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# coding=utf8 | |
from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, 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"] = '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' | |
md = """This is some code: | |
hello | |
```py | |
def fn(x, y, z): | |
print(x, y, z) | |
""" | |
def transcribe(audio): | |
text = p(audio)["text"] | |
return text | |
def construct_index(directory_path): | |
max_input_size = 10000 | |
num_outputs = 10000 | |
max_chunk_overlap = 20000 | |
chunk_size_limit = 600000 | |
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) | |
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.0, model_name="text-davinci-003", max_tokens=num_outputs)) | |
documents = SimpleDirectoryReader(directory_path).load_data() | |
index = GPTSimpleVectorIndex.from_documents(documents) | |
index.save_to_disk('index.json') | |
return index | |
def chatbot(input_text): | |
index = GPTSimpleVectorIndex.load_from_disk('index.json') | |
response = index.query(input_text) | |
return str(response.response) | |
with gr.Blocks() as demo: | |
gpt = gr.Chatbot(label="Zoh", elem_id="chatbot").style(height=800) | |
msg = gr.Textbox( 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) | |
vetor = [] | |
realPath = str(os.path.dirname(os.path.realpath(__file__))) | |
if str(message).upper()=="OLA" or str(message).upper()=="OLÁ" or str(message).upper()=="OI": | |
vetor = vetor + [((realPath + "\\images\\hippo-apresentacao.mp4",), "")] | |
elif str(message).upper() == "VINHO CASA DEL RONCO PINOT GRIGIO" : | |
vetor = vetor + [((realPath + "\\images\\casa-del-ronco-branco.png",), "")] | |
elif str(message).upper() == "SURVIVOR CHENIN BLANC" : | |
vetor = vetor + [((realPath + "\\images\\survivor-branco.png",), "")] | |
vetor = vetor + [((realPath + "\\images\\survivor.mp4",), "")] | |
elif str(message).upper() == "VINHO PORTO NOVA VERDE" : | |
vetor = vetor + [((realPath + "\\images\\porta-nova-branco.jpg",), "")] | |
vetor = vetor + [((realPath + "\\images\\porta-nova-verde.mp4",), "")] | |
elif str(message).upper() == "VINHO QUINTA DO PINTO ARINTO BRANCO" : | |
vetor = vetor + [((realPath + "\\images\\quinta-pinto-arinto-branco.png",), "")] | |
elif str(message).upper() == "VINHO 1492 CHARDONNAY" : | |
vetor = vetor + [((realPath + "\\images\\chardonay-branco.jpg",), "")] | |
elif str(message).upper() == "ME SUGIRA UM VINHO TINTO BOM COM QUEIJO" : | |
vetor = vetor + [((realPath + "\\images\\TNT-CABERNET.png",), "")] | |
vetor = vetor + [((realPath + "\\images\\vinho-queijo.mp4",), "")] | |
elif str(message).upper() == "VINHO BOM COM CHOCOLATE" : | |
vetor = vetor + [((realPath + "\\images\\TNT-CABERNET.png",), "")] | |
elif str(message).upper() == "VINHO BOM COM PEIXE" : | |
vetor = vetor + [((realPath + "\\images\\luson-branco.png",), "")] | |
vetor = vetor + [((realPath + "\\images\\vinho-peixe.mp4",), "")] | |
elif str(message).upper() == "VINHAS DO LASSO COLHEITA SELECIONADA" : | |
vetor = vetor + [((realPath + "\\images\\lasso-colheita-rose.png",), "")] | |
elif str(message).upper() == "DOM CAMPOS MOSCATEL" : | |
vetor = vetor + [((realPath + "\\images\\dom-campos-rose.png",), "")] | |
elif str(message).upper() == "BECAS ROSE MEIO SECO" : | |
vetor = vetor + [((realPath + "\\images\\becas-rose.png",), "")] | |
elif str(message).upper() == "PORTA DA RAVESSA" : | |
vetor = vetor + [((realPath + "\\images\\luson-branco.png",), "")] | |
return "", chat_history+vetor | |
clear.click(lambda:None, None, gpt, queue=False,) | |
msg.submit(respond, [msg, gpt], [msg,gpt]) | |
index = construct_index("docs") | |
demo.launch() | |