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import json | |
import logging | |
import os | |
import re | |
import string | |
import gradio as gr | |
from elasticsearch import Elasticsearch | |
from elasticsearch_dsl import Search, Q | |
es = Elasticsearch(os.environ.get("host"), timeout=100, http_compress=True, maxsize=1000) | |
def mark_tokens_bold(string, tokens): | |
for token in tokens: | |
pattern = re.escape(token) #r"\b" + re.escape(token) + r"\b" | |
string = re.sub(pattern, "<span style='color: #e6b800;'><b>" + token + "</b></span>", string) | |
return string | |
def process_results(results): | |
if len(results) == 0: | |
return """<br><p>No results retrieved.</p><br><hr>""" | |
results_html = "" | |
for result in results: | |
text_html = result["text"] | |
# text_html = mark_tokens_bold(text_html, highlight_terms) | |
repository = result["repository"] | |
license = result["license"] | |
language = result["language"] | |
results_html += """\ | |
<p style='font-size:16px; text-align: left;'>Source: <span style='color: #00134d;'>{}</span></p> | |
<br> | |
<p style='font-size:16px;> Language:<span style='color: #00134d;'>Python</span></p> | |
<br> | |
<p style='font-size:16px;> License:<span style='color: #00134d;'>MIT</span></p> | |
<br> | |
<pre style='height: 600px; overflow-y: scroll; overflow-x: hidden; color: #d9d9d9;border: 1px solid #e6b800; padding: 10px'><code>{}</code></pre> | |
<br> | |
<hr> | |
<br> | |
""".format(repository, text_html) | |
return results_html | |
def match_query(query, num_results=10): | |
s = Search(using=es, index=os.environ.get("index")) | |
s.query = Q("match", content=query) | |
s = s[:num_results] | |
response = s.execute() | |
return response | |
def phrase_query(query, num_results=10): | |
s = Search(using=es, index=os.environ.get("index")) | |
s.query = Q("match_phrase", content=query) | |
s = s[:num_results] | |
response = s.execute() | |
return response | |
def search(query, num_results=10): | |
if query.startswith('"') and query.endswith('"'): | |
print("HERE") | |
response = phrase_query(query[1:-1], num_results=num_results) | |
print(len(response)) | |
else: | |
response = match_query(query, num_results=num_results) | |
results = [{"text": hit.content, "repository": f"{hit.repository}/{hit.path}", "license": hit.license, "language": hit.language} for hit in response] | |
return process_results(results) | |
description = """# <p style="text-align: center; color: white;"><span style='color: #e6b800;'>StarCoder:</span> Dataset Search π </p> | |
<span style='color: white;'>When using <a href="https://huggingface.co/bigcode/large-model" style="color: #e6b800;">StarCoder</a> to generate code, it might produce exact copies of code in the pretraining dataset. \ | |
In that case, the code license might have requirements to comply with. With this search tool, our aim is to help in identifying if the code belongs to an existing repository. For exact matches, enclose your query in double quotes.</span>""" | |
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", | |
], | |
) | |
css = ".generating {visibility: hidden}" | |
monospace_css = """ | |
#q-input textarea { | |
font-family: monospace, 'Consolas', Courier, monospace; | |
} | |
""" | |
css = monospace_css + ".gradio-container {color: black}" | |
if __name__ == "__main__": | |
demo = gr.Blocks( | |
theme=theme, | |
css=css, | |
) | |
with demo: | |
with gr.Row(): | |
gr.Markdown(value=description) | |
with gr.Row(): | |
query = gr.Textbox(lines=5, placeholder="Type your query here...", label="Query") | |
with gr.Row(): | |
k = gr.Slider(1, 100, value=10, step=1, label="Max Results") | |
with gr.Row(): | |
submit_btn = gr.Button("Submit") | |
with gr.Row(): | |
results = gr.HTML(label="Results", value="") | |
def submit(query, k, lang="en"): | |
query = query.strip() | |
if query is None or query == "": | |
return "", "" | |
return { | |
results: search(query, k), | |
} | |
query.submit(fn=submit, inputs=[query, k], outputs=[results]) | |
submit_btn.click(submit, inputs=[query, k], outputs=[results]) | |
demo.launch(enable_queue=True, debug=True) | |