import gradio as gr from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed from transformers import pipeline import os description = """# <p style="text-align: center; color: white;"> ❄️ <span style='color: #ff75b3;'>IceCoder</span> Generator ❄️ </p> <span style='color: white;'>This is a demo to generate code with <a href="todo" style="color: #ff75b3;">IceCoder</a>, a 1.1B model for code generation in Python, Java & JavaScript.</span>""" token = os.environ["HUB_TOKEN"] device="cuda:0" revision = "dedup-alt-comments" tokenizer = AutoTokenizer.from_pretrained("bigcode/christmas-models", use_auth_token=token) model = AutoModelForCausalLM.from_pretrained("bigcode/christmas-models", revision=revision, trust_remote_code=True, use_auth_token=token) def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42): set_seed(seed) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) generated_text = pipe(gen_prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text'] return generated_text demo = gr.Blocks( css=".gradio-container {background-color: #20233fff; color:white}" ) with demo: with gr.Row(): gr.Markdown(value=description) with gr.Row(): with gr.Column(): code = gr.Textbox(lines=10, label="Input code") max_tokens= gr.Slider( minimum=8, maximum=1000, step=1, label="Number of tokens to generate", ) temperature = gr.Slider( minimum=0.1, maximum=2.5, step=0.1, label="Temperature", ) seed = gr.Slider( minimum=0, maximum=1000, step=1, label="Random seed to use for the generation" ) run = gr.Button() with gr.Column(): output = gr.Textbox(lines=10, label="Generated code") event = run.click(code_generation, [code, max_tokens, temperature, seed], output) gr.HTML(label="Contact", value="<img src='https://huggingface.co/datasets/bigcode/admin/resolve/main/bigcode_contact.png' alt='contact' style='display: block; margin: auto; max-width: 800px;'>") demo.launch()