' + HOMEPAGE_DEFAULT + '
'
return f"""{saved_content}
"""
def generate_editor_html():
saved_content = read_saved_homepage()
return f"""{saved_content}
{source}
DOI
{reviewed_badge}
{title}
{f'
Authors: {authors}
' if authors else ''}
{f'Journal: {journal}
' if journal else ''}
{f'Date: {date}
' if date else ''}
DOI: {doi_display}
Please enter a valid DOI, for example
"
# CrossRef — primary DOI metadata source
cr_url = "https://api.crossref.org/works/" + urllib.parse.quote(doi, safe='')
cr = _safe_request(cr_url)
if cr and cr.get('message'):
item = cr['message']
titles = item.get('title') or ['Untitled']
title = titles[0] if titles else 'Untitled'
auth = item.get('author') or []
names = []
for a in auth[:5]:
name = ' '.join(x for x in [a.get('given',''), a.get('family','')] if x).strip() or a.get('name','')
if name:
names.append(name)
if len(auth) > 5:
names.append('et al.')
journals = item.get('container-title') or []
journal = journals[0] if journals else ''
date_parts = (item.get('published-print') or item.get('published-online') or item.get('published') or {}).get('date-parts', [[]])
parts = date_parts[0] if date_parts else []
date = '-'.join(str(x) for x in parts) if parts else ''
url = item.get('URL') or f"https://doi.org/{doi}"
return _doi_html_result(doi, title, ', '.join(names), journal, date, url, 'CrossRef')
# Europe PMC fallback
epmc = _safe_request(
"https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=" +
urllib.parse.quote(f'DOI:"{doi}"') + "&resultType=core&pageSize=5&format=json"
)
if epmc:
items = epmc.get('resultList', {}).get('result', [])
if items:
item = items[0]
authors_list = item.get('authorList', {}).get('author', [])
names = [a.get('fullName','') for a in authors_list[:5] if a.get('fullName')]
if len(authors_list) > 5:
names.append('et al.')
pmid = item.get('pmid','')
url = f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/" if pmid else f"https://doi.org/{doi}"
return _doi_html_result(
doi,
item.get('title','Untitled'),
', '.join(names),
item.get('journalTitle',''),
item.get('firstPublicationDate', str(item.get('pubYear',''))),
url,
'Europe PMC'
)
# PubMed final fallback
pm_url = (
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&retmode=json&retmax=5&term=" +
urllib.parse.quote(f'"{doi}"[AID] OR "{doi}"[DOI]')
)
pm = _safe_request(pm_url)
ids = (pm or {}).get('esearchresult', {}).get('idlist', [])
if ids:
sm = _safe_request(
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=json&id=" + ','.join(ids)
)
if sm:
result = sm.get('result', {})
uid = result.get('uids', [ids[0]])[0]
item = result.get(uid, {})
raw = item.get('authors', [])
names = [a.get('name','') for a in raw[:5] if a.get('name')]
if len(raw) > 5:
names.append('et al.')
return _doi_html_result(
doi,
item.get('title','Untitled'),
', '.join(names),
item.get('fulljournalname', item.get('source','')),
item.get('pubdate',''),
f"https://pubmed.ncbi.nlm.nih.gov/{uid}/",
'PubMed'
)
return "10.1093/nar/gkac123. No publication metadata found for " + doi + ".
"
def get_reviewed_table_df():
"""Return all reviewed DOIs as a DataFrame for the admin console."""
columns = ['DOI', 'Reviewed At', 'Link']
rows = []
if _check_reviewed_table():
try:
res = supabase.table('reviewed_articles').select('doi,reviewed_at').order('reviewed_at', desc=True).execute()
for r in (res.data or []):
doi = (r.get('doi') or '').strip()
if doi:
rows.append({'DOI': doi, 'Reviewed At': r.get('reviewed_at') or '', 'Link': f'https://doi.org/{doi}'})
except Exception:
pass
db_ids = {r['DOI'].lower() for r in rows}
for doi in sorted(_reviewed_cache):
if doi not in db_ids:
rows.append({'DOI': doi, 'Reviewed At': 'Session only', 'Link': f'https://doi.org/{doi}'})
return pd.DataFrame(rows, columns=columns)
def reviewed_row_selection(evt: gr.SelectData, current_df):
try:
row_idx = evt.index[0]
doi = str(current_df.iloc[row_idx].get('DOI', '')).strip()
return doi, f'Selected reviewed DOI: {doi}' if doi else 'No DOI selected.'
except Exception as e:
return '', f'Selection error: {e}'
def remove_selected_reviewed_doi(selected_doi, request: gr.Request):
if not request_has_role(request, "senior"):
return 'Senior admin access required.', get_reviewed_table_df(), ''
if not selected_doi or not selected_doi.strip():
return ' Select a marked article first.', get_reviewed_table_df(), ''
msg = remove_reviewed_article(selected_doi)
return msg, get_reviewed_table_df(), ''
def _fetch_biorxiv(query, since_date_str, max_results=100):
"""Fetch from bioRxiv/medRxiv – basic keyword filtering (simple AND on split terms)."""
results = []
try:
today_str = datetime.date.today().strftime("%Y-%m-%d")
since_bxr = since_date_str.replace('/', '-')
# Extract keywords (remove quotes and split)
keywords = [w.strip('"').lower() for w in query.split() if len(w) > 2 and w not in ('AND', 'OR', 'NOT')]
# If query has quotes, keep them as phrase?
# We'll keep it simple: use the raw query as a phrase for title/abstract checking.
for server in ['biorxiv', 'medrxiv']:
url = f"https://api.biorxiv.org/details/{server}/{since_bxr}/{today_str}/0/json"
data = _safe_request(url)
if not data:
continue
collection = data.get('collection', [])
for item in collection:
title = item.get('title', 'Untitled')
abstract = item.get('abstract', '').lower()
# Check if all keywords appear (simple AND)
match = True
for kw in keywords:
if kw not in title.lower() and kw not in abstract:
match = False
break
if not match:
continue
doi = item.get('doi', '')
authors_str = item.get('authors', 'Unknown Authors')
author_list = [a.strip() for a in authors_str.split(';')]
names = author_list[:3]
if len(author_list) > 3:
names.append('et al.')
results.append({
'title': title,
'authors': '; '.join(names),
'journal': f"{server.capitalize()} (Preprint)",
'date': item.get('date', 'N/A'),
'url': f"https://doi.org/{doi}" if doi else f"https://www.{server}.org/",
'doi': doi,
'source': server.capitalize(),
'source_color': '#0891b2',
'source_bg': '#ecfeff',
})
if len(results) >= max_results:
break
if len(results) >= max_results:
break
return results, f"bioRxiv/medRxiv: {len(results)} results"
except Exception as e:
return results, f"bioRxiv/medRxiv: error – {str(e)}"
def _deduplicate(all_results):
"""Remove duplicates by DOI (if present) or by normalised title."""
seen_dois = set()
seen_titles = set()
unique = []
for r in all_results:
doi = r.get('doi', '').strip().lower()
title_key = r.get('title', '').strip().lower()[:80]
if doi and doi in seen_dois:
continue
if title_key and title_key in seen_titles:
continue
if doi:
seen_dois.add(doi)
if title_key:
seen_titles.add(title_key)
unique.append(r)
return unique
def _sort_results(results):
"""Sort results by date descending, with unparseable dates last."""
def date_key(r):
d = r.get('date', '') or ''
for fmt in ('%Y-%m-%d', '%Y/%m/%d', '%Y %b %d', '%Y %b', '%Y'):
try:
return datetime.datetime.strptime(d.strip()[:len(fmt)+2], fmt)
except Exception:
pass
parts = d.strip().split()
if parts:
try:
return datetime.datetime(int(parts[0]), 1, 1)
except Exception:
pass
return datetime.datetime.min
return sorted(results, key=date_key, reverse=True)
def _render_source_badge(source, color, bg):
return (
f'{source}'
)
def _run_multi_db_search(query, selected_sources, since_date, since_date_dash, window_label):
"""
Shared engine for running a multi-database search given an explicit
'since' date window, and rendering the resulting HTML.
"""
if not query or not query.strip():
return "Please enter a valid search query.
