Spaces:
Sleeping
Sleeping
File size: 5,011 Bytes
4b310eb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
# app.py
import gradio as gr
from transformers import pipeline
import torch
import warnings
warnings.filterwarnings('ignore')
# Configuração de dispositivo
device = "cuda" if torch.cuda.is_available() else "cpu"
try:
# Modelos para diferentes tarefas
models = {
'transcription': pipeline("automatic-speech-recognition", model="openai/whisper-small", device=device),
'translation_en_pt': pipeline("translation", model="Helsinki-NLP/opus-mt-en-pt", device=device),
'translation_pt_en': pipeline("translation", model="Helsinki-NLP/opus-mt-pt-en", device=device),
'summarization': pipeline("summarization", model="facebook/bart-large-cnn", device=device),
'sentiment': pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment", device=device),
'question_answering': pipeline("question-answering", model="deepset/roberta-base-squad2", device=device),
'chat': pipeline("text-generation", model="facebook/opt-1.3b", device=device)
}
except Exception as e:
print(f"Erro ao carregar modelos: {str(e)}")
def safe_process(func):
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
return f"Erro ao processar: {str(e)}"
return wrapper
@safe_process
def transcribe(audio):
if not audio:
return "Por favor, forneça um arquivo de áudio."
return models['transcription'](audio)["text"]
@safe_process
def translate(text, direction):
if not text:
return "Por favor, insira um texto para tradução."
model = models[f'translation_{direction}']
return model(text)[0]['translation_text']
@safe_process
def summarize(text):
if not text:
return "Por favor, insira um texto para resumir."
return models['summarization'](text, max_length=130, min_length=30)[0]['summary_text']
@safe_process
def analyze_sentiment(text):
if not text:
return "Por favor, insira um texto para análise."
return models['sentiment'](text)[0]['label']
@safe_process
def answer_question(question, context):
if not question or not context:
return "Por favor, forneça tanto a pergunta quanto o contexto."
return models['question_answering'](question=question, context=context)['answer']
@safe_process
def chat_response(message, history):
if not message:
return "Por favor, insira uma mensagem."
response = models['chat'](message, max_length=100, do_sample=True, temperature=0.7)
return response[0]['generated_text']
with gr.Blocks(theme=gr.themes.Soft()) as demo:
with gr.Tab("Início"):
gr.HTML(open("index.html").read())
with gr.Tab("Transcrição de Áudio"):
audio_input = gr.Audio(type="filepath", label="Upload de Áudio")
transcribe_button = gr.Button("Transcrever")
transcription_output = gr.Textbox(label="Transcrição", lines=3)
transcribe_button.click(transcribe, inputs=audio_input, outputs=transcription_output)
with gr.Tab("Tradução"):
with gr.Row():
translation_direction = gr.Radio(
["en_pt", "pt_en"],
label="Direção da Tradução",
value="en_pt"
)
text_to_translate = gr.Textbox(label="Texto para Traduzir", lines=3)
translate_button = gr.Button("Traduzir")
translation_output = gr.Textbox(label="Tradução", lines=3)
translate_button.click(
translate,
inputs=[text_to_translate, translation_direction],
outputs=translation_output
)
with gr.Tab("Resumo"):
text_to_summarize = gr.Textbox(label="Texto para Resumir", lines=5)
summarize_button = gr.Button("Resumir")
summary_output = gr.Textbox(label="Resumo", lines=3)
summarize_button.click(summarize, inputs=text_to_summarize, outputs=summary_output)
with gr.Tab("Análise de Sentimento"):
sentiment_text = gr.Textbox(label="Texto para Análise", lines=3)
sentiment_button = gr.Button("Analisar")
sentiment_output = gr.Textbox(label="Sentimento")
sentiment_button.click(analyze_sentiment, inputs=sentiment_text, outputs=sentiment_output)
with gr.Tab("Perguntas e Respostas"):
question_input = gr.Textbox(label="Pergunta")
context_input = gr.Textbox(label="Contexto", lines=5)
qa_button = gr.Button("Responder")
qa_output = gr.Textbox(label="Resposta", lines=2)
qa_button.click(
answer_question,
inputs=[question_input, context_input],
outputs=qa_output
)
with gr.Tab("Chat"):
chatbot = gr.Chatbot()
msg = gr.Textbox(label="Mensagem")
clear = gr.Button("Limpar")
msg.submit(chat_response, [msg, chatbot], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
if __name__ == "__main__":
demo.launch(share=True) |