import requests
import os
import gradio as gr
from newspaper import Article
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
from dotenv import load_dotenv

load_dotenv()


OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36'
}

def summarize (article_url):
    session = requests.Session()

    try:
        response = session.get(article_url, headers = headers, timeout = 10)

        if response.status_code == 200:
            article = Article(article_url)
            article.download()
            article.parse()

            # print(f'Title: {article.title}')
            # print(f'Content: {article.text}')

        else:
            print(f'Failed to retrieve article at url: {article_url}')
    except Exception as e:
        print(f'Error fectching article at url: {article_url}')

    article_title =  article.title
    article_text = article.text

    template = """You are a very good assistant that summarizes online articles 

    Here's the article you want to summarize

    ============
    Title: {article_title}

    {article_text}

    ============

    Write a summary of the previous article in a bulleted list.

    """

    prompt = template.format(article_title = article_title, article_text = article_text)


    messages = [HumanMessage(content= prompt)]

  
    chat = ChatOpenAI(model_name = 'gpt-3.5-turbo', temperature=0)

    summary = chat(messages)

    return(summary.content)


demo = gr.Interface(
    fn= summarize,
    inputs = ['text'],
    outputs = ['text'],
    article = 'The project takes in a url of an article or blog and returns a summary of it.'
)

demo.launch()