"
# If query contains boolean operators, force only PubMed
if _is_boolean_query(query):
selected_sources = ['PubMed'] # override
all_results = []
source_logs = []
source_map = {
'PubMed': lambda: _fetch_pubmed(query, since_date),
'Europe PMC': lambda: _fetch_europe_pmc(query, since_date_dash),
'CrossRef': lambda: _fetch_crossref(query, since_date_dash),
'Semantic Scholar': lambda: _fetch_semantic_scholar(query, since_date_dash),
'bioRxiv / medRxiv': lambda: _fetch_biorxiv(query, since_date_dash),
}
for src_name, fetch_fn in source_map.items():
if src_name not in selected_sources:
continue
try:
res, log = fetch_fn()
all_results.extend(res)
source_logs.append(log)
except Exception as e:
source_logs.append(f"{src_name}: unexpected error – {str(e)}")
if not all_results:
log_html = ' | '.join(source_logs)
return f"""
No papers found for "{query}"
{window_label}.
{log_html}
"""
unique_results = _deduplicate(all_results)
sorted_results = _sort_results(unique_results)
source_counts = {}
for r in sorted_results:
src = r.get('source', 'Unknown')
source_counts[src] = source_counts.get(src, 0) + 1
badge_row = ' '.join(
f''
f'{src}: {cnt}'
for src, cnt in source_counts.items()
)
log_html = ' | '.join(source_logs)
reviewed_dois = load_reviewed_dois()
html = f"""
{log_html}
{len(sorted_results)} unique publications retrieved ({window_label} · deduplicated)
{badge_row}
{log_html}
{title_html}
"""
html += "
{src_badge}
Authors: {authors}
{'•' + journal + '' if journal else ''}
{date}
{reviewed_badge}
{doi_link}
No results on this page.
")
reviewed_dois = load_reviewed_dois()
html = ""
for r in items:
src_badge = _render_source_badge(r['source'], r['source_color'], r['source_bg'])
title = r.get('title', 'Untitled')
url = r.get('url', '')
authors = r.get('authors', 'Unknown Authors')
journal = r.get('journal', '')
date = r.get('date', 'N/A')
doi = _extract_doi(r.get('doi', ''))
reviewed_badge = (
' Read'
if doi and doi in reviewed_dois else ''
)
doi_link = (
f'DOI: {doi}'
if doi else ''
)
title_html = (
f'{title}'
if url else
f'{title}'
)
html += f"""
{title_html}
"""
return html
def _ls_render_nav(state):
page = state['page']
pool = state['pool']
exhausted = state['exhausted']
sources = state['sources']
total_pool = len(pool)
max_known_page = max(1, (total_pool + LS_PAGE_SIZE - 1) // LS_PAGE_SIZE)
all_exhausted = all(s in exhausted for s in sources) if sources else True
has_more = not all_exhausted
pages_to_show = list(range(1, min(max_known_page, 10) + 1))
show_ellipsis = (max_known_page > 10) or has_more
btns = ""
for p in pages_to_show:
if p == page:
btns += (f'{p}')
else:
btns += (f'{p}')
if show_ellipsis:
btns += ('…')
src_counts = {}
for r in pool:
s = r.get('source', '')
src_counts[s] = src_counts.get(s, 0) + 1
badges = ' '.join(
f'{s}: {c}'
for s, c in src_counts.items()
)
status_line = (
f'
{src_badge}
Authors: {authors}
{'•' + journal + '' if journal else ''}
{date}
{reviewed_badge}
{doi_link}
'
f'Page {page} · {total_pool} results loaded'
+ (' · fetching more…' if has_more else ' · all sources loaded')
+ f'
'
)
return f"""
{status_line}
"""
def _ls_empty_state():
return {
'topic': '', 'sources': [], 'start_year': LS_YEAR_MIN, 'end_year': LS_YEAR_MAX,
'page': 1, 'pool': [], 'offsets': {}, 'exhausted': [],
'seen_dois': [], 'seen_titles': [],
}
def ls_init_search(query, sources, start_year, end_year):
start_year = int(start_year) if start_year else LS_YEAR_MIN
end_year = int(end_year) if end_year else LS_YEAR_MAX
if not query or not query.strip():
err = ("
{btns}
{badges}
"
" Please enter a valid search query.
")
return err, "", _ls_empty_state()
# If boolean query, restrict to PubMed
if _is_boolean_query(query):
sources = ['PubMed']
state = {
'topic': query.strip(), 'sources': list(sources),
'start_year': start_year, 'end_year': end_year,
'page': 1, 'pool': [], 'offsets': {}, 'exhausted': [],
'seen_dois': [], 'seen_titles': [],
}
_ls_fetch_batch(state)
if len(state['pool']) < LS_PAGE_SIZE:
_ls_fetch_batch(state)
if not state['pool']:
empty_html = (
""
" No papers found for “"
+ query +
"” in the selected year range across chosen databases.
"
)
return empty_html, "", state
return _ls_render_page(state), _ls_render_nav(state), state
def ls_prev_page(state):
if not state or state['page'] <= 1:
return _ls_render_page(state), _ls_render_nav(state), state
state = {**state, 'page': state['page'] - 1}
return _ls_render_page(state), _ls_render_nav(state), state
def ls_next_page(state):
if not state:
return "", "", state
state = dict(state)
next_page = state['page'] + 1
needed = next_page * LS_PAGE_SIZE
max_batches = 6
batches_done = 0
all_exhausted = lambda: all(s in state['exhausted'] for s in state['sources'])
while len(state['pool']) < needed and not all_exhausted() and batches_done < max_batches:
_ls_fetch_batch(state)
batches_done += 1
if len(state['pool']) >= (next_page - 1) * LS_PAGE_SIZE + 1:
state['page'] = next_page
return _ls_render_page(state), _ls_render_nav(state), state
# ── Database functions ────────────────────────────────────────────────────────
def search_supabase(species, tissue, tool, accession=""):
if not supabase:
return pd.DataFrame({"Error": ["Supabase keys are missing. Connection failed."]})
try:
table_name = "sra_database" if tool == "SRA" else "geo_database"
response = supabase.table(table_name).select("*").execute()
raw_data = response.data
if not raw_data:
return pd.DataFrame({"Status": [f"No records found in {table_name}."]})
df = pd.DataFrame(raw_data)
if 'created_at' in df.columns:
df = df.drop(columns=['created_at'])
if accession and accession.strip():
if "Accession" in df.columns:
df = df[df["Accession"].astype(str).str.lower().str.strip() == accession.lower().strip()]
if df.empty:
return pd.DataFrame({"Status": [f"No matching record for Accession '{accession}'."]})
else:
if "Organism" in df.columns:
df = df[df["Organism"].apply(normalize_organism) == normalize_organism(species)]
if "tissue" in df.columns:
df = df[df["tissue"].astype(str).str.lower().str.strip() == tissue.lower().strip()]
if df.empty:
return pd.DataFrame({"Status": [f"No records for '{species}' in {tool}."]})
display_cols = [c for c in ALL_UNIQUE_COLUMNS if c in df.columns]
return df[display_cols].fillna("N/A")
except Exception as e:
return pd.DataFrame({"Database Error": [f"Failed to fetch data: {str(e)}"]})
def load_entire_table():
if not supabase:
return pd.DataFrame({"Error": ["Supabase connection is inactive."]})
try:
sra_res = supabase.table("sra_database").select("*").execute()
df_sra = pd.DataFrame(sra_res.data) if sra_res.data else pd.DataFrame()
if 'created_at' in df_sra.columns:
df_sra = df_sra.drop(columns=['created_at'])
geo_res = supabase.table("geo_database").select("*").execute()
df_geo = pd.DataFrame(geo_res.data) if geo_res.data else pd.DataFrame()
if 'created_at' in df_geo.columns:
df_geo = df_geo.drop(columns=['created_at'])
if df_sra.empty and df_geo.empty:
return pd.DataFrame(columns=ALL_UNIQUE_COLUMNS)
combined_df = pd.concat([df_sra, df_geo], ignore_index=True)
ordered_cols = [c for c in ALL_UNIQUE_COLUMNS if c in combined_df.columns]
return combined_df[ordered_cols].fillna("N/A")
except Exception as e:
return pd.DataFrame({"Error": [str(e)]})
def generate_tools_launchpad_html(dataset_type="GEO"):
tools = SRA_TOOLS_DATA if dataset_type == "SRA" else GEO_TOOLS_DATA
icon = "" if dataset_type == "SRA" else ""
label = "SRA Download & QC" if dataset_type == "SRA" else "GEO Analytical"
accent = "#0891b2" if dataset_type == "SRA" else "#2563eb"
html = f"""{icon} Biological Data Located. Launch a {label} Suite below:
"""
for tool in tools:
html += f"""
Go to Tool
"""
html += "
{tool['Name']}
{tool['Type']}
{tool['Feature']}
{tools_launchpad}
")
def check_upload_credentials_generator(username, password, request: gr.Request):
"""Authenticate either the junior or senior admin and expose only their allowed UI."""
