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from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper |
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from langchain import OpenAI |
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import gradio as gr |
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import random |
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import time |
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import sys |
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import os |
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from transformers import pipeline |
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p = pipeline("automatic-speech-recognition") |
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os.environ["OPENAI_API_KEY"] = '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' |
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css = """ |
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#component-2 {position: absolute; bottom: 0; width: 100%; |
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} |
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div.float.svelte-1frtwj3 { |
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position: absolute; |
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opacity: 0; |
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top: var(--block-label-margin); |
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left: var(--block-label-margin);} |
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.wrap.svelte-6roggh.svelte-6roggh { |
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padding: var(--block-padding); |
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height: 100%; |
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max-height: 100%; |
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overflow-y: auto; |
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} |
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.bot.svelte-6roggh.svelte-6roggh, .pending.svelte-6roggh.svelte-6roggh { |
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border-color: #759ce9; |
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background: #ffffff; |
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} |
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div.svelte-1frtwj3 { |
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display: inline-flex; |
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align-items: center; |
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z-index: var(--layer-2); |
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box-shadow: var(--block-shadow); |
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border: var(--block-label-border-width) solid #ffffff; |
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border-top: none; |
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border-left: none; |
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border-radius: var(--block-label-radius); |
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background: #eff6ff; |
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padding: var(--block-label-padding); |
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pointer-events: none; |
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color: var(--block-label-text-color); |
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font-weight: var(--block-label-text-weight); |
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width: 100%; |
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line-height: var(--line-sm); |
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} |
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div.svelte-awbtu4 { |
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display: flex; |
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flex-direction: inherit; |
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flex-wrap: wrap; |
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gap: var(--form-gap-width); |
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box-shadow: var(--block-shadow); |
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border: var(--block-border-width) solid #5f0000; |
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border-radius: var(--radius-lg); |
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background: #ffffff; |
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overflow: hidden; |
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position: fixed; |
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bottom: 0; |
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margin-left: -16px; |
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} |
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img.svelte-ms5bsk { |
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width: var(--size-full); |
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height: 90px; |
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object-fit: contain; |
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} |
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.app.svelte-ac4rv4.svelte-ac4rv4 { |
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max-width: none; |
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background-color: #ffffff; |
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} |
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.app.svelte-ac4rv4.svelte-ac4rv4{max-width:none} |
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.wrap.svelte-1o68geq.svelte-1o68geq {max-height: none} |
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.block.svelte-mppz8v { |
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position: relative; |
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margin: 0; |
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box-shadow: var(--block-shadow); |
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border-width: var(--block-border-width); |
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border-color: #dbeafe; |
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border-radius: var(--block-radius); |
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background: #dbeafe; |
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width: 100%; |
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line-height: var(--line-sm); |
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} |
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div.bot.svelte-6roggh.svelte-6roggh { |
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background: #D9A13D; |
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} |
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div.bot.svelte-17nzccn.svelte-17nzccn { |
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background: #D9A13D; |
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} |
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div.user.svelte-6roggh.svelte-6roggh { |
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background: #5F0000; |
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} |
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div.user.svelte-17nzccn.svelte-17nzccn { |
