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from huggingface_hub import InferenceClient | |
import gradio as gr | |
# Initialize the inference client with the Mixtral model | |
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
def translate_text(text, target_language): | |
# Format the prompt to include the translation instruction | |
prompt = f"Translate the following text to {target_language}:\n{text}" | |
# Call the Mixtral model for translation | |
response = client(text_generation=prompt, parameters={"max_new_tokens": 100}, options={"wait_for_model": True}) | |
# The Mixtral model response includes the translated text in its output | |
translated_text = response[0]['generated_text'] | |
# Clean up the response to extract only the translated part | |
# This step might need adjustment based on the model's output format | |
translated_text = translated_text.replace(prompt, '').strip() | |
return translated_text | |
# Define the languages you want to support in your app | |
languages = [ | |
"French", | |
"Spanish", | |
"German", | |
"Italian", | |
"Portuguese", | |
# Add more languages as needed | |
] | |
# Create the Gradio interface | |
iface = gr.Interface( | |
fn=translate_text, | |
inputs=[ | |
gr.Textbox(label="Text to Translate", placeholder="Enter text here..."), | |
gr.Dropdown(label="Target Language", choices=languages) | |
], | |
outputs=gr.Textbox(label="Translated Text"), | |
title="Simple Translator with Mixtral", | |
description="Translate text to various languages using the Mixtral model from Hugging Face." | |
) | |
# Launch the interface | |
iface.launch() | |