global _admin_active
session_hash = getattr(request, "session_hash", None)
junior_configured = bool(AUTH_USER and AUTH_PASS)
senior_configured = bool(SENIOR_ADMIN_USER and SENIOR_ADMIN_PASS)
if not junior_configured and not senior_configured:
_admin_active = False
if session_hash:
_admin_sessions.pop(session_hash, None)
yield (
gr.Column(visible=True), gr.Column(visible=False),
"Configuration Error: No admin credentials are configured.",
BLANK_TEMPLATE_DF, gr.Column(visible=True), gr.Column(visible=False),
gr.HTML(value=generate_editor_html()), False,
gr.Column(visible=False), gr.Column(visible=False),
get_reviewed_table_df(), get_pubcrawler_terms_df(),
get_pubcrawler_articles_html(request), gr.TabItem(visible=False),
gr.Column(visible=False)
)
return
role = None
if senior_configured and username == SENIOR_ADMIN_USER and password == SENIOR_ADMIN_PASS:
role = "senior"
elif junior_configured and username == AUTH_USER and password == AUTH_PASS:
role = "junior"
if role:
_admin_active = True
if session_hash:
_admin_sessions[session_hash] = role
if role == "senior":
# Senior: full console, homepage editor, review history and PubCrawler.
yield (
gr.Column(visible=False), gr.Column(visible=True),
"Senior admin authenticated. Loading administration workspace...",
BLANK_TEMPLATE_DF, gr.Column(visible=False), gr.Column(visible=True),
gr.HTML(value=generate_editor_html()), True,
gr.Column(visible=True), gr.Column(visible=True),
get_reviewed_table_df(), get_pubcrawler_terms_df(),
get_pubcrawler_articles_html(request), gr.TabItem(visible=True),
gr.Column(visible=False)
)
entire_table_df = load_entire_table()
yield (
gr.Column(visible=False), gr.Column(visible=True),
"Senior admin workspace ready.",
entire_table_df, gr.Column(visible=False), gr.Column(visible=True),
gr.HTML(value=generate_editor_html()), True,
gr.Column(visible=True), gr.Column(visible=True),
get_reviewed_table_df(), get_pubcrawler_terms_df(),
get_pubcrawler_articles_html(request), gr.TabItem(visible=True),
gr.Column(visible=False)
)
else:
# Junior: DOI mark-as-read controls only. No data upload/delete, homepage,
# reviewed-history management, or PubCrawler administration.
yield (
gr.Column(visible=False), gr.Column(visible=False),
"Junior admin authenticated. DOI review tools are enabled in the Literature tabs.",
BLANK_TEMPLATE_DF, gr.Column(visible=True), gr.Column(visible=False),
gr.HTML(value=generate_view_html()), False,
gr.Column(visible=True), gr.Column(visible=True),
get_reviewed_table_df(), get_pubcrawler_terms_df(),
get_pubcrawler_articles_html(request), gr.TabItem(visible=False),
gr.Column(visible=True)
)
else:
_admin_active = False
if session_hash:
_admin_sessions.pop(session_hash, None)
yield (
gr.Column(visible=True), gr.Column(visible=False),
"Invalid credentials. Access denied.",
BLANK_TEMPLATE_DF, gr.Column(visible=True), gr.Column(visible=False),
gr.HTML(value=generate_view_html()), False,
gr.Column(visible=False), gr.Column(visible=False),
get_reviewed_table_df(), get_pubcrawler_terms_df(),
get_pubcrawler_articles_html(request), gr.TabItem(visible=False),
gr.Column(visible=False)
)
def upload_data_to_supabase(file_obj, species, tissue, dataset_type, request: gr.Request):
if not request_has_role(request, "senior"):
return "Senior admin access required."
if not supabase:
return "Configuration Error: Supabase connection is inactive."
if file_obj is None:
return "Please upload a valid CSV or Excel file first."
try:
if file_obj.name.endswith('.csv'):
uploaded_df = pd.read_csv(file_obj.name)
elif file_obj.name.endswith(('.xls', '.xlsx')):
uploaded_df = pd.read_excel(file_obj.name)
else:
return "Unsupported format. Please upload a .csv or .xlsx file."
uploaded_df.columns = [c.strip() for c in uploaded_df.columns]
table_name = "sra_database" if dataset_type == "SRA" else "geo_database"
for target_col in EXACT_MATCH_COLUMNS:
if target_col not in uploaded_df.columns:
uploaded_df[target_col] = None
db_ready_df = uploaded_df[EXACT_MATCH_COLUMNS].copy().dropna(subset=["Accession"])
canonical_organism = "Bos taurus" if normalize_organism(species) == "bos taurus" else species.strip()
db_ready_df["Organism"] = canonical_organism
db_ready_df["tissue"] = tissue.strip()
db_ready_df["dataset"] = dataset_type.strip()
db_ready_df = sanitize_dataframe(db_ready_df)
new_records = db_ready_df.to_dict(orient="records")
if not new_records:
return "No rows with valid Accession numbers found."
db_response = supabase.table(table_name).select("Accession,Organism,tissue").execute()
existing = db_response.data if db_response.data else []
exact_keys = set()
accession_only = {}
for r in existing:
acc = normalize_val(r.get("Accession"))
org = normalize_organism(r.get("Organism", ""))
tis = normalize_val(r.get("tissue", ""))
exact_keys.add((acc, org, tis))
if acc not in accession_only:
accession_only[acc] = []
accession_only[acc].append((r.get("Organism", ""), r.get("tissue", "")))
incoming_org_norm = normalize_organism(canonical_organism)
incoming_tis_norm = normalize_val(tissue)
true_dupes = []
cross_category_warnings = []
final_records = []
for r in new_records:
acc = normalize_val(r.get("Accession"))
key = (acc, incoming_org_norm, incoming_tis_norm)
if key in exact_keys:
true_dupes.append(acc)
else:
if acc in accession_only:
for (ex_org, ex_tis) in accession_only[acc]:
cross_category_warnings.append(
f" • {acc} already exists as Organism='{ex_org}', Tissue='{ex_tis}'"
)
final_records.append(r)
msg_parts = []
if cross_category_warnings:
warn_lines = "\n".join(cross_category_warnings)
msg_parts.append(
f" Warning — are you sure you inputted the right parameters?\n"
f"The following accessions already exist in a different category:\n{warn_lines}"
)
if true_dupes:
msg_parts.append(f"Skipped {len(true_dupes)} exact duplicate(s) (same accession + organism + tissue).")
if not final_records:
msg_parts.append("No new records to insert after duplicate check.")
return "\n".join(msg_parts)
supabase.table(table_name).insert(final_records).execute()
msg_parts.append(f" Inserted {len(final_records)} record(s) into {table_name}.")
return "\n".join(msg_parts)
except Exception as e:
return f"Upload failed: {str(e)}"
def handle_row_selection(evt: gr.SelectData, current_df):
try:
row_idx = evt.index[0]
if "Accession" in current_df.columns:
accession_id = str(current_df.iloc[row_idx].get("Accession", "")).strip()
if accession_id and accession_id != "N/A":
return accession_id, f"Selected: {accession_id}"
return "", "Selected row has no valid Accession ID."
except Exception as e:
return "", f"Selection error: {str(e)}"
def delete_record_from_supabase(accession_id, current_df, request: gr.Request):
if not request_has_role(request, "senior"):
return "Senior admin access required.", current_df, accession_id
if not supabase or not accession_id.strip() or accession_id == "N/A":
return "No valid record selected.", current_df, accession_id
try:
response = supabase.table("sra_database").delete().eq("Accession", accession_id.strip()).execute()
if not response.data:
response = supabase.table("geo_database").delete().eq("Accession", accession_id.strip()).execute()
if response.data:
return f"Deleted: {accession_id}", load_entire_table(), ""
return f"Record {accession_id} not found.", current_df, accession_id
except Exception as e:
return f"Deletion error: {str(e)}", current_df, accession_id
def _df_to_html_table(df):
if df is None or df.empty:
return "No records found.
