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import streamlit as st
import torch
from fairseq.models.transformer import TransformerModel
import os
import requests
# Define the URLs of your model and dictionary files
model_url = "https://huggingface.co/SLPG/English_to_Urdu_Unsupervised_MT/resolve/main/checkpoint_8_96000.pt"
dict_en_url = "https://huggingface.co/SLPG/English_to_Urdu_Unsupervised_MT/resolve/main/dict.en.txt"
dict_ur_url = "https://huggingface.co/SLPG/English_to_Urdu_Unsupervised_MT/resolve/main/dict.ur.txt"
# Define the paths to save the downloaded files
model_path = "sent_iwslt-bt-enur_42.pt"
dict_en_path = "dict.en.txt"
dict_ur_path = "dict.ur.txt"
# Define a function to download files
def download_file(url, file_path):
if not os.path.exists(file_path):
with requests.get(url, stream=True) as r:
r.raise_for_status()
with open(file_path, 'wb') as f:
for chunk in r.iter_content(chunk_size=8192):
f.write(chunk)
return file_path
# Download the model and dictionary files
download_file(model_url, model_path)
download_file(dict_en_url, dict_en_path)
download_file(dict_ur_url, dict_ur_path)
# Load the model
en_ur_model = TransformerModel.from_pretrained(
'.',
checkpoint_file=model_path,
data_name_or_path='.'
)
# Streamlit interface
st.title("Translation Model Inference")
input_text = st.text_area("Enter text to translate", "")
if st.button("Translate"):
if input_text:
output_text = en_ur_model.translate(input_text)
st.write(f"Translated Text: {output_text}")
else:
st.write("Please enter text to translate.")
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