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Update app.py
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import streamlit as st
from PIL import Image
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from huggingface_hub.hf_api import HfFolder
HfFolder.save_token('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')
access_token = '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'
#
image_path = r"image.JPG"
image = Image.open(image_path)
st.set_page_config(page_title="English To Hindi Language Translator App", layout="centered")
st.image(image, caption='English To Hindi Language Translator')
# page header
st.title(f"English Text to Hindi Translation App")
with st.form("Prediction_form"):
text = st.text_input("Enter text here")
#st.title(text)
#
submit = st.form_submit_button("Translate Text to Hindi")
#
if submit:
#tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M",use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained(".",use_auth_token=True)
#model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M",use_auth_token=True)
model = AutoModelForSeq2SeqLM.from_pretrained(".",use_auth_token=True)
inputs = tokenizer(text, return_tensors="pt")
translated_tokens = model.generate(**inputs,
forced_bos_token_id=tokenizer.lang_code_to_id["hin_Deva"],
max_length=100)
result = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
print(result)
# output header
st.header("Translated Text")
# output results
st.success(f"Translated Text : {result}")