" cols = list(df.columns) header = "".join(f'Welcome to ReproOmics Hub
Integrated Bioinformatics Platform for Animal Reproduction Research
ReproOmics Hub is an integrated bioinformatics platform developed to support research in Animal Reproductive Technologies (ART) with a primary focus on cattle and buffalo. The portal provides seamless access to public omics datasets, literature resources, AI-assisted analytical tools, and bioinformatics workflows to accelerate research in reproductive biology, embryo development, fertility, and livestock improvement.
About ReproOmics Hub
The portal integrates biological databases, literature resources, and computational tools into a single platform for researchers working in:
Animal Reproduction Technologies (ART)
Embryo Development
Oocyte Biology
Sperm Biology
Fertility Research
Reproductive Genomics
Transcriptomics
Epigenomics
Single-cell RNA sequencing
Functional Genomics
Researchers can search public repositories such as GEO and SRA, explore reproductive omics datasets, perform downstream analyses, and access AI-assisted resources through a unified interface.
"
" Create a reviewed_articles (doi TEXT PRIMARY KEY, reviewed_at TIMESTAMPTZ DEFAULT NOW())"
" table in Supabase for persistent storage. Currently using session memory.
No articles marked yet.
" + note html = "" for r in rows: article_id = r.get('doi', '') at = (r.get('reviewed_at') or '')[:10] display = article_id[:80] + ('...' if len(article_id) > 80 else '') link = f"https://doi.org/{article_id}" if not article_id.startswith('http') else article_id html += ( f""
f""
f"{display}"
+ (f"{at}" if at else "") +
f"
"
)
for c in cache_only:
display = c[:80] + ('...' if len(c) > 80 else '')
html += (
f""
f"*"
f"{display}"
f"session"
f"
"
)
return html
def manual_mark_doi(doi, request: gr.Request):
"""Manually mark a DOI as read for the current authenticated Gradio session."""
if not doi or not doi.strip():
return " Please enter a valid DOI."
session_hash = getattr(request, "session_hash", None)
if not session_hash:
return " Could not determine your session. Please reload the page and log in again."
return mark_article_api(doi.strip(), "mark", session_hash)
def manual_mark_doi_and_refresh(doi, request: gr.Request):
if not request_has_role(request, "senior"):
return "Senior admin access required.", get_reviewed_table_df()
msg = manual_mark_doi(doi, request)
return msg, get_reviewed_table_df()
# ── Theme ─────────────────────────────────────────────────────────────────────
light_theme = gr.themes.Base(
primary_hue=gr.themes.colors.blue,
neutral_hue=gr.themes.colors.slate,
).set(
body_background_fill="#f3f6fa",
body_background_fill_dark="#f3f6fa",
body_text_color="#132238",
body_text_color_dark="#132238",
block_background_fill="#ffffff",
block_background_fill_dark="#ffffff",
block_border_color="#dbe3ed",
block_border_color_dark="#dbe3ed",
block_border_width="1px",
block_label_text_color="#475569",
block_label_text_color_dark="#475569",
block_label_text_weight="600",
block_label_text_size="13px",
block_title_text_color="#132238",
block_title_text_color_dark="#132238",
block_title_text_weight="700",
block_shadow="0 1px 2px rgba(15,23,42,.04)",
input_background_fill="#ffffff",
input_background_fill_dark="#ffffff",
input_border_color="#cfd9e5",
input_border_color_dark="#cfd9e5",
input_border_width="1px",
input_placeholder_color="#94a3b8",
input_placeholder_color_dark="#94a3b8",
input_shadow="none",
input_shadow_focus="0 0 0 3px rgba(37,99,235,.10)",
checkbox_background_color="#ffffff",
checkbox_background_color_dark="#ffffff",
checkbox_border_color="#cfd9e5",
checkbox_border_color_hover="#94a3b8",
checkbox_border_color_selected="#2563eb",
checkbox_background_color_selected="#2563eb",
button_primary_background_fill="#2563eb",
button_primary_background_fill_dark="#2563eb",
button_primary_background_fill_hover="#1d4ed8",
button_primary_text_color="#ffffff",
button_primary_text_color_dark="#ffffff",
button_primary_border_color="transparent",
button_secondary_background_fill="#ffffff",
button_secondary_background_fill_dark="#ffffff",
button_secondary_background_fill_hover="#f8fafc",
button_secondary_text_color="#334155",
button_secondary_text_color_dark="#334155",
button_secondary_border_color="#cfd9e5",
button_secondary_border_color_dark="#cfd9e5",
button_secondary_border_color_hover="#b8c6d8",
button_large_padding="10px 18px",
button_small_padding="6px 12px",
button_large_text_size="13px",
button_small_text_size="12px",
table_even_background_fill="#ffffff",
table_even_background_fill_dark="#ffffff",
table_odd_background_fill="#f8fafc",
table_odd_background_fill_dark="#f8fafc",
table_border_color="#dbe3ed",
table_border_color_dark="#dbe3ed",
border_color_primary="#dbe3ed",
color_accent="#2563eb",
color_accent_soft="#eff6ff",
)
css = r"""
:root {
--bg: #eef2f7;
--bg-deep: #e9eef5;
--surface: rgba(255,255,255,.96);
--surface-solid: #ffffff;
--surface-soft: #f7f9fc;
--surface-tint: #f3f7ff;
--line: #dde4ee;
--line-strong: #cbd6e4;
--text: #122033;
--text-2: #334155;
--muted: #66758a;
--muted-2: #94a3b8;
--accent: #2563eb;
--accent-2: #4f46e5;
--accent-soft: #eef4ff;
--success: #16803a;
--success-soft: #edf9f1;
--danger: #b42318;
--danger-soft: #fff1f1;
--warning-soft: #fff8e8;
--radius-sm: 10px;
--radius-md: 14px;
--radius-lg: 18px;
--shadow-xs: 0 1px 2px rgba(15,23,42,.04);
--shadow-sm: 0 2px 8px rgba(15,23,42,.05), 0 1px 2px rgba(15,23,42,.04);
--shadow-md: 0 12px 30px rgba(15,23,42,.07), 0 2px 8px rgba(15,23,42,.04);
--shadow-lg: 0 22px 50px rgba(15,23,42,.10), 0 4px 14px rgba(15,23,42,.05);
}
*, *::before, *::after { box-sizing: border-box; }
html, body { margin: 0 !important; background: var(--bg) !important; }
body {
color: var(--text) !important;
font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif !important;
background:
radial-gradient(circle at 10% 0%, rgba(79,70,229,.05), transparent 32%),
radial-gradient(circle at 92% 5%, rgba(37,99,235,.05), transparent 28%),
var(--bg) !important;
}
.gradio-container {
max-width: 1540px !important;
margin: 0 auto !important;
padding: 20px 30px 48px !important;
background: transparent !important;
}
.gradio-container h1,
.gradio-container h2,
.gradio-container h3,
.gradio-container h4,
.gradio-container strong,
.gradio-container b {
color: var(--text) !important;
}
.gradio-container p,
.gradio-container label,
.gradio-container .block label span {
color: var(--muted) !important;
}
/* App header */
.app-header {
position: relative;
overflow: hidden;
margin: 0 0 18px;
padding: 20px 22px;
border: 1px solid rgba(203,214,228,.9);
border-radius: 20px;
background:
linear-gradient(135deg, rgba(255,255,255,.99) 0%, rgba(246,249,255,.98) 62%, rgba(243,247,255,.98) 100%);
box-shadow: var(--shadow-md);
}
.app-header::after {
content: "";
position: absolute;
left: 22px; right: 22px; bottom: 0;
height: 2px;
background: linear-gradient(90deg, transparent, #7aa2ff, #8b7cf6, transparent);
opacity: .6;
}
.app-header-row { display:flex; align-items:center; justify-content:space-between; gap:22px; }
.app-brand { display:flex; align-items:center; gap:14px; min-width:0; }
.app-mark {
width: 48px; height: 48px; flex: 0 0 48px;
display:flex; align-items:center; justify-content:center;
border-radius:14px;
color:#fff; font-size:16px; font-weight:800; letter-spacing:.02em;
background: linear-gradient(135deg, #2563eb 0%, #4f46e5 100%);
border: 1px solid rgba(37,99,235,.25);
box-shadow: 0 8px 18px rgba(37,99,235,.20);
}
.app-title { margin:0; font-size:21px; font-weight:850; letter-spacing:-.035em; }
