app.py

import gradio as gr
import lemminflect
import spacy
from transformers import pipeline
import wikipedia

nlp = spacy.load("en_core_web_sm")
sentiment_analyzer = pipeline(
  "sentiment-analysis",
  model="distilbert-base-uncased-finetuned-sst-2-english",
  revision="af0f99b"
)

def is_positive(text):
  return sentiment_analyzer(text)[0]["label"] == "POSITIVE"

def make_past_tense(token):
  if token.tag_ in ("VBP", "VBZ"):
    return f'{token._.inflect("VBD")} '
  return token.text_with_ws

def make_dystopian(term, text):
  doc = nlp(text)
  if is_positive(term):
    return "".join([make_past_tense(token) for token in doc])
  return doc.text_with_ws

def get_summary(term):
  if not term:
    return ""
  try:
    results = wikipedia.search(term)
  except wikipedia.exceptions.DisambiguationError as e:
    return e.error
  if len(results) > 0:
    summary = wikipedia.summary(results[0], sentences=1, auto_suggest=False, redirect=True)
    return make_dystopian(term, summary)
  return "Could not find an article on the term provided."

def launch_demo():
  title = "Dystopedia"
  description = (
    "Make any Wikipedia topic dystopian. "
    "Inspired by [this Tweet](https://twitter.com/lbcyber/status/1115015586243862528)"
  )
  examples = ["joy", "hope", "peace", "Earth", "water", "food"]
  gr.Interface(
    fn=get_summary,
    inputs=gr.Textbox(label="term", placeholder="Enter a term...", max_lines=1),
    outputs=gr.Textbox(label="description"),
    title=title,
    description=description,
    examples=examples,
    cache_examples=True,
    allow_flagging="never",
  ).launch()

launch_demo()