app.py

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


# define the text summarizer function
def text_prompt(request, page_url, contraseña, temp):
    try:
        page = newspaper.Article(url=page_url)
        page.download()
        page.parse()
    except Exception as e:
        return "", f"--- Ha ocurrido un error al procesar la URL: {e} ---", ""

    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

    tokens = tokenizer.tokenize(page.text)
    
    # Recortar el texto a un máximo de 1000 tokens
    num_tokens = len(tokens)
    if num_tokens > 1800:
    tokens = tokens[:1800]
    page_text = " ".join(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" + page_text,
                max_tokens=2048,
                temperature=temp,
                top_p=0.9,
            )
            # get the response text
            response_text = response.choices[0].text
            # clean the response text
            response_text = re.sub(r'\s+', ' ', response_text)
            return page.text, response_text, num_tokens
        except Exception as e:
            return page.text, f"--- Ha ocurrido un error al procesar la solicitud: {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="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 info extraction",
    description="This tool allows querying the text retrieved from the URL 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 2000 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."
)

# captura de errores en la integración como componente

error_message = ""

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