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from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import torch
# ==========================
# Load your model
# ==========================
MODEL_ID = "OSS-forge/DeepSeek-Coder-1.3B-cleaned"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
device_map="auto" if torch.cuda.is_available() else None,
)
model.to(device)
model.eval()
# ==========================
# Example prompt from the paper
# ==========================
PAPER_PROMPT = (
"def hash_(listing_id):\n"
" \"\"\"Creates an hashed column using the listing id for the vehicle\"\"\"\n"
)
# ==========================
# Prompt builder
# ==========================
def build_instruction_prompt(instruction: str) -> str:
return '''
You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, and you only answer questions related to computer science.
### Instruction:
{}
### Response:
'''.format(instruction.strip()).lstrip()
# ==========================
# Gradio logic
# ==========================
def generate_code(instruction, chat_history, is_first_time):
if chat_history is None or is_first_time:
chat_history = []
instruction = instruction.strip()
if not instruction:
return chat_history, gr.update(value=instruction), False
prompt = build_instruction_prompt(instruction)
inputs = tokenizer(
prompt,
return_tensors="pt",
padding=True,
truncation=True,
max_length=512,
).to(device)
try:
stop_id = tokenizer.convert_tokens_to_ids("<|EOT|>")
except Exception:
stop_id = tokenizer.eos_token_id
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=256,
do_sample=False,
pad_token_id=stop_id,
eos_token_id=stop_id,
)
input_len = inputs["input_ids"].shape[1]
generated_tokens = outputs[0, input_len:]
code = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
user_message = f"**Instruction**:\n{instruction}"
ai_message = f"**Generated code**:\n```python\n{code}\n```"
chat_history = chat_history + [
{"role": "user", "content": user_message},
{"role": "assistant", "content": ai_message},
]
return chat_history, gr.update(value=""), False
def reset_interface():
return [], gr.update(value=""), True
# ==========================
# Gradio UI
# ==========================
with gr.Blocks(title="Python Code Generator") as demo:
gr.Markdown("# 🧠 Higher-Quality DeepSeek-Coder for Python")
gr.Markdown(
"Generate Python code from natural language instructions using a DeepSeek-Coder model trained on cleaned data."
)
with gr.Row():
with gr.Column(scale=2):
instruction_input = gr.Textbox(
label="Instruction",
placeholder=(
"Describe the code you want. "
"E.g., 'Write a Python function that checks if a number is prime.'"
),
lines=4,
)
# 🔹 Example prompt from the paper (click to fill the textbox)
gr.Examples(
examples=[[PAPER_PROMPT]],
inputs=[instruction_input],
label="Try the paper's hash_ example",
)
is_first = gr.State(True)
submit_btn = gr.Button("Generate Code")
reset_btn = gr.Button("Start Over")
with gr.Column(scale=3):
chat_output = gr.Chatbot(
label="Conversation",
height=500,
type="messages",
)
submit_btn.click(
fn=generate_code,
inputs=[instruction_input, chat_output, is_first],
outputs=[chat_output, instruction_input, is_first],
)
reset_btn.click(
fn=reset_interface,
outputs=[chat_output, instruction_input, is_first],
)
if __name__ == "__main__":
print("Launching Gradio interface...")
demo.queue(max_size=10).launch()
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