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from huggingface_hub import hf_hub_download
from llama_cpp import Llama
import gradio as gr
# Download the model from Hugging Face
model_name = "johnpaulbin/articulate-V1-Q8_0-GGUF"
model_file = "articulate-V1-q8_0.gguf" # Verify the exact file name in the repository
model_path = hf_hub_download(repo_id=model_name, filename=model_file)
# Initialize the Llama model
llm = Llama(
model_path=model_path,
n_ctx=1028, # Context length
n_threads=2, # Number of CPU threads
n_gpu_layers=0 # Run on CPU only
)
# Define the translation function
def translate(direction, text):
# Determine source and target languages based on direction
if direction == "English to Spanish":
source_lang = "ENGLISH"
target_lang = "SPANISH"
elif direction == "Spanish to English":
source_lang = "SPANISH"
target_lang = "ENGLISH"
else:
return "Invalid direction"
# Construct the prompt for raw completion
prompt = f"[{source_lang}]{text}[{target_lang}]"
# Generate completion with deterministic settings (greedy decoding)
response = llm.create_completion(
prompt,
max_tokens=200, # Limit output length
temperature=0, # Greedy decoding
top_k=1 # Select the most probable token
)
# Extract and return the generated text
return response['choices'][0]['text'].strip()
# Define the Gradio interface
direction_options = ["English to Spanish", "Spanish to English"]
iface = gr.Interface(
fn=translate,
inputs=[
gr.Dropdown(choices=direction_options, label="Translation Direction"),
gr.Textbox(lines=5, label="Input Text")
],
outputs=gr.Textbox(lines=5, label="Translation"),
title="Translation App",
description="Translate text between English and Spanish using the Articulate V1 model."
)
# Launch the app
iface.launch() |