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import gradio as gr
description = """# <p style="text-align: center; color: white;"> 🎅 <span style='color: #ff75b3;'>SantaCoder:</span> Code Generation </p>
<span style='color: white;'>This is a demo to generate code with <a href="https://huggingface.co/bigcode/santacoder" style="color: #ff75b3;">SantaCoder</a>,
a 1.1B parameter model for code generation in Python, Java & JavaScript. The model can also do infilling, just specify where you would like the model to complete code
with the <span style='color: #ff75b3;'><FILL-HERE></span> token.</span>"""
def post_processing(prompt, completion):
completion = "<span style='color: #ff75b3;'>" + completion + "</span>"
prompt = "<span style='color: #727cd6;'>" + prompt + "</span>"
code_html = f"<br><hr><br><pre style='font-size: 12px'><code>{prompt}{completion}</code></pre><br><hr>"
return GENERATION_TITLE + code_html
def post_processing_fim(prefix, middle, suffix):
prefix = "<span style='color: #727cd6;'>" + prefix + "</span>"
middle = "<span style='color: #ff75b3;'>" + middle + "</span>"
suffix = "<span style='color: #727cd6;'>" + suffix + "</span>"
code_html = f"<br><hr><br><pre style='font-size: 12px'><code>{prefix}{middle}{suffix}</code></pre><br><hr>"
return GENERATION_TITLE + code_html
def fim_generation(prompt, max_new_tokens, temperature):
prefix = prompt.split("<FILL-HERE>")[0]
suffix = prompt.split("<FILL-HERE>")[1]
[middle] = infill((prefix, suffix), max_new_tokens, temperature)
return post_processing_fim(prefix, middle, suffix)
def extract_fim_part(s: str):
# Find the index of
start = s.find(FIM_MIDDLE) + len(FIM_MIDDLE)
stop = s.find(EOD, start) or len(s)
return s[start:stop]
def infill(prefix_suffix_tuples, max_new_tokens, temperature):
if type(prefix_suffix_tuples) == tuple:
prefix_suffix_tuples = [prefix_suffix_tuples]
prompts = [f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}" for prefix, suffix in prefix_suffix_tuples]
# `return_token_type_ids=False` is essential, or we get nonsense output.
inputs = tokenizer_fim(prompts, return_tensors="pt", padding=True, return_token_type_ids=False).to(device)
with torch.no_grad():
outputs = model.generate(
**inputs,
do_sample=True,
temperature=temperature,
max_new_tokens=max_new_tokens,
pad_token_id=tokenizer.pad_token_id
)
# WARNING: cannot use skip_special_tokens, because it blows away the FIM special tokens.
return [
extract_fim_part(tokenizer_fim.decode(tensor, skip_special_tokens=False)) for tensor in outputs
]
def code_generation(prompt, max_new_tokens, temperature=0.2, seed=42):
#set_seed(seed)
if "<FILL-HERE>" in prompt:
return fim_generation(prompt, max_new_tokens, temperature=0.2)
else:
completion = pipe(prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_new_tokens)[0]['generated_text']
completion = completion[len(prompt):]
return post_processing(prompt, completion)
with gr.Blocks():
with gr.Row():
_, colum_2, _ = gr.Column(scale=1), gr.Column(scale=6), gr.Column(scale=1)
with colum_2:
gr.Markdown(value=description)
code = gr.Textbox(lines=5, label="Input code", value="def all_odd_elements(sequence):\n \"\"\"Returns every odd element of the sequence.\"\"\"")
with gr.Accordion("Advanced settings", open=False):
max_new_tokens= gr.Slider(
minimum=8,
maximum=1024,
step=1,
value=48,
label="Number of tokens to generate",
)
temperature = gr.Slider(
minimum=0.1,
maximum=2.5,
step=0.1,
value=0.2,
label="Temperature",
)
seed = gr.Slider(
minimum=0,
maximum=1000,
step=1,
label="Random seed to use for the generation"
)
run = gr.Button()
output = gr.HTML(label="Generated code")
event = run.click(code_generation, [code, max_new_tokens, temperature, seed], output, api_name="predict")
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() |