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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
model_id = "deepseek-ai/deepseek-coder-6.7b-instruct"
# model_id = "deepseek-ai/deepseek-coder-33b-instruct"
# model_id = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
# model_id = "deepseek-ai/DeepSeek-Coder-V2-Instruct"
# This works best
# model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
# device_map=None,
# torch_dtype=torch.float32,
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True
)
# model.to("cpu")
def generate_code(prompt, style="Clean & Pythonic"):
# spinner.update(visible=True)
if style == "Verbose like a 15th-century manuscript":
prompt = "In a manner most detailed, write code that... " + prompt
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs,
# max_new_tokens=100,
max_new_tokens=512,
do_sample=False,
temperature=1.0,
top_p=0.95,
top_k=50,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id
)
# spinner.update(visible=False)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
with gr.Blocks() as demo:
gr.Markdown("<h1 style='text-align:center;'>Codice Da Vinci 📜💻</h1>")
with gr.Row():
prompt = gr.Textbox(label="How shall Codice Da Vinci help today?", lines=3)
style = gr.Dropdown(["Clean & Pythonic", "Verbose like a 15th-century manuscript"], label="Code Style")
generate_btn = gr.Button("Generate")
output = gr.Code(label="🧾 Leonardo's Work")
spinner = gr.HTML(
"<div style='text-align:center'><img src='https://media2.giphy.com/media/v1.Y2lkPTc5MGI3NjExMXViMm02MnR6bGJ4c2h3ajYzdWNtNXNtYnNic3lnN2xyZzlzbm9seSZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9cw/k32ddF9WVs44OUaZAm/giphy.gif' width='80'></div>",
visible=False
)
def wrapped_generate(prompt, style):
code = generate_code(prompt, style)
return gr.update(visible=False), code
generate_btn.click(
fn=wrapped_generate,
inputs=[prompt, style],
outputs=[spinner, output],
show_progress=True,
preprocess=True
)
generate_btn.click(
fn=lambda: gr.update(visible=True),
inputs=[],
outputs=[spinner],
queue=False
)
demo.launch() |