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| import http.client as http_client | |
| import json | |
| import logging | |
| import os | |
| import re | |
| import string | |
| import gradio as gr | |
| import requests | |
| def get_docid_html(docid): | |
| data_org, dataset, docid = docid.split("/") | |
| docid_html = """<a | |
| class="underline-on-hover" | |
| title="I am hovering over the text" | |
| style="color:#2D31FA;" | |
| href="https://huggingface.co/datasets/bigscience-data/{}" | |
| target="_blank">{}</a><span style="color: #7978FF;">/{}</span>""".format( | |
| dataset, data_org + "/" + dataset, docid | |
| ) | |
| return docid_html | |
| PII_TAGS = {"KEY", "EMAIL", "USER", "IP_ADDRESS", "ID", "IPv4", "IPv6"} | |
| PII_PREFIX = "PI:" | |
| def process_pii(text): | |
| for tag in PII_TAGS: | |
| text = text.replace( | |
| PII_PREFIX + tag, | |
| """<b><mark style="background: Fuchsia; color: Lime;">REDACTED {}</mark></b>""".format(tag), | |
| ) | |
| return text | |
| def process_results(results, highlight_terms): | |
| if len(results) == 0: | |
| return """<br><p style='font-family: Arial; color:Silver; text-align: center;'> | |
| No results retrieved.</p><br><hr>""" | |
| results_html = "" | |
| for result in results: | |
| tokens = result["text"].split() | |
| tokens_html = [] | |
| for token in tokens: | |
| if token in highlight_terms: | |
| tokens_html.append("<b>{}</b>".format(token)) | |
| else: | |
| tokens_html.append(token) | |
| tokens_html = " ".join(tokens_html) | |
| tokens_html = process_pii(tokens_html) | |
| meta_html = ( | |
| """ | |
| <p class='underline-on-hover' style='font-size:12px; font-family: Arial; color:#585858; text-align: left;'> | |
| <a href='{}' target='_blank'>{}</a></p>""".format( | |
| result["meta"]["url"], result["meta"]["url"] | |
| ) | |
| if "meta" in result and result["meta"] is not None and "url" in result["meta"] | |
| else "" | |
| ) | |
| docid_html = get_docid_html(result["docid"]) | |
| results_html += """{} | |
| <p style='font-size:14px; font-family: Arial; color:#7978FF; text-align: left;'>Document ID: {}</p> | |
| <p style='font-size:12px; font-family: Arial; color:MediumAquaMarine'>Language: {}</p> | |
| <p style='font-family: Arial;'>{}</p> | |
| <br> | |
| """.format( | |
| meta_html, docid_html, result["lang"], tokens_html | |
| ) | |
| return results_html + "<hr>" | |
| def scisearch(query, language, num_results=10): | |
| try: | |
| query = query.strip() | |
| if query == "" or query is None: | |
| return | |
| post_data = {"query": query, "k": num_results} | |
| if language != "detect_language": | |
| post_data["lang"] = language | |
| output = requests.post( | |
| os.environ.get("address"), | |
| headers={"Content-type": "application/json"}, | |
| data=json.dumps(post_data), | |
| timeout=60, | |
| ) | |
| payload = json.loads(output.text) | |
| if "err" in payload: | |
| if payload["err"]["type"] == "unsupported_lang": | |
| detected_lang = payload["err"]["meta"]["detected_lang"] | |
| return f""" | |
| <p style='font-size:18px; font-family: Arial; color:MediumVioletRed; text-align: center;'> | |
| Detected language <b>{detected_lang}</b> is not supported.<br> | |
| Please choose a language from the dropdown or type another query. | |
| </p><br><hr><br>""" | |
| results = payload["results"] | |
| highlight_terms = payload["highlight_terms"] | |
| if language == "detect_language": | |
| return ( | |
| ( | |
| f"""<p style='font-family: Arial; color:MediumAquaMarine; text-align: center; line-height: 3em'> | |
| Detected language: <b>{results[0]["lang"]}</b></p><br><hr><br>""" | |
| if len(results) > 0 and language == "detect_language" | |
