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import torch
import gradio as gr
import json
# Use a pipeline as a high-level helper
from transformers import pipeline
model_path = ("../Models/models--facebook--nllb-200-distilled-600M/snapshots"
"/f8d333a098d19b4fd9a8b18f94170487ad3f821d")
text_translator = pipeline("translation", model="facebook/nllb-200-distilled-600M",
torch_dtype=torch.bfloat16)
# Load the JSON data from the file
with open('language.json', 'r') as file:
language_data = json.load(file)
# Get all available languages (excluding duplicates with different scripts)
available_languages = []
seen_languages = set()
for entry in language_data:
base_language = entry['Language'].split('(')[0].strip()
if base_language not in seen_languages:
available_languages.append(base_language)
seen_languages.add(base_language)
# Sort languages alphabetically
available_languages.sort()
def get_FLORES_code_from_language(language):
# First try exact match
for entry in language_data:
if entry['Language'].lower() == language.lower():
return entry['FLORES-200 code']
# If no exact match, try matching the base language name
for entry in language_data:
if entry['Language'].lower().startswith(language.lower()):
return entry['FLORES-200 code']
return None
def translate_text(text, destination_language):
dest_code = get_FLORES_code_from_language(destination_language)
if dest_code is None:
return f"Error: Could not find FLORES code for language {destination_language}"
translation = text_translator(text,
src_lang="eng_Latn",
tgt_lang=dest_code)
return translation[0]["translation_text"]
gr.close_all()
demo = gr.Interface(
fn=translate_text,
inputs=[
gr.Textbox(label="Input text to translate", lines=6),
gr.Dropdown(choices=available_languages, label="Select Destination Language")
],
outputs=[gr.Textbox(label="Translated text", lines=4)],
title="@GenAILearniverse Project 4: Multi Language Translator",
description="This application translates English text to multiple languages. Select your desired target language from the dropdown menu."
)
if __name__ == "__main__":
demo.launch()
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