Create app.py

app.py

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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
+
+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
+
+  nlp = en_core_web_sm.load()
+  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()