app.py

import gradio as gr
import os
import openai
from newspaper import Article
import json
import re
from transformers import GPT2Tokenizer
import requests


# define the text summarizer function
def text_prompt(request, page_url, contraseña, temp):
    try:
        headers = {'User-Agent': 'Chrome/83.0.4103.106'}
        response = requests.get(page_url, headers=headers)
        html = response.text

        page = Article('')
        page.set_html(html)
        page.parse()

    except Exception as e:
        return "", f"--- An error occurred while processing the URL: {e} ---", ""
    
    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
    sentences = page.text.split('.')
    
    tokens = []
    page_text = ""
    
    for sentence in sentences:
        tokens.extend(tokenizer.tokenize(sentence))
        
        # Trim text to a maximum of 1800 tokens
        if len(tokens) > 1800:
            break
        page_text += sentence + ". "
        
    # Delete the last space
    page_text = page_text.strip()

    num_tokens = len(tokens)

    if num_tokens > 10:
        openai.api_key = contraseña
        # get the response from openai API
        try:
            response = openai.Completion.create(
                engine="text-davinci-003",
                prompt=request + "\n\n" + ">>\n" + page_text + "\n<<",
                max_tokens=2048,
                temperature=temp,
                top_p=0.9,
            )
            # get the response text
            response_text = response.choices[0].text
            total_tokens = response["usage"]["total_tokens"]

            # clean the response text
            response_text = re.sub(r'\s+', ' ', response_text)
            response_text = response_text.strip()
            return page.text, response_text, total_tokens
        except Exception as e:
            return page.text, f"--- An error occurred while processing the request: {e} ---", num_tokens
    return page.text, "--- Min number of tokens:", num_tokens

# define the gradio interface
iface = gr.Interface(
    fn=text_prompt,
    inputs=[gr.Textbox(lines=1, placeholder="Enter your prompt here...", label="Prompt:", type="text"),
            gr.Textbox(lines=1, placeholder="Enter the URL here...", label="URL to parse:", type="text"),
            gr.Textbox(lines=1, placeholder="Enter your API-key here...", label="API-Key:", type="password"),
            gr.Slider(0.0,1.0, value=0.3, label="Temperature:")
            ],
    outputs=[gr.Textbox(label="Input:"), gr.Textbox(label="Output:"), gr.Textbox(label="Total Tokens:")],
    examples=[["Summarize the following text as a list:","https://blog.google/outreach-initiatives/google-org/our-commitment-on-using-ai-to-accelerate-progress-on-global-development-goals/","",0.3],
            ["Generate a summary of the following text. Give me an overview of main business impact from the text following this template:\n- Summary:\n- Business Impact:\n- Companies:", "https://ai.googleblog.com/2019/10/quantum-supremacy-using-programmable.html","",0.7],
            ["Generate the next insights based on the following text. Indicates N/A if the information is not available in the text.\n- Summary:\n- Acquisition Price:\n- Why is this important for the acquirer:\n- Business Line for the acquirer:\n- Tech Focus for the acquired (list):","https://techcrunch.com/2022/09/28/eqt-acquires-billtrust-a-company-automating-the-invoice-to-cash-process-for-1-7b/","",0.3]
    ],
    title="ChatGPT / GPT-3 info extraction from URL",
    description="This tool allows querying the text retrieved from the URL with newspaper3k lib and using OpenAI's [text-davinci-003] engine.\nThe URL text can be referenced in the prompt as \"following text\".\nA GPT2 tokenizer is included to ensure that the 1.800 token limit for OpenAI queries is not exceeded. Provide a prompt with your request, the url for text retrieval, your api-key and temperature to process the text."
)

# error capturing in integration as a component

error_message = ""

try:
    iface.queue(concurrency_count=20)
    iface.launch()
except Exception as e:
    error_message = "An error occurred: " + str(e)
    iface.outputs[1].value = error_message