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

# ุชุญู…ูŠู„ ุงู„ู…ูˆุฏูŠู„
model_name = "Salesforce/codegen-350M-mono"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cpu") 

# ุฏุงู„ุฉ ุงู„ุชูˆู„ูŠุฏ
def generate_code(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=128, num_return_sequences=1)
    code = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return code

# ูˆุงุฌู‡ุฉ Gradio
gr.Interface(
    fn=generate_code,
    inputs=gr.Textbox(lines=5, placeholder="Describe what code you want...", label="Prompt"),
    outputs=gr.Textbox(label="Generated Code"),
    title="Code Generator - Mono Model",
    description="Generate Python code from a text description using CodeGen-350M-Mono model"
).launch()