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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("jsbeaudry/makandal-pre-trained")
model = AutoModelForCausalLM.from_pretrained("jsbeaudry/makandal-pre-trained")
# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# Generation function
def generate_text(prompt):
inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(device)
output = model.generate(
**inputs,
max_new_tokens=30,
do_sample=True,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
temperature=0.9,
top_k=40,
top_p=0.85,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id
)
return tokenizer.decode(output[0], skip_special_tokens=True)
# Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=gr.Textbox(lines=2, placeholder="Ekri yon sijè oswa yon fraz..."),
outputs="text",
title="Makandal Text Generator",
description="Ekri yon fraz oswa mo kle pou jenere tèks ak modèl Makandal la. Modèl sa fèt espesyalman pou kontèks Ayiti."
)
if __name__ == "__main__":
iface.launch()
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