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import gradio as gr
from transformers import pipeline
# Load models locally on the Hugging Face Space server
# Note: In a real space, you can also use Inference Endpoints for even more speed
models = {
"Spanish": pipeline("translation_en_to_es", model="Helsinki-NLP/opus-mt-en-es"),
"German": pipeline("translation_en_to_de", model="Helsinki-NLP/opus-mt-en-de"),
"Japanese": pipeline("translation_en_to_ja", model="staka/fugumt-en-ja"),
"Ukranian": pipeline("translation_en_to_uk", model="Helsinki-NLP/opus-mt-en-uk"),
"Russian": pipeline("translation_en_to_ru", model="Helsinki-NLP/opus-mt-en-ru"),
}
def translate(text, language):
if not text:
return "Please enter an English sentence."
# Perform translation using the selected model
result = models[language](text)
return result[0]['translation_text']
# Create the Gradio interface
with gr.Blocks(title="Short Translation Web App") as demo:
gr.Markdown("# Short Translation")
with gr.Row():
with gr.Column(scale=2):
input_text = gr.Textbox(
label="English Sentence",
placeholder="Type your English sentence here...",
lines=2
)
with gr.Row():
translate_btn = gr.Button("Translate", variant="primary")
clear_btn = gr.Button("Clear")
with gr.Column(scale=1):
lang_radio = gr.Radio(
choices=["Spanish", "German", "Japanese", "Ukranian",
"Russian"],
label="Translation Language",
value="Spanish"
)
# Output area styled to resemble the maroon box (using Gradio's color system)
output_text = gr.Textbox(
label="Translation",
interactive=False,
container=True,
elem_id="output-box"
)
# Custom CSS to mimic the maroon look from the Tkinter app
demo.css = """
#output-box textarea {
background-color: #800000 !important;
color: white !important;
font-size: 1.2em !important;
font-weight: bold !important;
text-align: center !important;
}
"""
# Define button actions
translate_btn.click(fn=translate, inputs=[input_text, lang_radio], outputs=output_text)
clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[input_text, output_text])
# Launch the app
if __name__ == "__main__":
demo.launch()
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