├── requirements.txt
├── ss
    ├── 1.png
    ├── 2.png
    └── 3.png
├── README.md
└── streamlit.py


/requirements.txt:
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1 | nemo_toolkit
2 | streamlit
3 | 
4 | 


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/ss/1.png:
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https://raw.githubusercontent.com/somiljain7/nmt-app/main/ss/1.png


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/ss/2.png:
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https://raw.githubusercontent.com/somiljain7/nmt-app/main/ss/2.png


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/ss/3.png:
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https://raw.githubusercontent.com/somiljain7/nmt-app/main/ss/3.png


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/README.md:
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1 | # nmt-app
2 | 
3 | - **NMT En Hi Transformer12x2**
4 | ![](https://github.com/somiljain7/nmt-app/blob/main/ss/3.png).
5 | - **NMT En Es**
6 | ![](https://github.com/somiljain7/nmt-app/blob/main/ss/1.png)
7 | - **NMT En de**
8 | ![](https://github.com/somiljain7/nmt-app/blob/main/ss/2.png)
9 | 


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/streamlit.py:
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 1 | import streamlit as st
 2 | import os
 3 | from nemo.collections.nlp.models import MTEncDecModel
 4 | MTEncDecModel.list_available_models()
 5 | 
 6 | def main():
 7 | 	""" NLP Based App with Streamlit """
 8 | 
 9 | 	# Title
10 | 	st.title("NeMo webapp")
11 | 	st.subheader("Neural Machine Translation (NMT) model to translate from One language to Another")
12 | 	
13 | 
14 | 
15 | 	if st.checkbox("translate from English to Hindi"):
16 | 		
17 | 
18 | 		message1 = st.text_area("Enter Text","Type Here....")
19 | 		model1 = MTEncDecModel.from_pretrained("nmt_en_hi_transformer12x2")
20 | 		translations1 = model1.translate([message1], source_lang="en", target_lang="hi")
21 | 		st.success(translations1[0])
22 | 
23 | 	elif st.checkbox("translate from English to spanish"):
24 | 		
25 | 
26 | 		message2 = st.text_area("Enter Text","Type Here....")
27 | 		model2 = MTEncDecModel.from_pretrained("nmt_en_es_transformer12x2")
28 | 		translations2 = model2.translate([message2], source_lang="en", target_lang="es")
29 | 		st.success(translations2[0])
30 | 
31 | 	elif st.checkbox("translate from English to german"):
32 | 		
33 | 
34 | 		message3 = st.text_area("Enter Text","Type Here....")
35 | 		model3 = MTEncDecModel.from_pretrained("nmt_en_de_transformer12x2")
36 | 		translations3 = model3.translate([message3], source_lang="en", target_lang="de")
37 | 		st.success(translations3[0])
38 | 
39 | 
40 | 
41 | 
42 | 
43 | 
44 | 	st.sidebar.subheader("About the App")
45 | 	st.sidebar.text("NeMo by NVIDIA (nmt inference tool")
46 | 	st.sidebar.info("Use this tool to get the translation from one language to other ")
47 | 	st.sidebar.subheader("Developed by")
48 | 	st.sidebar.text("Somil Jain")
49 | 
50 | 
51 | 
52 | 
53 | if __name__ == '__main__':
54 | 	main()
55 | 


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