test_image) | |
| # cancer classes | |
| classes = {4: ('nv', 'melanocytic nevi'), | |
| 6: ('mel', 'melanoma'), | |
| 2: ('bkl', 'benign keratosis-like lesions'), | |
| 1: ('bcc' , 'basal cell carcinoma'), | |
| 5: ('vasc', 'pyogenic granulomas and hemorrhage'), | |
| 0: ('akiec', 'Actinic keratoses and intraepithelial carcinomae'), | |
| 3: ('df', 'dermatofibroma')} | |
| return classes.get(np.argmax(result))[1] | |
| def is_path(str): | |
| pics = ['bmp','png','jpg','jpeg','tiff','gif', 'pcx', 'tga', 'exif', 'fpx', 'svg','psd','cdr','pc','dxf','ufo','eps','ai','raw'] | |
| if (str.find('.') == -1): | |
| return -1 | |
| elif(str[str.rfind('.')+1::] in pics): | |
| return str | |
| else: | |
| return -1 | |
| #/Users/yuxizheng/xizheng/proj_past_7007/Week_5/test_pics_with_label/ISIC_0034299_bcc_1.jpg | |
| def chat(user_response): | |
| rob_response = "DOCTOR STRANGE: Hi! I am a chatbot to tell you the diagnosis, please show me your skin picture." | |
| # check input is path | |
| path = is_path(user_response) | |
| if (path == -1): | |
| user_response = user_response.lower() | |
| # process user response | |
| if (user_response != 'bye'): | |
| if (user_response == 'thanks' or user_response == 'thank you'): | |
| flag = False | |
| rob_response = "DOCTOR STRANGE: You are welcome, this is my job" | |
| elif (path != -1): | |
| r = get_class(path) | |
| rob_response = "DOCTOR STRANGE: Please wait few second, your picture is processing." | |
| if (r == 'false'): | |
| rob_response ="DOCTOR STRANGE: Sorry, cannot find the picture through your input path. Please try again." | |
| else: | |
| rob_response ="DOCTOR STRANGE: The diagnosis shows that you are having " + r + '\n' + "DOCTOR STRANGE: " + response(r) | |
| sent_tokens.remove(r) | |
| else: | |
| if (greeting(user_response) != None): | |
| rob_response ="DOCTOR STRANGE: " + greeting(user_response) | |
| else: | |
| rob_response ="DOCTOR STRANGE: " +response(user_response) | |
| sent_tokens.remove(user_response) | |
| else: | |
| rob_response ="DOCTOR STRANGE: Bye! take care." | |
| return rob_response | |