import gradio as gr
from haystack.nodes import BM25Retriever, FARMReader
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.pipelines import ExtractiveQAPipeline
from abc import ABC, abstractmethod

import certifi
ca_certs=certifi.where()

class Config():
    es_host = "ask2democracy.es.us-central1.gcp.cloud.es.io"
    es_user = "elastic"
    es_password = "siKAHmmk2flwEaKNqQVZwp49"
    proposals_index = "petrolfo"
    #reader_model_name_or_path = "deepset/roberta-base-squad2"
    reader_model_name_or_path = "deepset/xlm-roberta-large-squad2"
    use_gpu = True
    
    
class DocumentQueries(ABC):
    
    @abstractmethod
    def search_by_query(self, query : str, retriever_top_k: int, reader_top_k: int, es_index: str):
        pass

class ExtractiveProposalQueries(DocumentQueries):
    
    def __init__(self, es_host: str, es_index: str, es_user, es_password, reader_name_or_path: str, use_gpu = False) -> None:
        reader = FARMReader(model_name_or_path = reader_name_or_path, use_gpu = use_gpu, num_processes=1)
        self._initialize_pipeline(es_host, es_index, es_user, es_password, reader = reader)
        
    
    def _initialize_pipeline(self, es_host, es_index, es_user, es_password, reader = None):
        if reader is not None:
            self.reader = reader
        self.es_host = es_host
        self.es_user = es_user
        self.es_password = es_password
        self.document_store = ElasticsearchDocumentStore(host = es_host, username=es_user, password=es_password, index = es_index, port = 443, scheme='https', verify_certs=True, ca_certs=ca_certs)        
        self.retriever = BM25Retriever(document_store = self.document_store)
        self.pipe = ExtractiveQAPipeline(self.reader, self.retriever)

    def search_by_query(self, query : str, retriever_top_k: int, reader_top_k: int, es_index: str = None) :
        if es_index is not None:
            self._initialize_pipeline(self.es_host, es_index, self.es_user, self.es_password)
        params = {"Retriever": {"top_k": retriever_top_k}, "Reader": {"top_k": reader_top_k}}
        prediction = self.pipe.run( query = query, params = params)      
        return prediction["answers"]



query = ExtractiveProposalQueries(es_host = Config.es_host, es_index = Config.proposals_index,
                                  es_user = Config.es_user, es_password = Config.es_password,
                                  reader_name_or_path = Config.reader_model_name_or_path,
                                  use_gpu = Config.use_gpu)


def update(query):
    return f"{query}", f"{query}", f"{query}", f"{query}"

def search(question):
  p1_result = query.search_by_query(query = question, retriever_top_k = 5, reader_top_k = 3, es_index = "petro")
  p2_result = query.search_by_query(query = question, retriever_top_k = 5, reader_top_k = 3, es_index = "rodolfo")

  return [p1_result[0].answer, 
          p1_result[0].context,
          p2_result[0].answer,
          p2_result[0].context]

demo = gr.Blocks()

with demo:
    gr.Markdown(
    """
    # Ask2Democracy
    Preguntale a los candidatos
    """)

    inp = gr.Textbox(placeholder="Haz tu pregunta aquí")
    search_button = gr.Button("Buscar")
    with gr.Row():
      response = gr.Label(value="Petro")
      context = gr.Label(value="El viejo")
    with gr.Row():
      with gr.Column():      
#        resp_1 = gr.Markdown("<b>Respuesta</b>")
#        context_1 = gr.Markdown("<b>Contexto</b>")
        resp_1 = gr.Textbox(lines=1, label="respuesta")
        context_1 = gr.Textbox(lines=5, label="contexto")
      with gr.Column():      
        resp_2 = gr.Textbox(lines=1, label="respuesta")
        context_2 = gr.Textbox(lines=5, label="contexto")
    
      search_button.click(search, inputs = inp, outputs=[resp_1, context_1, resp_2, context_2])
demo.launch(debug = True)