import json import logging import os import re import string import html import gradio as gr import nh3 from elasticsearch import Elasticsearch from elasticsearch_dsl import Search, Q # es = Elasticsearch(os.environ.get("host"), timeout=100, http_compress=True, maxsize=1000) es = Elasticsearch(os.environ.get("host"), http_compress=True, timeout=200) def mark_tokens_bold(text, tokens): for token in tokens: if token in ["<", "b", "/", ">"]: continue pattern = re.escape(token) #r"\b" + re.escape(token) + r"\b" text = re.sub(pattern, "" + token + "", text) return text def process_results(results, query): if len(results) == 0: return """

No results retrieved.



""" results_html = "" for result in results: text_html = result["text"] if query.startswith('"') and query.endswith('"'): text_html = mark_tokens_bold(text_html, query[1:-1].split(" ")) else: text_html = mark_tokens_bold(text_html, query.split(" ")) repository = result["repository"] path = result["path"] license = result["license"] language = result["language"] code_height = min(600, len(text_html.split('\n')) * 20) # limit to maximum height of 600px results_html += """\

Source: {}   |   Language: \ {}   |   License: {}

{}

""".format(repository, path, f"{repository}/{path}", language, license, code_height, 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): print(es.ping()) query = query[:200] if query.startswith('"') and query.endswith('"'): response = phrase_query(query[1:-1], num_results=num_results) else: response = match_query(query, num_results=num_results) results = [{"text": html.escape(hit.content), "repository": hit.repository, "path":hit.path, "license": hit.license[0], "language": hit.language} for hit in response] return process_results(results, query) description = """#

StarCoder: Dataset Search 🔍

The server is currently down. Thank you for your patience as we migrate and re-index

When using StarCoder to generate code, it might produce close or exact copies of code in the pretraining dataset. Identifying such cases can provide important context, and help credit the original developer of the code. 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.

This first iteration of the search tool truncates queries down to 200 characters, so as not to overwhelm the server it is currently running on. Please not that this is not a production-ready app, but rather a research tool that we make available as a proof-of-concept. If you need a reliable search app for your business or research, we would advise you to index the dataset yourself.
""" 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", elem_id="q-input", interactive=False ) with gr.Row(): k = gr.Slider(1, 100, value=10, step=1, label="Max Results") with gr.Row(): submit_btn = gr.Button("Submit", interactive=False) 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)