import gradio as gr
import requests
import openai
from deep_translator import GoogleTranslator

review =  None
review2 = None
user = None
image= None
link = None
response2 = None
response3 = None
traductor = GoogleTranslator(source='en', target='es')
traduccion = None 
calificacion = None 
creacion = None


def generate_text(text):
    url = f"https://api.yelp.com/v3/businesses/{text}/reviews?limit=1&sort_by=yelp_sort"

    headers = {
        "accept": "application/json",
        "Authorization": "Bearer F8JdKZ15jG6YRSwEg3_s0GZJOFC5SxUw8vo2VPETCM2Jm_DXslytWRiWL6b7hqJnCnGhDGKIYa8KW7aIvoSDyXDYxxK9gm5ok6xnvKka2_LiYhGRaei1oUaaCW6KY3Yx"
    }

    response = requests.get(url, headers=headers)
    response = response.json()
    review = response['reviews'][0]['text']
    user = response['reviews'][0]['user']['name']
    image = response['reviews'][0]['user']['image_url']
    link = response['reviews'][0]['url']
    calificacion = response['reviews'][0]['rating']
    creacion = response['reviews'][0]['time_created']
    openai.api_key = "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"
    traduccion = traductor.translate(review)
    response2 = openai.Completion.create(
        model="text-davinci-003",
        prompt= f"Responder la siguiente reseña. nombre:{user}. {review}",
        temperature=0.5,
        max_tokens=300,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0
    )
    response2 = response2.choices[0].text
    


    return  review,user,image,response2,traduccion,calificacion,creacion




with gr.Blocks() as demo:
    gr.Markdown(
        """
<p align="center">
    <img src="https://neurona-ba.com/wp-content/uploads/2021/07/HenryLogo.jpg">
    <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/a/ad/Yelp_Logo.svg/465px-Yelp_Logo.svg.png?20210803213252">
</p>
<p align="center">
    <img width=1024 src="https://raw.githubusercontent.com/BonfantiMatias/images/0b4c42f7b313156ea6fb396d32d98221783fb5cc/gradio1.png">
</p>
"""
    )
    gr.Markdown(
        """
        <p align="center">
        </p>
        <p align="center">
        </p>
        """
    )
    gr.Markdown("# Paso 1: Ingresar el Negocio")
    gr.Markdown(
    """
- Puede ingresar el Negocio por su Alias o ID de [YELP.com](https://www.yelp.com)
- Precionar el Boton 'Iniciar Modelo' que se encuentra al final de la pagina
"""
    )
    with gr.Column():
            seed = gr.Textbox(label="Introducir el nombre de la empresa o su Id")
            with gr.Row():        
                    gr.Examples(["la-fábrica-del-taco-buenos-aires"], inputs=[seed])
                    gr.Examples(["versailles-miami-4"], inputs=[seed])
                    gr.Examples(["la-casa-de-toño-ciudad-de-méxico-4"], inputs=[seed])
                    gr.Examples(["osteria-del-bugiardo-verona"], inputs=[seed])
                    gr.Examples(["arume-barcelona"], inputs=[seed])   

    

    gr.Markdown("# Mediante la Api Yelp Fusion se obtienen los datos de la ultima review del local seleccionado")
    
            
    
    with gr.Row():
        
        with gr.Column():
            user = gr.Textbox(label="Id Cliente" )            
            review = gr.Textbox(label="Reseña Cliente")            
            calificacion = gr.Textbox(label="Calificacion de la reseña")         
            creacion = gr.Textbox(label="Fecha de creacion de la reseña")
        with gr.Column():
            image = gr.Image(label="Avatar Cliente", lines= 7, max_lines=10)
        
            
        
    gr.Markdown("# Traduccion de la review al idioma español")           
    with gr.Row():
            traduccion = gr.Textbox(label="Traduccion Review")

    gr.Markdown("# Respuesta a la review implementando el modelo GPT-3 de OpenAi")
    with gr.Row():
            response2 = gr.Textbox(label="Respuesta GPT-3")
            
    with gr.Row():
            btn = gr.Button("Iniciar Modelo")
            btn.click(generate_text, inputs=[seed], outputs=[review,user,image,response2,traduccion,calificacion,creacion])        
  
    
if __name__ == "__main__":
    demo.launch()