# coding=utf8
from llama_index import load_index_from_storage, SimpleDirectoryReader, readers, GPTVectorStoreIndex,StorageContext, ServiceContext, LLMPredictor, PromptHelper
from langchain import OpenAI
import gradio as gr
import random
import time
import sys
import os
from transformers import pipeline
p = pipeline("automatic-speech-recognition")

os.environ["OPENAI_API_KEY"]

css = """

#component-2 {position: absolute; bottom: 0;    width: 100%;
}
.app.svelte-ac4rv4>.main.svelte-ac4rv4 {
    display: flex;
    flex-grow: 1;
    flex-direction: column;
    background-image: url(https://i.ibb.co/xj8R4r3/background-vertical.png);
}
div.svelte-1frtwj3 {
    display: inline-flex;
    align-items: center;}
    
div.float.svelte-1frtwj3 {
    position: absolute;
    opacity: 0;
    top: var(--block-label-margin);
    left: var(--block-label-margin);}
    
.wrap.svelte-6roggh.svelte-6roggh {
 adding: var(--block-padding);
    height: 100%;
    max-height: 800px;
    overflow-y: auto;
    }
    
.bot.svelte-6roggh.svelte-6roggh, .pending.svelte-6roggh.svelte-6roggh {
    border-color: var(--border-color-accent);
    background-color: var(--color-accent-soft);
    color: white;
    font-family: initial;
    font-style: italic;
    font: message-box;
    font-weight: bold;
}
div.svelte-1frtwj3 {
    display: inline-flex;
    align-items: center;
    z-index: var(--layer-2);
    box-shadow: var(--block-shadow);
    border: var(--block-label-border-width) solid #ffffff;
    border-top: none;
    border-left: none;
    border-radius: var(--block-label-radius);
    background: #eff6ff;
    padding: var(--block-label-padding);
    pointer-events: none;
    color: var(--block-label-text-color);
    font-weight: var(--block-label-text-weight);
    width: 100%;
    line-height: var(--line-sm);
    }
div.svelte-awbtu4 {
    display: flex;
    flex-direction: inherit;
    flex-wrap: wrap;
    gap: var(--form-gap-width);
    box-shadow: var(--block-shadow);
    border: var(--block-border-width) solid #5f0000;
    border-radius: var(--radius-lg);
    background: #ffffff;
    overflow: hidden;
    position: fixed;
    bottom: 0;
    margin-left: -16px;
}

img.svelte-ms5bsk {
    width: var(--size-full);
    height: 90px;
    object-fit: contain;
}
.app.svelte-ac4rv4.svelte-ac4rv4 {
    max-width: none;
    background-color: #ffffff;
}
.app.svelte-ac4rv4.svelte-ac4rv4{max-width:none}
.wrap.svelte-1o68geq.svelte-1o68geq {max-height: none}
.block.svelte-mppz8v {
    position: relative;
    margin: 0;
    box-shadow: var(--block-shadow);
    border-width: var(--block-border-width);
    border-color: white;
    border-radius: var(--block-radius);
    background: white;
    width: 100%;
    line-height: var(--line-sm);
}
div.bot.svelte-6roggh.svelte-6roggh {
    background: #D9A13D;
}
div.bot.svelte-17nzccn.svelte-17nzccn {
    background: #D9A13D;
}
div.user.svelte-6roggh.svelte-6roggh {
        background: #5F0000;
color: white;    
        
    
}
div.user.svelte-17nzccn.svelte-17nzccn {
    background: #5F0000;
}    
"""

def transcribe(audio):
    text = p(audio)["text"]
    return text
def construct_index(directory_path):    
    num_outputs = 2000
    
    prompt_helper = PromptHelper(context_window=3900, num_output=256, max_chunk_overlap=20, chunk_size_limit=1024)

    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.0, model_name="gpt-3.5-turbo-16k", max_tokens=num_outputs))
    

    documents = SimpleDirectoryReader(directory_path).load_data()

    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
    index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context, prompt_helper=prompt_helper)

    index.storage_context.persist(persist_dir='index.json')

    return index


def chatbot(input_text):    
    num_outputs = 4097    

    prompt_helper = PromptHelper(context_window=3900, num_output=256, max_chunk_overlap=20, chunk_size_limit=1024)
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.0, model_name="gpt-3.5-turbo-16k", max_tokens=num_outputs))
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
    storage_context = StorageContext.from_defaults(persist_dir='index.json')
    
    # load index
    index = load_index_from_storage(storage_context)

    query_engine = index.as_query_engine(service_context=service_context, verbose=True, response_mode="compact")    
    response = query_engine.query(input_text)
    return str(response.response)

with gr.Blocks(css=css) as demo:
    realPath = str(os.path.dirname(os.path.realpath(__file__)))
    img1 = gr.Image("images/1024x150_cabeçalho.hippo.png", elem_classes=".img.svelte-ms5bsk", elem_id="img.svelte-ms5bsk").style(container=False)
    gpt = gr.Chatbot(label = ".", elem_classes=".wrap.svelte-1o68geq.svelte-1o68geq", elem_id="chatbot").style(container=True)
    msg = gr.Textbox(elem_id="div.svelte-awbtu4",elem_classes="textBoxBot", show_label=False,
                placeholder="Bem vindo ao Hippo Supermercados, em que posso ajuda-lo?",
            ).style(container=False)
    #clear = gr.Button("Limpar Conversa")
   # gr.Audio(source="microphone", type="filepath",label="ESTÁ COM DIFICULDADES EM ESCREVER? CLIQUE E ME DIGA O QUE DESEJA")
    def respond(message, chat_history):
        chat_history.append((message, chatbot(message)))
        time.sleep(1)

        return "", chat_history

   # clear.click(lambda:None, None, gpt, queue=False,)
    msg.submit(respond, [msg, gpt], [msg,gpt])

index = construct_index("docs")
demo.launch()