.app-subtitle { margin:4px 0 0; font-size:12px; color:var(--muted); }
.app-meta { display:flex; align-items:center; gap:7px; flex-wrap:wrap; justify-content:flex-end; }
.app-meta-chip {
border:1px solid #d9e1ec;
background:rgba(255,255,255,.82);
color:#475569;
padding:6px 10px;
border-radius:999px;
font-size:10.5px;
font-weight:700;
letter-spacing:.01em;
box-shadow: var(--shadow-xs);
}
/* Navigation */
.tabs { gap: 10px !important; }
.tab-nav {
position: sticky;
top: 10px;
z-index: 20;
display:flex !important;
gap:6px !important;
width:fit-content;
max-width:100%;
margin:0 auto 22px !important;
padding:6px !important;
border:1px solid rgba(203,214,228,.92) !important;
border-radius:14px !important;
background:rgba(255,255,255,.90) !important;
box-shadow:0 10px 28px rgba(15,23,42,.08) !important;
backdrop-filter:blur(14px);
}
.tab-nav button {
border:1px solid transparent !important;
background:transparent !important;
color:#64748b !important;
border-radius:9px !important;
padding:9px 13px !important;
font-size:11.5px !important;
font-weight:750 !important;
transition:background .18s ease, color .18s ease, box-shadow .18s ease, transform .18s ease !important;
}
.tab-nav button:hover {
background:#f4f7fb !important;
color:#1e40af !important;
transform:translateY(-1px);
}
.tab-nav button.selected {
color:#173b9e !important;
background:linear-gradient(180deg,#eef4ff,#e7efff) !important;
border-color:#c9d8fb !important;
box-shadow:0 3px 10px rgba(37,99,235,.10) !important;
}
/* Base component surfaces */
.gradio-container .block,
.gradio-container .form,
.gradio-container .panel,
.gradio-container [data-testid="block-wrap"] {
border-color: transparent !important;
box-shadow: none !important;
background: transparent !important;
}
.gradio-container .block:hover,
.gradio-container .form:hover,
.gradio-container .panel:hover,
.gradio-container [data-testid="block-wrap"]:hover {
box-shadow:none !important;
background:transparent !important;
}
.clean-card,
.admin-section {
background:var(--surface-solid);
border:1px solid var(--line);
border-radius:var(--radius-md);
box-shadow:var(--shadow-sm);
}
.clean-card { padding:18px; }
.clean-section-title { margin:0 0 6px; font-size:15px; font-weight:800; letter-spacing:-.015em; }
.clean-section-note { margin:0; font-size:12px; color:var(--muted); line-height:1.65; }
.clean-kicker {
margin-bottom:7px;
color:#64748b;
font-size:9.5px;
font-weight:850;
text-transform:uppercase;
letter-spacing:.13em;
}
.home-hero {
position:relative;
overflow:hidden;
padding:28px 28px;
border:1px solid #d7e2f0;
border-radius:20px;
background:
radial-gradient(circle at 100% 0%, rgba(79,70,229,.07), transparent 30%),
linear-gradient(140deg,#ffffff,#f7faff);
box-shadow:var(--shadow-md);
}
.home-hero::before {
content:"";
position:absolute;
width:180px; height:180px;
right:-70px; top:-75px;
border-radius:50%;
background:rgba(37,99,235,.05);
}
/* Form controls */
.gradio-container input,
.gradio-container textarea,
.gradio-container select,
.gradio-container .wrap input,
.gradio-container .wrap textarea {
background:#fff !important;
border:1px solid var(--line-strong) !important;
border-radius:10px !important;
box-shadow:none !important;
color:var(--text) !important;
font-size:13px !important;
transition:border-color .16s ease, box-shadow .16s ease, background .16s ease !important;
}
.gradio-container input:hover,
.gradio-container textarea:hover,
.gradio-container select:hover {
border-color:#bac7d7 !important;
}
.gradio-container input:focus,
.gradio-container textarea:focus,
.gradio-container select:focus {
border-color:#78a0ee !important;
box-shadow:0 0 0 3px rgba(37,99,235,.09) !important;
outline:none !important;
}
/* The component wrapper stays visually neutral; only the actual control responds. */
.gradio-container .block:has(input):hover,
.gradio-container .block:has(textarea):hover,
.gradio-container .block:has(select):hover {
background:transparent !important;
box-shadow:none !important;
border-color:transparent !important;
}
/* Checkbox / radio groups */
.gradio-container .checkbox-group .wrap,
.gradio-container .radio-group .wrap { gap:8px !important; }
.gradio-container .checkbox-group label,
.gradio-container .radio-group label {
min-height:36px;
border:1px solid var(--line-strong) !important;
background:#fff !important;
border-radius:10px !important;
padding:8px 11px !important;
color:#475569 !important;
transition:border-color .15s ease, background .15s ease, transform .15s ease !important;
}
.gradio-container .checkbox-group label:hover,
.gradio-container .radio-group label:hover {
background:#f8fbff !important;
border-color:#b8c6d9 !important;
transform:translateY(-1px);
}
.gradio-container .checkbox-group label.selected,
.gradio-container .radio-group label.selected {
background:linear-gradient(180deg,#f0f5ff,#eaf1ff) !important;
border-color:#94b1eb !important;
color:#17408f !important;
}
/* Buttons */
.gradio-container button {
min-height:40px !important;
border-radius:10px !important;
box-shadow:none !important;
font-size:12px !important;
font-weight:760 !important;
letter-spacing:.005em !important;
transition:transform .16s ease, box-shadow .16s ease, background .16s ease, border-color .16s ease !important;
}
.gradio-container button.primary {
color:#fff !important;
background:linear-gradient(135deg,#2563eb,#315bdc) !important;
border:1px solid #2563eb !important;
box-shadow:0 7px 16px rgba(37,99,235,.18) !important;
}
.gradio-container button.primary:hover {
background:linear-gradient(135deg,#1d4ed8,#4338ca) !important;
transform:translateY(-1px);
box-shadow:0 10px 20px rgba(37,99,235,.22) !important;
}
.gradio-container button.secondary {
color:#334155 !important;
background:#fff !important;
border:1px solid var(--line-strong) !important;
}
.gradio-container button.secondary:hover {
background:#f8fafc !important;
border-color:#b8c6d7 !important;
transform:translateY(-1px);
box-shadow:0 5px 12px rgba(15,23,42,.06) !important;
}
.gradio-container button.stop {
color:#b42318 !important;
background:#fff !important;
border:1px solid #f0c9c6 !important;
}
.gradio-container button.stop:hover {
background:#fff7f6 !important;
border-color:#e7aaa5 !important;
}
/* Dataframes */
.gradio-container .table-wrap,
.gradio-container .dataframe {
overflow:hidden !important;
border:1px solid var(--line) !important;
border-radius:12px !important;
background:#fff !important;
box-shadow:var(--shadow-xs) !important;
}
.gradio-container .dataframe table { font-size:12px !important; }
.gradio-container .dataframe th {
padding:10px 12px !important;
background:#f6f8fb !important;
color:#475569 !important;
font-weight:750 !important;
border-bottom:1px solid var(--line) !important;
}
.gradio-container .dataframe td {
padding:9px 12px !important;
color:#334155 !important;
border-color:#edf1f5 !important;
}
.gradio-container .dataframe tr:hover td { background:#f8fbff !important; }
/* Result area */
.results-shell { display:flex; flex-direction:column; gap:12px; }
.results-summary {
padding:14px 16px;
border:1px solid #d9e3ef;
border-radius:12px;
background:linear-gradient(135deg,#ffffff,#f8fbff);
box-shadow:var(--shadow-xs);
}
.results-summary-title { font-size:13px; font-weight:800; color:var(--text); }
.results-summary-note { font-size:11px; color:var(--muted); margin-top:3px; }
.source-pills { display:flex; gap:6px; flex-wrap:wrap; }
.source-pill {
padding:4px 8px;
border-radius:999px;
background:#f4f7fb;
border:1px solid #dfe6ee;
font-size:10px;
