app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

import torch

# Check if a GPU is available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained("truongghieu/deci-finetuned", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("truongghieu/deci-finetuned", trust_remote_code=True,low_cpu_mem_usage=True)

# Move the model to the GPU if available
model.to(device)

generation_config = GenerationConfig(
    penalty_alpha=0.6,
    do_sample=True,
    top_k=5,
    temperature=0.5,
    repetition_penalty=1.2,
    max_new_tokens=200,
    pad_token_id=tokenizer.eos_token_id
)

# Define a function that takes a text input and generates a text output
def generate_text(text):
    input_text = text
    input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)  # Move input to the GPU
    output_ids = model.generate(input_ids, generation_config=generation_config)
    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return output_text

iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
iface.launch()