QwenPie / app.py
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Update app.py
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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()