File size: 2,054 Bytes
7db96fe aed8eed 7db96fe aed8eed 7db96fe aed8eed 7db96fe aed8eed 7db96fe |
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 |
import gradio as gr
import json
from transformers import pipeline
# Initialize the pipeline with the model
try:
text_translator = pipeline("translation", model="facebook/nllb-200-distilled-600M")
except Exception as e:
raise ValueError(f"Error initializing the model: {e}")
# Load the JSON data for language codes
try:
with open('language.json', 'r') as file:
language_data = json.load(file)
except FileNotFoundError:
raise FileNotFoundError("The language.json file was not found at the specified path.")
except json.JSONDecodeError:
raise ValueError("Error decoding the JSON file. Ensure it is formatted correctly.")
# Create a function to get the FLORES-200 code for a given language
def get_FLORES_code_from_language(language):
for entry in language_data:
if entry['Language'].lower() == language.lower():
return entry['FLORES-200 code']
return None
# Translation function
def translate_text(text, destination_language):
if not text.strip():
return "Input text cannot be empty."
dest_code = get_FLORES_code_from_language(destination_language)
if not dest_code:
return f"Language '{destination_language}' is not supported."
try:
# Perform translation
translation = text_translator(text, src_lang="eng_Latn", tgt_lang=dest_code)
return translation[0]["translation_text"]
except Exception as e:
return f"Error during translation: {e}"
# Extract available languages from JSON
available_languages = [entry['Language'] for entry in language_data]
# Create the Gradio interface
demo = gr.Interface(
fn=translate_text,
inputs=[
gr.Textbox(label="Input text to translate", lines=6),
gr.Dropdown(available_languages, label="Select language to translate to")
],
outputs=gr.Textbox(label="Translated text", lines=6),
title="Project 03: Multi Language Translator",
description="Translate English text into multiple languages using the NLLB-200 model."
)
# Launch the interface
demo.launch()
|