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667228d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import torch
import gradio as gr
from transformers import pipeline
# Use a pipeline as a high-level helper
from transformers import pipeline
# model_path = ("../Models/models--Helsinki-NLP--opus-mt-en-de/snapshots"
# "/6183067f769a302e3861815543b9f312c71b0ca4")
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-de")
# text_translator = pipeline("translation", model=model_path, tokenizer=model_path)
def translate_text(text, destination_language):
# German uses your preloaded local snapshot
if destination_language == "German":
out = text_translator(text)
return out[0]["translation_text"]
# For other targets we load the correct Marian model on demand (EN -> target)
if destination_language == "French":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
elif destination_language == "Hindi":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
elif destination_language == "Romanian":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ro")
elif destination_language == "Spanish":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
elif destination_language == "Italian":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-it")
elif destination_language == "Portuguese":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-pt")
elif destination_language == "Russian":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ru")
elif destination_language == "Japanese":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ja")
elif destination_language == "Korean":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ko")
elif destination_language == "Chinese":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-zh")
elif destination_language == "Arabic":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar")
elif destination_language == "Turkish":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-tr")
elif destination_language == "Dutch":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-nl")
elif destination_language == "Polish":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-pl")
elif destination_language == "Ukrainian":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-uk")
elif destination_language == "Czech":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-cs")
elif destination_language == "Swedish":
p = pipeline("translation", model="Helsinki-NLP/opus-mt-en-sv")
else:
return "Unsupported language. Please choose a listed destination."
out = p(text) # Marian pipeline returns a list of dicts
return out[0]["translation_text"]
gr.close_all()
demo = gr.Interface(
fn=translate_text,
inputs=[
gr.Textbox(label="Input text to translate", lines=6),
gr.Dropdown(
[
"German", "French", "Hindi", "Romanian", "Spanish", "Italian",
"Portuguese", "Russian", "Japanese", "Korean", "Chinese",
"Arabic", "Turkish", "Dutch", "Polish", "Ukrainian", "Czech", "Swedish"
],
label="Select Destination Language"
)
],
outputs=[gr.Textbox(label="Translated text", lines=4)],
title="@SahibhimGenAI Project 4: Multi language translator",
description="Translate English text to your selected language (loads the appropriate MarianMT model per language)."
)
demo.launch()
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