color:#475569;
font-weight:700;
}
/* AI tools */
.ai-tools-section-title {
margin:28px 0 12px;
padding:0 0 9px;
border-bottom:1px solid var(--line);
color:#334155 !important;
font-size:11px !important;
font-weight:850 !important;
text-transform:uppercase;
letter-spacing:.12em;
}
.ai-tools-grid {
display:grid;
grid-template-columns:repeat(auto-fit,minmax(280px,1fr));
gap:15px;
}
.ai-tool-card {
display:flex;
flex-direction:column;
gap:9px;
padding:17px 18px;
background:#fff !important;
border:1px solid var(--line) !important;
border-radius:14px !important;
text-decoration:none !important;
color:inherit !important;
box-shadow:var(--shadow-sm) !important;
transition:transform .18s ease, border-color .18s ease, box-shadow .18s ease !important;
}
.ai-tool-card:hover {
transform:translateY(-3px) !important;
border-color:#b9c9e0 !important;
box-shadow:var(--shadow-lg) !important;
}
.ai-tool-card .card-name { color:#1d4ed8 !important; font-size:13px; font-weight:780; line-height:1.4; }
.ai-tool-card .card-desc { color:#64748b !important; font-size:11.5px; line-height:1.62; flex:1; }
.ai-tool-card .card-footer { display:flex; flex-wrap:wrap; gap:6px; margin-top:3px; }
.card-badge { font-size:9.5px; font-weight:700; padding:3px 7px; border-radius:999px; }
.badge-type { background:#eef4ff !important; color:#1d4ed8 !important; border:1px solid #c5d7ff !important; }
.badge-spp { background:#edf9f1 !important; color:#166534 !important; border:1px solid #c9ebd2 !important; }
.badge-link { background:#f7f9fc !important; color:#64748b !important; border:1px solid #e2e7ef !important; margin-left:auto; }
/* PubCrawler */
.crawler-banner {
position:relative;
overflow:hidden;
border:1px solid #d2def1;
background:linear-gradient(135deg,#ffffff,#f3f7ff 72%,#f2efff 100%);
border-radius:18px;
padding:22px 22px;
margin-bottom:16px;
box-shadow:var(--shadow-md);
}
.crawler-banner::after {
content:"";
position:absolute;
width:160px; height:160px;
right:-55px; top:-70px;
border-radius:50%;
background:rgba(79,70,229,.05);
}
.crawler-banner h2 { margin:0 0 6px; font-size:20px; letter-spacing:-.025em; }
.crawler-banner p { margin:0; font-size:12px; max-width:820px; line-height:1.65; }
.crawler-stat { color:#1d4ed8; font-weight:800; }
.crawler-grid { align-items:stretch !important; }
/* Admin */
.admin-login {
max-width:570px;
margin:34px auto;
}
.admin-login > div {
background:linear-gradient(145deg,#ffffff,#f7faff) !important;
border:1px solid #d7e2ef !important;
border-radius:18px !important;
box-shadow:var(--shadow-lg) !important;
padding:20px !important;
}
.admin-dashboard-title { margin:0 0 4px; font-size:18px; font-weight:800; }
.admin-section { padding:17px; }
.admin-section + .admin-section { margin-top:13px; }
/* Homepage editor */
#homepage-canvas-editor {
background:#fbfcfe !important;
border:1px solid #d6dee9 !important;
border-radius:12px !important;
}
/* Reduce excessive default Gradio whitespace */
.gradio-container .gap { gap:12px !important; }
.gradio-container .form { gap:10px !important; }
/* Mobile */
@media (max-width: 1100px) {
.gradio-container { padding:14px 16px 34px !important; }
.app-header-row { align-items:flex-start; flex-direction:column; }
.app-meta { justify-content:flex-start; }
.tab-nav {
width:100%;
overflow-x:auto;
justify-content:flex-start;
margin-left:0 !important;
margin-right:0 !important;
}
.tab-nav button { flex:0 0 auto; }
}
@media (prefers-reduced-motion: reduce) {
*, *::before, *::after { transition:none !important; animation:none !important; }
}
/* ── Visibility and contrast refinement ─────────────────────────────────── */
:root {
--bg: #f4f7fb;
--surface: #ffffff;
--surface-solid: #ffffff;
--surface-soft: #f8fafc;
--surface-tint: #eef4ff;
--line: #cfd8e3;
--line-strong: #aebccc;
--text: #0f172a;
--text-2: #334155;
--muted: #475569;
--muted-2: #64748b;
}
/* Stronger baseline typography and clearer section hierarchy. */
.gradio-container,
.gradio-container p,
.gradio-container label,
.gradio-container .block label span,
.gradio-container .prose,
.gradio-container .markdown-body {
opacity: 1 !important;
}
.gradio-container p,
.gradio-container .clean-section-note,
.gradio-container .clean-kicker {
color: #475569 !important;
}
.gradio-container .clean-section-title,
.gradio-container h1,
.gradio-container h2,
.gradio-container h3,
.gradio-container h4,
.gradio-container strong,
.gradio-container b {
color: #0f172a !important;
}
.gradio-container .clean-kicker {
font-size: 10px !important;
font-weight: 850 !important;
letter-spacing: .14em !important;
}
/* Make form labels unmistakable. */
.gradio-container label,
.gradio-container .block label,
.gradio-container .block label span,
.gradio-container [data-testid="block-label"],
.gradio-container [data-testid="block-label"] span {
color: #1e293b !important;
font-weight: 700 !important;
font-size: 12px !important;
}
/* Clearer inputs and placeholders. */
.gradio-container input,
.gradio-container textarea,
.gradio-container select,
.gradio-container .wrap input,
.gradio-container .wrap textarea {
border-color: #aebccc !important;
color: #0f172a !important;
background: #ffffff !important;
}
.gradio-container input::placeholder,
.gradio-container textarea::placeholder {
color: #64748b !important;
opacity: 1 !important;
}
.gradio-container input:hover,
.gradio-container textarea:hover,
.gradio-container select:hover {
border-color: #7f91a6 !important;
}
.gradio-container input:focus,
.gradio-container textarea:focus,
.gradio-container select:focus {
border-color: #2563eb !important;
box-shadow: 0 0 0 3px rgba(37,99,235,.12) !important;
}
/* Stronger radio/checkbox readability without adding visual clutter. */
.gradio-container .checkbox-group label,
.gradio-container .radio-group label {
border-color: #c1ccd8 !important;
color: #334155 !important;
background: #ffffff !important;
}
.gradio-container .checkbox-group label:hover,
.gradio-container .radio-group label:hover {
background: #f3f7ff !important;
border-color: #8ea4c0 !important;
}
.gradio-container .checkbox-group label.selected,
.gradio-container .radio-group label.selected {
color: #163b86 !important;
background: #eaf1ff !important;
border-color: #6f93d7 !important;
}
/* More legible navigation. */
.tab-nav {
border-color: #b9c6d6 !important;
background: rgba(255,255,255,.97) !important;
box-shadow: 0 8px 22px rgba(15,23,42,.10) !important;
}
.tab-nav button {
color: #475569 !important;
font-size: 12px !important;
font-weight: 800 !important;
}
.tab-nav button:hover {
background: #eef3f9 !important;
color: #173b7a !important;
}
.tab-nav button.selected {
color: #123b86 !important;
background: #e7efff !important;
border-color: #b3c8ee !important;
}
/* Buttons: stronger text and borders so actions remain obvious. */
.gradio-container button.secondary {
color: #1e293b !important;
border-color: #aebccc !important;
}
.gradio-container button.secondary:hover {
color: #0f172a !important;
background: #f1f5f9 !important;
border-color: #8194aa !important;
}
.gradio-container button.primary {
min-height: 42px !important;
font-size: 12px !important;
font-weight: 800 !important;
}
/* Tables need stronger header/body contrast. */
.gradio-container .dataframe th {
background: #edf2f7 !important;
color: #1e293b !important;
border-bottom-color: #c5d0dc !important;
}
.gradio-container .dataframe td {
color: #334155 !important;
border-color: #e1e7ee !important;
}
.gradio-container .dataframe tr:hover td {
background: #f3f7fb !important;
}
/* Give custom HTML notes/cards enough contrast against the page background. */
