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| import json | |
| import os | |
| import traceback | |
| from typing import List, Tuple | |
| import gradio as gr | |
| import requests | |
| from huggingface_hub import HfApi | |
| hf_api = HfApi() | |
| roots_datasets = { | |
| dset.id.split("/")[-1]: dset | |
| for dset in hf_api.list_datasets( | |
| author="bigscience-data", use_auth_token=os.environ.get("bigscience_data_token") | |
| ) | |
| } | |
| def get_docid_html(docid): | |
| data_org, dataset, docid = docid.split("/") | |
| metadata = roots_datasets[dataset] | |
| if metadata.private: | |
| docid_html = """ | |
| <a title="This dataset is private. See the introductory text for more information" | |
| style="color:#AA4A44; font-weight: bold; text-decoration:none" | |
| onmouseover="style='color:#AA4A44; font-weight: bold; text-decoration:underline'" | |
| onmouseout="style='color:#AA4A44; font-weight: bold; text-decoration:none'" | |
| href="https://huggingface.co/datasets/bigscience-data/{dataset}" | |
| target="_blank"> | |
| 🔒{dataset} | |
| </a> | |
| <span style="color:#7978FF; ">/{docid}</span>""".format( | |
| dataset=dataset, docid=docid | |
| ) | |
| else: | |
| docid_html = """ | |
| <a title="This dataset is licensed {metadata}" | |
| style="color:#7978FF; font-weight: bold; text-decoration:none" | |
| onmouseover="style='color:#7978FF; font-weight: bold; text-decoration:underline'" | |
| onmouseout="style='color:#7978FF; font-weight: bold; text-decoration:none'" | |
| href="https://huggingface.co/datasets/bigscience-data/{dataset}" | |
| target="_blank"> | |
| {dataset} | |
| </a> | |
| <span style="color:#7978FF; ">/{docid}</span>""".format( | |
| metadata=metadata.tags[0].split(":")[-1], dataset=dataset, docid=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 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 "" | |
| def format_result(result, highlight_terms, exact_search, datasets_filter=None): | |
| text, url, docid = result | |
| if datasets_filter is not None: | |
| datasets_filter = set(datasets_filter) | |
| dataset = docid.split("/")[1] | |
| if not dataset in datasets_filter: | |
| return "" | |
| if exact_search: | |
| query_start = text.find(highlight_terms) | |
| query_end = query_start + len(highlight_terms) | |
| tokens_html = text[0:query_start] | |
| tokens_html += "<b>{}</b>".format(text[query_start:query_end]) | |
| tokens_html += text[query_end:] | |
| else: | |
| tokens = 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) | |
| url_html = ( | |
| """ | |
| <span style='font-size:12px; font-family: Arial; color:Silver; text-align: left;'> | |
| <a style='text-decoration:none; color:Silver;' | |
| onmouseover="style='text-decoration:underline; color:Silver;'" | |
| onmouseout="style='text-decoration:none; color:Silver;'" | |
| href='{url}' | |
| target="_blank"> | |
| {url} | |
| </a> | |
| </span><br> | |
| """.format( | |
| url=url | |
| ) | |
| if url is not None | |
| else "" | |
| ) | |
| docid_html = get_docid_html(docid) | |
| language = "FIXME" | |
| result_html = """{} | |
| <span style='font-size:14px; font-family: Arial; color:#7978FF; text-align: left;'>Document ID: {}</span><br> | |
| <!-- <span style='font-size:12px; font-family: Arial; color:MediumAquaMarine'>Language: {}</span><br> --> | |
| <span style='font-family: Arial;'>{}</span><br> | |
| <br> | |
| """.format( | |
| url_html, docid_html, language, tokens_html | |
| ) | |
| return "<p>" + result_html + "</p>" | |
| def format_result_page( | |
| language, results, highlight_terms, num_results, exact_search, datasets_filter=None | |
| ) -> gr.HTML: | |
| filtered_num_results = 0 | |
| header_html = "" | |
| if language == "detect_language" and not exact_search: | |
| header_html += """<div style='font-family: Arial; color:MediumAquaMarine; text-align: center; line-height: 3em'> | |
| Detected language: <b style='color:MediumAquaMarine'>{}</b></div>""".format( | |
| list(results.keys())[0] | |
| ) | |
| results_html = "" | |
| for lang, results_for_lang in results.items(): | |
| if len(results_for_lang) == 0: | |
| if exact_search: | |
| results_html += """<div style='font-family: Arial; color:Silver; text-align: left; line-height: 3em'> | |
| No results found.</div>""" | |
| else: | |
| results_html += """<div style='font-family: Arial; color:Silver; text-align: left; line-height: 3em'> | |
| No results for language: <b>{}</b></div>""".format( | |
| lang | |
| ) | |
| continue | |
| results_for_lang_html = "" | |
| for result in results_for_lang: | |
| result_html = format_result( | |
| result, highlight_terms, exact_search, datasets_filter | |
| ) | |
| if result_html != "": | |
| filtered_num_results += 1 | |
| results_for_lang_html += result_html | |
| if language == "all" and not exact_search: | |
| results_for_lang_html = f""" | |
| <details> | |
