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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): | |
# Correctly format the prompt for the translation task | |
prompt = f"Translate the following text to {target_language}: {text}" | |
# Correctly perform the model call for text generation | |
response = client.text_generation( | |
inputs=prompt, | |
parameters={"max_new_tokens": 100}, | |
options={"wait_for_model": True} | |
) | |
# Extract the translated text from the response | |
translated_text = response[0]['generated_text'] | |
# Depending on the model's response, you may need to clean the output | |
# For example, remove the input part from the response if necessary | |
translated_text = translated_text.split(prompt)[-1].strip() | |
return translated_text | |
languages = [ | |
"French", | |
"Spanish", | |
"German", | |
"Italian", | |
"Portuguese", | |
# Add more languages as needed | |
] | |
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." | |
) | |
iface.launch() |