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| import nltk | |
| nltk.download('punkt') | |
| nltk.download('averaged_perceptron_tagger') | |
| nltk.download('brown') | |
| import spacy | |
| from spacy import displacy | |
| from collections import Counter | |
| import en_core_web_sm | |
| nlp = en_core_web_sm.load() | |
| from transformers import pipeline | |
| summarization = pipeline("summarization", model = "facebook/bart-large-cnn") | |
| from textblob import TextBlob | |
| import gradio as gr | |
| def story(txt): | |
| #Find all the names that appear in the story and define the function for finding the most appeared name | |
| doc = nlp(txt) | |
| labellist = [] | |
| namelist = [] | |
| orglist = [(X.text, X.label_) for X in doc.ents] | |
| def most_common(List): | |
| return max(set(List), key=List.count) | |
| for i in orglist: | |
| if i[1] == ("PERSON"): | |
| labellist.append(i[0]) | |
| for i in labellist: | |
| if i not in namelist: | |
| namelist.append(i) | |
| #Generate a short summary for the story | |
| summary = summarization(txt) | |
| #Determine whether the story is positive, negative or neutral | |
| count = 0 | |
| count2 = 0 | |
| blob = TextBlob(txt) | |
| for sentence in blob.sentences: | |
| for i in sentence.sentiment: | |
| if sentence.sentiment[0]>0: | |
| count += 1 | |
| if sentence.sentiment[0]<0: | |
| count2 += 1 | |
| if count>count2: | |
| sentiment = ("The story is positive.") | |
| elif count<count2: | |
| sentiment = ("The stroy is negative.") | |
| else: | |
| sentiment = ("The story is neutral.") | |
| #Determine the output | |
| output = ("All names appeared:", namelist, "The most appeared name is", most_common(labellist)+".", "Short summary:", summary, sentiment) | |
| return(output) | |
| #create web app using Gradio | |
| demo = gr.Interface(fn = story, inputs="text", outputs="text") | |
| demo.launch() |