.clean-card,
.admin-section,
.results-summary,
.ai-tool-card {
border-color: #cbd5e1 !important;
}
.clean-section-note {
font-size: 12.5px !important;
}
/* Remove accidental low-opacity text inherited from the theme. */
.gradio-container [style*="color:#94a3b8"],
.gradio-container [style*="color: #94a3b8"] {
color: #64748b !important;
}
/* ── Larger type and denser layout refinement ───────────────────────────── */
.gradio-container {
max-width: 1600px !important;
padding: 12px 18px 28px !important;
}
.gradio-container .row,
.gradio-container [data-testid="row"] {
gap: 16px !important;
}
.gradio-container [data-testid="column"] {
min-height: 0 !important;
}
.app-header {
margin-bottom: 12px !important;
padding: 14px 18px !important;
border-radius: 16px !important;
}
.app-header-row { gap: 18px !important; }
.app-mark {
width: 52px !important;
height: 52px !important;
flex-basis: 52px !important;
}
.app-title { font-size: 26px !important; }
.app-subtitle { font-size: 14px !important; }
.app-meta-chip { font-size: 12px !important; padding: 7px 11px !important; }
.tab-nav {
margin-bottom: 14px !important;
padding: 4px !important;
border-radius: 12px !important;
}
.tab-nav button {
min-height: 42px !important;
padding: 10px 15px !important;
font-size: 13.5px !important;
}
.clean-card { padding: 14px 16px !important; }
.clean-section-title { font-size: 18px !important; margin-bottom: 6px !important; }
.clean-section-note { font-size: 14px !important; line-height: 1.55 !important; }
.clean-kicker { font-size: 11px !important; margin-bottom: 5px !important; }
.home-hero {
padding: 20px 22px !important;
border-radius: 16px !important;
margin-bottom: 12px !important;
}
.gradio-container label,
.gradio-container .block label,
.gradio-container .block label span,
.gradio-container [data-testid="block-label"],
.gradio-container [data-testid="block-label"] span {
font-size: 13.5px !important;
line-height: 1.3 !important;
}
.gradio-container input,
.gradio-container textarea,
.gradio-container select,
.gradio-container .wrap input,
.gradio-container .wrap textarea {
min-height: 46px !important;
font-size: 15px !important;
line-height: 1.4 !important;
border-radius: 9px !important;
}
.gradio-container textarea { min-height: 100px !important; }
.gradio-container input::placeholder,
.gradio-container textarea::placeholder {
font-size: 14px !important;
}
.gradio-container .checkbox-group label,
.gradio-container .radio-group label {
min-height: 42px !important;
padding: 9px 12px !important;
font-size: 13.5px !important;
}
.gradio-container button {
min-height: 44px !important;
padding: 9px 15px !important;
font-size: 14px !important;
}
.gradio-container button.primary {
min-height: 44px !important;
font-size: 14px !important;
}
.gradio-container .dataframe table { font-size: 13.5px !important; }
.gradio-container .dataframe th {
padding: 11px 13px !important;
font-size: 13.5px !important;
}
.gradio-container .dataframe td {
padding: 10px 13px !important;
font-size: 13.5px !important;
}
.results-summary { padding: 13px 15px !important; }
.results-summary-title { font-size: 15px !important; }
.results-summary-note { font-size: 13px !important; }
.source-pill { font-size: 11.5px !important; padding: 5px 9px !important; }
/* Custom result cards use inline font sizes; raise them consistently. */
.art-result-card { padding: 16px 18px !important; }
.art-result-card a,
.art-result-card span,
.art-result-card div,
.art-result-card em {
font-size: 13.5px !important;
}
.art-result-card a {
font-size: 16px !important;
}
.ai-tools-grid {
grid-template-columns: repeat(auto-fit, minmax(310px, 1fr)) !important;
gap: 12px !important;
}
.ai-tool-card { padding: 15px 17px !important; }
.ai-tool-card .card-name { font-size: 15px !important; }
.ai-tool-card .card-desc { font-size: 13px !important; line-height: 1.55 !important; }
.card-badge { font-size: 11px !important; padding: 4px 8px !important; }
.crawler-banner {
padding: 18px 20px !important;
margin-bottom: 12px !important;
border-radius: 16px !important;
}
.crawler-banner h2 { font-size: 23px !important; }
.crawler-banner p { font-size: 14px !important; line-height: 1.55 !important; }
.admin-login { margin: 18px auto !important; }
.admin-section { padding: 14px !important; }
.admin-section + .admin-section { margin-top: 10px !important; }
/* Reduce oversized empty-state areas without removing the breathing room. */
.gradio-container .clean-card[style*="padding:70px"],
.gradio-container .clean-card[style*="padding: 70px"] {
padding: 36px 20px !important;
}
.gradio-container .clean-card[style*="padding:55px"],
.gradio-container .clean-card[style*="padding: 55px"] {
padding: 30px 20px !important;
}
@media (max-width: 1100px) {
.gradio-container { padding: 10px 12px 22px !important; }
.app-title { font-size: 22px !important; }
.app-subtitle { font-size: 13px !important; }
.tab-nav button { font-size: 13px !important; padding: 9px 12px !important; }
.gradio-container input,
.gradio-container textarea,
.gradio-container select { font-size: 14px !important; }
}
"""
# ── PubCrawler term management (admin-only) ──────────────────────────────────
def _pubcrawler_admin_ok(request: gr.Request):
return request_has_role(request, "senior")
def get_pubcrawler_terms_df():
"""Load the terms stored in the Supabase `pubcrawler` table."""
columns = ['ID', 'Term']
if not supabase:
return pd.DataFrame(columns=columns)
try:
res = (
supabase.table('pubcrawler')
.select('id,term')
.order('id', desc=True)
.execute()
)
rows = []
for row in (res.data or []):
rows.append({
'ID': row.get('id'),
'Term': row.get('term', ''),
})
return pd.DataFrame(rows, columns=columns)
except Exception:
return pd.DataFrame(columns=columns)
def save_pubcrawler_term(term, request: gr.Request):
"""Insert a crawler term for an authenticated admin."""
if not _pubcrawler_admin_ok(request):
return ' Admin login required. Please log in first.', get_pubcrawler_terms_df(), '', get_pubcrawler_articles_html(request)
term = ' '.join(str(term or '').split())
if not term:
return ' Enter a term before saving.', get_pubcrawler_terms_df(), '', get_pubcrawler_articles_html(request)
if not supabase:
return ' Supabase connection is unavailable.', get_pubcrawler_terms_df(), '', get_pubcrawler_articles_html(request)
try:
# Prevent accidental duplicate terms without requiring a unique DB constraint.
existing = (
supabase.table('pubcrawler')
.select('id,term')
.eq('term', term)
.limit(1)
.execute()
)
if existing.data:
return 'Note: That term is already in PubCrawler.', get_pubcrawler_terms_df(), term, get_pubcrawler_articles_html(request)
supabase.table('pubcrawler').insert({'term': term}).execute()
return f' Saved PubCrawler term: **{term}**', get_pubcrawler_terms_df(), '', get_pubcrawler_articles_html(request)
except Exception as e:
return f' Could not save term: {e}', get_pubcrawler_terms_df(), term, get_pubcrawler_articles_html(request)
def pubcrawler_row_selection(evt: gr.SelectData, current_df):
try:
row_idx = evt.index[0]
row = current_df.iloc[row_idx]
row_id = str(row.get('ID', '')).strip()
term = str(row.get('Term', '')).strip()
return row_id, f'Selected term: {term}' if term else 'No term selected.'
except Exception as e:
return '', f'Selection error: {e}'
def get_pubcrawler_articles_html(request: gr.Request):
"""Search every saved PubCrawler term across the literature sources for the past 30 days."""
if not _pubcrawler_admin_ok(request):
return " Admin login required. Please log in first.