| <summary style='font-family: Arial; color:MediumAquaMarine; text-align: left; line-height: 3em'> | |
| Results for language: <b>{lang}</b> | |
| </summary> | |
| {results_for_lang_html} | |
| </details>""" | |
| results_html += results_for_lang_html | |
| if num_results is not None: | |
| header_html += """<div style='font-family: Arial; color:MediumAquaMarine; text-align: center; line-height: 3em'> | |
| Total number of matches: <b style='color:MediumAquaMarine'>{}</b></div>""".format( | |
| num_results | |
| ) | |
| return header_html + results_html | |
| def extract_results_from_payload(query, language, payload, exact_search): | |
| results = payload["results"] | |
| processed_results = dict() | |
| datasets = set() | |
| highlight_terms = None | |
| num_results = None | |
| if exact_search: | |
| highlight_terms = query | |
| num_results = payload["num_results"] | |
| results = {"dummy": results} | |
| else: | |
| highlight_terms = payload["highlight_terms"] | |
| for lang, results_for_lang in results.items(): | |
| processed_results[lang] = list() | |
| for result in results_for_lang: | |
| text = result["text"] | |
| url = ( | |
| result["meta"]["url"] | |
| if "meta" in result | |
| and result["meta"] is not None | |
| and "url" in result["meta"] | |
| else None | |
| ) | |
| docid = result["docid"] | |
| _, dataset, _ = docid.split("/") | |
| datasets.add(dataset) | |
| processed_results[lang].append((text, url, docid)) | |
| return processed_results, highlight_terms, num_results, list(datasets) | |
| def process_error(error_type): | |
| if error_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>""" | |
| def extract_error_from_payload(payload): | |
| if "err" in payload: | |
| return payload["err"]["type"] | |
| return None | |
| def request_payload(query, language, exact_search, num_results=10, received_results=0): | |
| post_data = {"query": query, "k": num_results, "received_results": received_results} | |
| if language != "detect_language": | |
| post_data["lang"] = language | |
| address = "http://34.105.160.81:8080" if exact_search else os.environ.get("address") | |
| output = requests.post( | |
| address, | |
| headers={"Content-type": "application/json"}, | |
| data=json.dumps(post_data), | |
| timeout=60, | |
| ) | |
| payload = json.loads(output.text) | |
| return payload | |
| title = ( | |
| """<p style="text-align: center; font-size:28px"> 🌸 🔎 ROOTS search tool 🔍 🌸 </p>""" | |
| ) | |
| description = """ | |
| The ROOTS corpus was developed during the [BigScience workshop](https://bigscience.huggingface.co/) for the purpose | |
| of training the Multilingual Large Language Model [BLOOM](https://huggingface.co/bigscience/bloom). This tool allows | |
| you to search through the ROOTS corpus. We serve a BM25 index for each language or group of languages included in | |
| ROOTS. You can read more about the details of the tool design | |
| [here](https://huggingface.co/spaces/bigscience-data/scisearch/blob/main/roots_search_tool_specs.pdf). For more | |
| information and instructions on how to access the full corpus check [this form](https://forms.gle/qyYswbEL5kA23Wu99).""" | |
| if __name__ == "__main__": | |
| demo = gr.Blocks( | |
| css=".underline-on-hover:hover { text-decoration: underline; } .flagging { font-size:12px; color:Silver; }" | |
| ) | |
| with demo: | |
| processed_results_state = gr.State([]) | |
| highlight_terms_state = gr.State([]) | |
| num_results_state = gr.State(0) | |
| exact_search_state = gr.State(False) | |
| lang_state = gr.State("") | |
| max_page_size_state = gr.State(100) | |
| received_results_state = gr.State(0) | |
| with gr.Row(): | |
| gr.Markdown(value=title) | |
| with gr.Row(): | |
| gr.Markdown(value=description) | |
| with gr.Row(): | |
| query = gr.Textbox( | |
| lines=1, | |
| max_lines=1, | |
| placeholder="Put your query in double quotes for exact search.", | |
| 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", | |
| ) | |
| k = gr.Slider(1, 100, value=10, step=1, label="Max Results") | |
| with gr.Row(): | |
| submit_btn = gr.Button("Submit") | |
| with gr.Row(visible=False) as datasets_filter: | |
| available_datasets = gr.Dropdown( | |
| type="value", | |
| choices=[], | |
| value=[], | |
| label="Datasets Filter", | |
| multiselect=True, | |
| ) | |
| with gr.Row(): | |
| results = gr.HTML(label="Results") | |
| with gr.Row(visible=False) as pagination: | |
| next_page_btn = gr.Button("Next Page") | |
| 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 run_query(query, lang, k, dropdown_input, max_page_size, received_results): | |
| query = query.strip() | |
| exact_search = False | |
| if query.startswith('"') and query.endswith('"') and len(query) >= 2: | |
| exact_search = True | |
| query = query[1:-1] | |
| k = max_page_size | |
| else: | |
| query = " ".join(query.split()) | |
