app.py

from newspaper import Article
from newspaper import Config
import gradio as gr
import os
import openai
openai.api_key = os.getenv('api_token')
def extract_article_text(url):
  USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
  config = Config()
  config.browser_user_agent = USER_AGENT
  config.request_timeout = 10

  article = Article(url, config=config)
  article.download()
  article.parse()
  text = article.text
  return text

def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0.5, # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]

def prompt_summary(url,movie):
  text = extract_article_text(url)
  text = text[:4096]
  prompt_sum = f"""
  Summarize the text {text} as if a 8-year old kid understands. The summary should be atmost 200 words and should help the kid understand how the summary could help him solve a real-world problem. Here is the format:
  1.Importance of the article:
  2.Real world scenario:
  3.Key takeaway:
  """

  prompt_mov = f"""
  Convert the technical article {text} into a short story involving the characters from the movie {movie}. The story should not exceed 200 words and should be written in a way that captures the essence of the article while also making it engaging and entertaining.
  """
  prompt_topic = f"""
  Extract 5 key topics from the text {text}. The topics should clearly tell the user why it is important to read the article. Length of the topic should be limited to a single word
  """
  response_top = get_completion(prompt_topic)
  response_mov = get_completion(prompt_mov)
  response_sum = get_completion(prompt_sum)
  return response_top, response_sum, response_mov

inputs = [
    gr.inputs.Textbox(label="Article URL"),
    gr.inputs.Textbox(label="Which is your favorite movie?")
]

outputs = [
    gr.outputs.Textbox(label="Key topics"),
    gr.outputs.Textbox(label="Summary without jargon"),
    gr.outputs.Textbox(label="Summary as movie synopsis")
]

gr.Interface(prompt_summary, inputs, outputs, title="Article Cortex", description="Helps you understand any technical article as if it were a movie synopsis.").launch(debug=True)