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, system_role, 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 3000 tokens
        if len(tokens) > 3000:
            break
        page_text += sentence + ". "
        
    # Delete the last space
    page_text = page_text.strip()

    num_tokens = len(tokens)

    if num_tokens > 10 and contraseña.startswith("sk-"):
        openai.api_key = contraseña
        # get the response from openai API
        try:
            response = openai.ChatCompletion.create(
                model="gpt-3.5-turbo",
                messages=[
                    {"role": "system", "content": system_role},
                    {"role": "user", "content": request + "\n\n" + 'Text:\n\n"' + page_text + '\n"'}
                ],
                max_tokens=512,
                temperature=temp,
                top_p=1.0,
            )
            # get the response text
            response_text = response['choices'][0]['message']['content']
            total_tokens = response["usage"]["total_tokens"]

            # clean the response text
            response_text = re.sub(r'\s+', ' ', response_text)
            response_text = f"#### [{page.title}]({page_url})\n\n{response_text.strip()}"
            total_tokens_str = str(total_tokens) + " (${:.2f} USD)".format(total_tokens/1000*0.002)


            return page.text, response_text, total_tokens_str
        except Exception as e:
            return page.text, f"--- An error occurred while processing the request: {e} ---", num_tokens
    return page.text, "--- Check API-Key or Min number of tokens:", str(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 your system-role description here...", label="System Role:", type="text"),
            gr.Textbox(lines=1, placeholder="Enter the Article's URL here...", label="Article's 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.Markdown(label="Output:"), gr.Markdown(label="Total Tokens:")],
    examples=[["Resumen el siguiente texto en un máximo de 100 palabras.", "Actuar como consultor de negocio. La respuesta deberá aparentar ser novedosa. Formatea la respuesta en Markdown. El texto deberá ser traducido siempre al español. Deberás añadir al final una lista de topics del texto en forma de lista separada por comas.", "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 the main business impact from the text following this template:\n- Summary:\n- Business Impact:\n- Companies:", "Act as a Business Consultant", "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):","Act as a Business Consultant", "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 from URL",
    description="This tool allows querying the text retrieved from the URL with newspaper3k lib and using OpenAI's [gpt-3.5-turbo] 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 description for the system role, 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