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import json
import os
import shutil
import requests
import gradio as gr
from huggingface_hub import Repository
from text_generation import Client
from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css
HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_URL = "https://api-inference.huggingface.co/models/codellama/CodeLlama-7b-hf"
FIM_PREFIX = "<PRE> "
FIM_MIDDLE = " <MID>"
FIM_SUFFIX = "<SUF>"
FIM_INDICATOR = "<FILL_HERE>"
EOS_STRING = "</s>"
EOT_STRING = "<EOT>"
theme = gr.themes.Monochrome(
primary_hue="indigo",
secondary_hue="blue",
neutral_hue="slate",
radius_size=gr.themes.sizes.radius_sm,
font=[
gr.themes.GoogleFont("Open Sans"),
"ui-sans-serif",
"system-ui",
"sans-serif",
],
)
client = Client(
API_URL,
headers={"Authorization": f"Bearer {HF_TOKEN}"},
)
def generate(
prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
fim_mode = False
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
if FIM_INDICATOR in prompt:
fim_mode = True
try:
prefix, suffix = prompt.split(FIM_INDICATOR)
except:
raise ValueError(f"Only one {FIM_INDICATOR} allowed in prompt!")
prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}"
stream = client.generate_stream(prompt, **generate_kwargs)
if fim_mode:
output = prefix
else:
output = prompt
previous_token = ""
for response in stream:
if response.token.text in [EOS_STRING, EOT_STRING]:
if fim_mode:
output += suffix
else:
return output
else:
output += response.token.text
previous_token = response.token.text
yield output
return output
examples = [
"X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1)\n\n# Train a logistic regression model, predict the labels on the test set and compute the accuracy score",
"// Returns every other value in the array as a new array.\nfunction everyOther(arr) {",
"Poor English: She no went to the market. Corrected English:",
"def alternating(list1, list2):\n results = []\n for i in range(min(len(list1), len(list2))):\n results.append(list1[i])\n results.append(list2[i])\n if len(list1) > len(list2):\n <FILL_HERE>\n else:\n results.extend(list2[i+1:])\n return results",
]
def process_example(args):
for x in generate(args):
pass
return x
css = ".generating {visibility: hidden}"
monospace_css = """
#q-input textarea {
font-family: monospace, 'Consolas', Courier, monospace;
}
"""
css += share_btn_css + monospace_css + ".gradio-container {color: black}"
description = """
<div style="text-align: center;">
<h1> 🦙 CodeLlama Playground</h1>
</div>
<div style="text-align: left;">
<p>This is a demo to generate text and code with the following Code Llama model (7B). Please note that this model is not designed for instruction purposes but for code completion. If you're looking for instruction or want to chat with a fine-tuned model, you can visit the <a href="https://huggingface.co/codellama/">Code Llama Org</a> and select an instruct model.</p>
</div>
"""
with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
with gr.Column():
gr.Markdown(description)
with gr.Row():
with gr.Column():
instruction = gr.Textbox(
placeholder="Enter your code here",
lines=5,
label="Input",
elem_id="q-input",
)
submit = gr.Button("Generate", variant="primary")
output = gr.Code(elem_id="q-output", lines=30, label="Output")
with gr.Row():
with gr.Column():
with gr.Accordion("Advanced settings", open=False):
with gr.Row():
column_1, column_2 = gr.Column(), gr.Column()
with column_1:
temperature = gr.Slider(
label="Temperature",
value=0.2,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
)
max_new_tokens = gr.Slider(
label="Max new tokens",
value=256,
minimum=0,
maximum=8192,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
)
with column_2:
top_p = gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
)
repetition_penalty = gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
)
with gr.Group(elem_id="share-btn-container"):
community_icon = gr.HTML(community_icon_html, visible=True)
loading_icon = gr.HTML(loading_icon_html, visible=True)
share_button = gr.Button(
"Share to community", elem_id="share-btn", visible=True
)
gr.Examples(
examples=examples,
inputs=[instruction],
cache_examples=False,
fn=process_example,
outputs=[output],
)
submit.click(
generate,
inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty],
outputs=[output],
)
share_button.click(None, [], [], _js=share_js)
demo.queue(concurrency_count=16).launch(debug=True) |