app.py

import os
import subprocess

# Instala os pacotes necessários
subprocess.run(["pip", "install", "--upgrade", "pip"])
subprocess.run(["pip", "install", "--upgrade", "torch", "transformers", "accelerate"])
subprocess.run(["pip", "install", "git+https://github.com/TimDettmers/bitsandbytes.git"])

import accelerate
import bitsandbytes
import gradio as gr
from transformers import LlamaForCausalLM, LlamaTokenizer

# Define a variável de ambiente para desabilitar CUDA
os.environ["TRANSFORMERS_NO_CUDA"] = "1"

# Carrega o modelo e o tokenizador
model = LlamaForCausalLM.from_pretrained("Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_4k-q4_k_m-v2")
tokenizer = LlamaTokenizer.from_pretrained("Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_4k-q4_k_m-v2")

def predict(input_text):
    # Codifica o texto de entrada e gera a saída
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    output = model.generate(input_ids, max_length=4096, do_sample=True, top_k=50, top_p=0.50, num_return_sequences=1)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Cria a interface Gradio
iface = gr.Interface(fn=predict, inputs="text", outputs="text")
iface.launch()