| else "" | |
| ) | |
| + process_results(results, highlight_terms) | |
| ) | |
| if language == "all": | |
| results_html = "" | |
| for lang, results_for_lang in results.items(): | |
| if len(results_for_lang) == 0: | |
| results_html += f"""<p style='font-family: Arial; color:Silver; text-align: left; line-height: 3em'> | |
| No results for language: <b>{lang}</b><hr></p>""" | |
| continue | |
| collapsible_results = f""" | |
| <details> | |
| <summary style='font-family: Arial; color:MediumAquaMarine; text-align: left; line-height: 3em'> | |
| Results for language: <b>{lang}</b><hr> | |
| </summary> | |
| {process_results(results_for_lang, highlight_terms)} | |
| </details>""" | |
| results_html += collapsible_results | |
| return results_html | |
| return process_results(results, highlight_terms) | |
| except Exception as e: | |
| results_html = f""" | |
| <p style='font-size:18px; font-family: Arial; color:MediumVioletRed; text-align: center;'> | |
| Raised {type(e).__name__}</p> | |
| <p style='font-size:14px; font-family: Arial; '> | |
| Check if a relevant discussion already exists in the Community tab. If not, please open a discussion. | |
| </p> | |
| """ | |
| return results_html | |
| def flag(query, language, num_results, issue_description): | |
| try: | |
| post_data = {"query": query, "k": num_results, "flag": True, "description": issue_description} | |
| if language != "detect_language": | |
| post_data["lang"] = language | |
| output = requests.post( | |
| os.environ.get("address"), | |
| headers={"Content-type": "application/json"}, | |
| data=json.dumps(post_data), | |
| timeout=120, | |
| ) | |
| results = json.loads(output.text) | |
| except: | |
| print("Error flagging") | |
| return "" | |
| description = """# <p style="text-align: center;">🌏gæa🌖</p> | |
| # <p style="text-align: center;"> gæa</p> | |
| A large scale text corpora search engine.""" | |
| if __name__ == "__main__": | |
| demo = gr.Blocks( | |
| css=".underline-on-hover:hover { text-decoration: underline; } .flagging { font-size:12px; color:Silver; }" | |
| ) | |
| with demo: | |
| with gr.Row(): | |
| gr.Markdown(value=description) | |
| with gr.Row(): | |
| query = gr.Textbox(lines=2, placeholder="Type your query here...", label="Query") | |
| with gr.Row(): | |
| lang = gr.Dropdown( | |
| choices=[ | |
| "ar", | |
| "ca", | |
| "code", | |
| "en", | |
| "es", | |
| "eu", | |
| "fr", | |
| "id", | |
| "indic", | |
| "nigercongo", | |
| "pt", | |
| "vi", | |
| "zh", | |
| "detect_language", | |
| "all", | |
| ], | |
| value="en", | |
| label="Language", | |
| ) | |
| 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") | |
| flag_description = """ | |
| <p class='flagging'> | |
| If you choose to flag your search, we will save the query, language and the number of results you requested. | |
| Please consider adding any additional context in the box on the right.</p>""" | |
| with gr.Column(visible=False) as flagging_form: | |
| flag_txt = gr.Textbox( | |
| lines=1, | |
| placeholder="Type here...", | |
| label="""If you choose to flag your search, we will save the query, language and the number of results | |
| you requested. Please consider adding relevant additional context below:""", | |
| ) | |
| flag_btn = gr.Button("Flag Results") | |
| flag_btn.click(flag, inputs=[query, lang, k, flag_txt], outputs=[flag_txt]) | |
| def submit(query, lang, k): | |
| if query == "": | |
| return ["", ""] | |
| return { | |
| results: scisearch(query, lang, k), | |
| flagging_form: gr.update(visible=True), | |
| } | |
| submit_btn.click(submit, inputs=[query, lang, k], outputs=[results, flagging_form]) | |
| demo.launch(enable_queue=True, debug=True) | |