import gradio as gr
import os
import time
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import EmbeddingRetriever
import pandas as pd
from deep_translator import GoogleTranslator


def load_qa_model():
  document_store = InMemoryDocumentStore()
  retriever = EmbeddingRetriever(
    document_store=document_store,
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
    use_gpu=False,
    scale_score=False,
  )
  # Get dataframe with columns "question", "answer" and some custom metadata
  df = pd.read_csv('social-faq.csv', on_bad_lines='skip', delimiter=';')
  # Minimal cleaning
  df.fillna(value="", inplace=True)
  df["question"] = df["question"].apply(lambda x: x.strip())

  questions = list(df["question"].values)
  df["embedding"] = retriever.embed_queries(queries=questions).tolist()
  df = df.rename(columns={"question": "content"})

  # Convert Dataframe to list of dicts and index them in our DocumentStore
  docs_to_index = df.to_dict(orient="records")
  document_store.write_documents(docs_to_index)

  return retriever

def add_text(history, text):
    history = history + [(text, None)]
    return history, gr.Textbox(value="", interactive=False)


def add_file(history, file):
    history = history + [((file.name,), None)]
    return history


def chatbot_response(chat_history, language):
    global retriever
    global last_answer

    
    chat_history[-1][1] = ""


    if language == 'pt-br':
        response = get_answers(retriever, GoogleTranslator(source='pt', target='en').translate(chat_history[0][0]))
        response = GoogleTranslator(source='en', target='pt').translate(response)
    else:
        response = get_answers(retriever, chat_history[0][0])
       
    last_answer = response
    
    for character in response:
        chat_history[-1][1] += character
        time.sleep(0.01)
        yield chat_history



def get_answers(retriever, query):
  from haystack.pipelines import FAQPipeline

  pipe = FAQPipeline(retriever=retriever)

  from haystack.utils import print_answers

  # Run any question and change top_k to see more or less answers
  prediction = pipe.run(query=query, params={"Retriever": {"top_k": 1}})

  answers = prediction['answers']

  if answers:
    return answers[0].answer
  else:
    return "I don't have an answer to that question"




retriever = load_qa_model()


with gr.Blocks() as demo:
    with gr.Accordion("Settings", open=False):
      language = gr.Radio(["en-us", "pt-br"], label="Language", info="Choose the language to display the classification result and audio", value='en-us', interactive=True)


    with gr.Tab("Help"):
      chatbot = gr.Chatbot(
        [],
        elem_id="chatbot",
        bubble_full_width=False,
        # avatar_images=(None, "content/avatar-socialear.png"),
      )

      with gr.Row():
        txt = gr.Textbox(
            scale=4,
            show_label=False,
            placeholder="Enter text and press enter",
            container=False,
        )
        inputRecord = gr.Audio(label="Record a question", source="microphone", type="filepath")
        with gr.Column():
          btn = gr.Button(value="Listen the answer")
          audioOutput = gr.Audio(interactive=False)

      txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
          chatbot_response, [chatbot, language], chatbot
      )
      txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
     

demo.queue()
demo.launch()