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import gradio as gr | |
import easyocr | |
import transformers | |
import torch | |
import CUDA | |
CUDA=None | |
reader=easyocr.Reader(['en']) | |
# this needs to run only once to load the model into memory | |
result=reader.readtext('https://huggingface.co/spaces/KAPtechies/Translation/blob/main/WhatsApp%20Image%202023-09-23%20at%208.03.28%20AM.jpeg',detail=0) | |
news=" ".join(result) | |
from transformers import AutoTokenizer | |
tokenizer=AutoTokenizer.from_pretrained("facebook/mbart-large-50-one-to-many-mmt",use_fast=False) | |
from transformers import MBartForConditionalGeneration | |
# download and save model | |
model=MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt") | |
input_text=[news] | |
# convert sentences to tensors | |
model_inputs=tokenizer(input_text,return_tensors="pt",padding=True,truncation=True) | |
# translate from English to Hindi | |
generated_tokens=model.generate( | |
**model_inputs, | |
forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"] | |
) | |
translation=tokenizer.batch_decode(generated_tokens,skip_special_tokens=True) | |
translation | |
from transformers import AutoTokenizer | |
tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-one-to-many-mmt", use_fast=False) | |
from transformers import MBartForConditionalGeneration | |
# download and save model | |
model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt") | |
def translator(img): | |
reader = easyocr.Reader(['en']) | |
result = reader.readtext(img,detail = 0) | |
news= " ".join(result) | |
input_text = [news] | |
# convert sentences to tensors | |
model_inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True) | |
# translate from English to Hindi | |
generated_tokens = model.generate( | |
**model_inputs, | |
forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"] | |
) | |
translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) | |
return translation | |
demo = gr.Interface(fn=translator, inputs=gr.Image(), outputs="text") | |
demo.launch(inline=False) |