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background: #5F0000; |
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} |
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""" |
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def transcribe(audio): |
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text = p(audio)["text"] |
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return text |
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def construct_index(directory_path): |
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max_input_size = 10000 |
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num_outputs = 10000 |
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max_chunk_overlap = 20000 |
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chunk_size_limit = 600000 |
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prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) |
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llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.0, model_name="text-davinci-003", max_tokens=num_outputs)) |
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documents = SimpleDirectoryReader(directory_path).load_data() |
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index = GPTSimpleVectorIndex.from_documents(documents) |
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index.save_to_disk('index.json') |
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return index |
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def chatbot(input_text): |
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index = GPTSimpleVectorIndex.load_from_disk('index.json') |
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response = index.query(input_text) |
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return str(response.response) |
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with gr.Blocks(css=css) as demo: |
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realPath = str(os.path.dirname(os.path.realpath(__file__))) |
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img1 = gr.Image("images/1024x150_cabeçalho.hippo.png", elem_classes=".img.svelte-ms5bsk", elem_id="img.svelte-ms5bsk").style(container=False) |
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gpt = gr.Chatbot(elem_classes=".wrap.svelte-1o68geq.svelte-1o68geq", elem_id="chatbot").style(container=True) |
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msg = gr.Textbox(elem_id="div.svelte-awbtu4",elem_classes="textBoxBot", show_label=False, |
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placeholder="Bem vindo ao Hippo Supermercados, em que posso ajuda-lo?", |
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).style(container=False) |
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def respond(message, chat_history): |
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chat_history.append((message, chatbot(message))) |
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time.sleep(1) |
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vetor = [] |
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realPath = str(os.path.dirname(os.path.realpath(__file__))) |
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if str(message).upper()=="OLA" or str(message).upper()=="OLÁ" or str(message).upper()=="OI": |
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vetor = vetor + [((realPath + "\\images\\hippo-apresentacao.mp4",), "")] |
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elif str(message).upper() == "VINHO CASA DEL RONCO PINOT GRIGIO" : |
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vetor = vetor + [((realPath + "\\images\\casa-del-ronco-branco.png",), "")] |
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elif str(message).upper() == "SURVIVOR CHENIN BLANC" : |
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vetor = vetor + [((realPath + "\\images\\survivor-branco.png",), "")] |
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vetor = vetor + [((realPath + "\\images\\survivor.mp4",), "")] |
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elif str(message).upper() == "VINHO PORTO NOVA VERDE" : |
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vetor = vetor + [((realPath + "\\images\\porta-nova-branco.jpg",), "")] |
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vetor = vetor + [((realPath + "\\images\\porta-nova-verde.mp4",), "")] |
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elif str(message).upper() == "VINHO QUINTA DO PINTO ARINTO BRANCO" : |
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vetor = vetor + [((realPath + "\\images\\quinta-pinto-arinto-branco.png",), "")] |
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elif str(message).upper() == "VINHO 1492 CHARDONNAY" : |
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vetor = vetor + [((realPath + "\\images\\chardonay-branco.jpg",), "")] |
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elif str(message).upper() == "ME SUGIRA UM VINHO TINTO BOM COM QUEIJO" : |
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vetor = vetor + [((realPath + "\\images\\TNT-CABERNET.png",), "")] |
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vetor = vetor + [((realPath + "\\images\\vinho-queijo.mp4",), "")] |
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elif str(message).upper() == "VINHO BOM COM CHOCOLATE" : |
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vetor = vetor + [((realPath + "\\images\\TNT-CABERNET.png",), "")] |
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elif str(message).upper() == "VINHO BOM COM PEIXE" : |
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vetor = vetor + [((realPath + "\\images\\luson-branco.png",), "")] |
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vetor = vetor + [((realPath + "\\images\\vinho-peixe.mp4",), "")] |
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elif str(message).upper() == "VINHAS DO LASSO COLHEITA SELECIONADA" : |
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vetor = vetor + [((realPath + "\\images\\lasso-colheita-rose.png",), "")] |
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elif str(message).upper() == "DOM CAMPOS MOSCATEL" : |
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vetor = vetor + [((realPath + "\\images\\dom-campos-rose.png",), "")] |
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elif str(message).upper() == "BECAS ROSE MEIO SECO" : |
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vetor = vetor + [((realPath + "\\images\\becas-rose.png",), "")] |
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elif str(message).upper() == "PORTA DA RAVESSA" : |
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vetor = vetor + [((realPath + "\\images\\luson-branco.png",), "")] |
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return "", chat_history+vetor |
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msg.submit(respond, [msg, gpt], [msg,gpt]) |
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index = construct_index("docs") |
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demo.launch() |
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