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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)
num_tokens = len(tokens)
if num_tokens > 10 and num_tokens < 2000:
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, "--- Max 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]
],
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 |