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import gradio as gr | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
# Carica il modello e il tokenizer | |
model_name = "Qwen/Qwen3-235B-A22B" | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") | |
def generate_text(prompt, max_length=200, temperature=0.7): | |
# Tokenizza l'input | |
inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
# Genera la risposta | |
outputs = model.generate( | |
inputs["input_ids"], | |
max_length=max_length, | |
temperature=temperature, | |
do_sample=True, | |
top_p=0.9, | |
) | |
# Decodifica la risposta | |
response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return response | |
# Crea l'interfaccia Gradio | |
with gr.Blocks() as demo: | |
gr.Markdown("# Qwen3-235B Demo") | |
with gr.Row(): | |
with gr.Column(): | |
prompt_input = gr.Textbox(label="Il tuo prompt", lines=4) | |
temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperatura") | |
max_length = gr.Slider(minimum=50, maximum=500, value=200, step=10, label="Lunghezza massima") | |
submit_btn = gr.Button("Genera") | |
with gr.Column(): | |
output = gr.Textbox(label="Risposta generata", lines=8) | |
submit_btn.click( | |
generate_text, | |
inputs=[prompt_input, max_length, temperature], | |
outputs=[output] | |
) | |
# Avvia l'applicazione | |
if __name__ == "__main__": | |
demo.launch() |