app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
import spaces

def load_model(model_name):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        device_map=device,
        torch_dtype="auto",
        trust_remote_code=True,
    )
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    generator = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        return_full_text=False,
        max_new_tokens=500,
        do_sample=False
    )
    return generator

@spaces.GPU
def generate_text(prompt, model_name):
    generator = load_model(model_name)
    messages = [{"role": "user", "content": prompt}]
    output = generator(messages)
    return output[0]["generated_text"]

# Create Gradio interface
demo = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(lines=2, placeholder="Coloque seu prompt aqui......"),
        gr.Dropdown(
            choices=["Qwen/Qwen2.5-1.5B-Instruct","microsoft/Phi-3-mini-4k-instruct", "ALLaM-AI/ALLaM-7B-Instruct-preview"],
            label="Choose Model",
            value="ALLaM-AI/ALLaM-7B-Instruct-preview"
        )
    ],
    outputs=gr.Textbox(label="Generated Text"),
    title="Gerador de textos - Luciano",
    description="Entre com o texto que deseja gerar....",
 
)

demo.launch()