| if query == "" or query is None: | |
| return None | |
| print("submitting", query, lang, k) | |
| payload = request_payload(query, lang, exact_search, k, received_results) | |
| err = extract_error_from_payload(payload) | |
| if err is not None: | |
| return process_error(err) | |
| ( | |
| processed_results, | |
| highlight_terms, | |
| num_results, | |
| ds, | |
| ) = extract_results_from_payload( | |
| query, | |
| lang, | |
| payload, | |
| exact_search, | |
| ) | |
| results_html = format_result_page( | |
| lang, processed_results, highlight_terms, num_results, exact_search | |
| ) | |
| return ( | |
| processed_results, | |
| highlight_terms, | |
| num_results, | |
| exact_search, | |
| results_html, | |
| ds, | |
| ) | |
| def submit(query, lang, k, dropdown_input, max_page_size): | |
| ( | |
| processed_results, | |
| highlight_terms, | |
| num_results, | |
| exact_search, | |
| results_html, | |
| datasets, | |
| ) = run_query(query, lang, k, dropdown_input, max_page_size, 0) | |
| has_more_results = exact_search and (num_results > max_page_size) | |
| return { | |
| processed_results_state: processed_results, | |
| highlight_terms_state: highlight_terms, | |
| num_results_state: num_results, | |
| exact_search_state: exact_search, | |
| results: results_html, | |
| flagging_form: gr.update(visible=True), | |
| datasets_filter: gr.update(visible=True), | |
| available_datasets: gr.Dropdown.update( | |
| choices=datasets, value=datasets | |
| ), | |
| pagination: gr.update(visible=has_more_results), | |
| received_results_state: len(next(iter(processed_results.values()))), | |
| } | |
| def next_page( | |
| query, | |
| lang, | |
| k, | |
| dropdown_input, | |
| max_page_size, | |
| received_results, | |
| processed_results, | |
| ): | |
| ( | |
| processed_results, | |
| highlight_terms, | |
| num_results, | |
| exact_search, | |
| results_html, | |
| datasets, | |
| ) = run_query( | |
| query, lang, k, dropdown_input, max_page_size, received_results | |
| ) | |
| num_processed_results = len(next(iter(processed_results.values()))) | |
| has_more_results = exact_search and (num_results > max_page_size) | |
| print("num_processed_results", num_processed_results) | |
| print("has_more_results", has_more_results) | |
| print("current page", len(list(processed_results.values())[0])) | |
| return { | |
| processed_results_state: processed_results, | |
| highlight_terms_state: highlight_terms, | |
| num_results_state: num_results, | |
| exact_search_state: exact_search, | |
| results: results_html, | |
| flagging_form: gr.update(visible=True), | |
| datasets_filter: gr.update(visible=True), | |
| available_datasets: gr.Dropdown.update( | |
| choices=datasets, value=datasets | |
| ), | |
| pagination: gr.update( | |
| visible=num_processed_results >= max_page_size and has_more_results | |
| ), | |
| received_results_state: received_results + num_processed_results, | |
| } | |
| def filter_datasets( | |
| lang, | |
| processed_results, | |
| highlight_terms, | |
| num_results, | |
| exact_search, | |
| datasets_filter, | |
| ): | |
| results_html = format_result_page( | |
| lang, | |
| processed_results, | |
| highlight_terms, | |
| num_results, | |
| exact_search, | |
| datasets_filter, | |
| ) | |
| return {results: results_html} | |
| query.submit( | |
| fn=submit, | |
| inputs=[query, lang, k, available_datasets, max_page_size_state], | |
| outputs=[ | |
| processed_results_state, | |
| highlight_terms_state, | |
| num_results_state, | |
| exact_search_state, | |
| results, | |
| flagging_form, | |
| datasets_filter, | |
| available_datasets, | |
| pagination, | |
| received_results_state, | |
| ], | |
| ) | |
| submit_btn.click( | |
| submit, | |
| inputs=[query, lang, k, available_datasets, max_page_size_state], | |
| outputs=[ | |
| processed_results_state, | |
| highlight_terms_state, | |
| num_results_state, | |
| exact_search_state, | |
| results, | |
| flagging_form, | |
| datasets_filter, | |
| available_datasets, | |
| pagination, | |
| received_results_state, | |
| ], | |
| ) | |
| next_page_btn.click( | |
| next_page, | |
| inputs=[ | |
| query, | |
| lang, | |
| k, | |
| available_datasets, | |
| max_page_size_state, | |
| received_results_state, | |
| processed_results_state, | |
| ], | |
| outputs=[ | |
| processed_results_state, | |
| highlight_terms_state, | |
| num_results_state, | |
| exact_search_state, | |
| results, | |
| flagging_form, | |
| datasets_filter, | |
| available_datasets, | |
| pagination, | |
| received_results_state, | |
| ], | |
| ) | |
| available_datasets.change( | |
| filter_datasets, | |
| inputs=[ | |
| lang, | |
| processed_results_state, | |
| highlight_terms_state, | |
| num_results_state, | |
| exact_search_state, | |
| available_datasets, | |
| ], | |
| outputs=[results], | |
| ) | |
| demo.launch(enable_queue=True, debug=True) | |