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from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
pipeline
)
from textblob import TextBlob as tb
import gradio as gr
tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
pipe = pipeline(model="aware-ai/whisper-base-german")
def translate(text):
blob = tb(text)
translation = str(blob.translate(from_lang='de',to='en'))
return translation
def translate_to_de(text):
blob = tb(text)
translation = str(blob.translate(from_lang='en',to='de'))
return translation
def transcribe(audio):
text = pipe(audio)["text"]
return text
def generate(input, knowledge):
if knowledge == '':
pass
else:
knowledge = translate(knowledge)
input = translate(input)
top_p = 1
min_length = 8
max_length = 64
instruction = 'given a dialog context and related knowledge, you need to answer the question based on the knowledge.'
if knowledge != '':
knowledge = '[KNOWLEDGE] ' + knowledge
dialog = ' EOS '.join([input])
query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
outputs = model.generate(input_ids, min_length=int(
min_length), max_length=int(max_length), top_p=top_p, do_sample=True)
output = tokenizer.decode(outputs[0], skip_special_tokens=True)
output = translate_to_de(output)
return output
with gr.Blocks() as app:
conocimiento = gr.Textbox(label='Conocimiento',lines=7,max_lines=7)
voice = gr.Audio(source='microphone',type='filepath')
send_button = gr.Button(value='Transcribir')
transc = gr.Textbox(label='Transcripción',value='',)
button2 = gr.Button(value='Respuesta de la IA')
respuesta = gr.Textbox(label='Respuesta',interactive=False,value='')
send_button.click(fn=transcribe,inputs=voice,outputs=transc)
button2.click(fn=generate,inputs=[transc,conocimiento],outputs=respuesta)
app.launch() |