"
if not supabase:
return " Supabase connection is unavailable.
"
terms_df = get_pubcrawler_terms_df()
terms = []
if not terms_df.empty:
terms = [str(x).strip() for x in terms_df['Term'].tolist() if str(x).strip()]
if not terms:
return " No PubCrawler terms have been saved yet.
"
since = datetime.date.today() - datetime.timedelta(days=30)
since_date = since.strftime('%Y/%m/%d')
since_dash = since.strftime('%Y-%m-%d')
all_results = []
term_logs = []
for term in terms:
source_map = {
'PubMed': lambda t=term: _fetch_pubmed(t, since_date, max_results=200),
'Europe PMC': lambda t=term: _fetch_europe_pmc(t, since_dash, max_results=200),
'CrossRef': lambda t=term: _fetch_crossref(t, since_dash, max_results=100),
'Semantic Scholar': lambda t=term: _fetch_semantic_scholar(t, since_dash, max_results=100),
'bioRxiv / medRxiv': lambda t=term: _fetch_biorxiv(t, since_dash, max_results=100),
}
for source_name, fetch_fn in source_map.items():
try:
results, log = fetch_fn()
for result in results:
result = dict(result)
result['_crawler_term'] = term
all_results.append(result)
if results:
term_logs.append(f"{term}: {source_name} {len(results)}")
except Exception as e:
term_logs.append(f"{term}: {source_name} error")
if not all_results:
return f"""
No publications matching the saved PubCrawler terms were found in the past 30 days.
"""
# Deduplicate while retaining all saved terms that matched each article.
merged = {}
for r in all_results:
doi = _extract_doi(r.get('doi', ''))
title_key = str(r.get('title', '')).strip().lower()
key = ('doi', doi.lower()) if doi else ('title', title_key[:140])
if key not in merged:
merged[key] = dict(r)
merged[key]['_crawler_terms'] = [r.get('_crawler_term', '')]
else:
hit = r.get('_crawler_term', '')
if hit and hit not in merged[key]['_crawler_terms']:
merged[key]['_crawler_terms'].append(hit)
if not merged[key].get('doi') and doi:
merged[key]['doi'] = doi
results = _sort_results(list(merged.values()))
reviewed_dois = load_reviewed_dois()
source_counts = {}
for r in results:
src = r.get('source', 'Unknown')
source_counts[src] = source_counts.get(src, 0) + 1
count_badges = ''.join(
f"{html_lib.escape(src)}: {cnt}"
for src, cnt in source_counts.items()
)
html = f"""
{len(results)} unique publications · past 30 days
{count_badges}
Searching {len(terms)} saved PubCrawler term(s); duplicates merged.
{title}
'
)
html += f"""
{title_html}
"""
html += "
{src}
{reviewed_badge}
{term_html}
{date}
Authors: {authors}
{f' • {journal}' if journal else ''}
{f'DOI: {doi_esc}
' if doi else ''}
भाकृअनुप-राष्ठ्रीय डेरी अनुसंधान संस्थान
ICAR – National Dairy Research Institute
KARNAL, HARYANA
ReproOmics Hub — Bioinformatics Hub for Animal Reproduction Research
RH
ReproOmics Hub
Research workspace for animal reproduction data, literature, and analysis
Site editor
Homepage Content
Edit the homepage content below. Changes are saved to the local homepage file.
Dataset search
Query Biological Records
Search your GEO and SRA records using animal, tissue, source, and accession filters.
How it works
Search and inspect stored records
Results appear in a structured table. After a match is found, the analysis launchpad becomes available below it.
Analysis
Available analysis tools
Launch the relevant GEO or SRA workflow for the returned record.
Literature discovery
Multi-Database Literature Search
Search the most recent six months across PubMed, Europe PMC, CrossRef, Semantic Scholar, and bioRxiv/medRxiv. Results are deduplicated and sorted by date.
Direct lookup
Search by DOI
Look up one DOI across CrossRef, Europe PMC, and PubMed.
Enter a topic or DOI to begin.
")
with gr.Column(scale=1, min_width=220, visible=False) as pm_doi_col:
with gr.Group():
gr.HTML("""
Admin action
Mark DOI as Read
Store a reviewed DOI in Supabase.
""") pm_doi_input = gr.Textbox(label="DOI", placeholder="10.1093/nar/gkac123") pm_doi_btn = gr.Button("Mark as Read", variant="primary") pm_doi_status = gr.Markdown("") pm_search_btn.click(fn=fetch_multi_db_papers, inputs=[pm_topic_input, pm_sources_input], outputs=pm_output_html) pm_doi_search_btn.click(fn=search_by_doi, inputs=[pm_doi_search_input], outputs=pm_output_html) def manual_mark_doi_only(doi, request: gr.Request): return manual_mark_doi(doi, request) pm_doi_btn.click(fn=manual_mark_doi_only, inputs=[pm_doi_input], outputs=pm_doi_status) # ── TAB 4: Literature Search ──────────────────────────────────────────── with gr.TabItem("Literature Search"): gr.HTML("""Full archive
Literature Search — All Time
Search by topic, database, and year range. Results are loaded page by page and deduplicated.
Direct lookup
Search by DOI
Open a publication directly from its DOI.
Enter a topic and click Search to begin.
")
with gr.Row():
ls_prev_btn = gr.Button("Previous", variant="secondary")
ls_next_btn = gr.Button("Next", variant="primary")
with gr.Column(scale=1, min_width=220, visible=False) as ls_doi_col:
with gr.Group():
gr.HTML("Admin action
Mark DOI as Read
Store a reviewed DOI in Supabase.
") ls_doi_input = gr.Textbox(label="DOI", placeholder="10.1093/nar/gkac123") ls_doi_btn = gr.Button("Mark as Read", variant="primary") ls_doi_status = gr.Markdown("") ls_search_btn.click(fn=ls_init_search, inputs=[ls_topic_input, ls_sources_input, ls_year_from, ls_year_to], outputs=[ls_output_html, ls_nav_html, ls_state]) ls_doi_search_btn.click(fn=search_by_doi, inputs=[ls_doi_search_input], outputs=ls_output_html) ls_prev_btn.click(fn=ls_prev_page, inputs=[ls_state], outputs=[ls_output_html, ls_nav_html, ls_state]) ls_next_btn.click(fn=ls_next_page, inputs=[ls_state], outputs=[ls_output_html, ls_nav_html, ls_state]) ls_doi_btn.click(fn=manual_mark_doi_only, inputs=[ls_doi_input], outputs=ls_doi_status) # ── TAB 5: AI Tools Directory ────────────────────────────────────────── with gr.TabItem("AI Tools"): gr.HTML("""AI / ML / DL Tools
Curated AI, Machine Learning & Deep Learning tools for cattle & buffalo reproduction research. Click any card to open the tool or source paper.
AI / ML / DL Tools for Cattle & Buffalo Reproduction
Click any card to open the source paper or tool.
Oocyte Assessment / Grading
DeepLabV3Plus + SqueezeNet Pipeline
Combines DeepLabV3Plus segmentation with a refined SqueezeNet classifier; 96% validation accuracy for oocyte meiotic-stage classification.
CNN-Based Oocyte Competence Scoring
Semi-automatic CNN-based system for bovine oocyte competence, labelling images post hoc by resulting blastocyst outcomes.
ML + Microfluidic Integration
Multiple supervised ML models (RF, XGBoost, SVM, LightGBM, KNN, Naïve Bayes, LR) evaluated for oocyte quality prediction integrated with microfluidic chip data.
Embryo / Blastocyst Grading
Blasto3Q
Fully automated ANN-based bovine blastocyst grading per IETS standard (Grade 1/2/3); 76.4% accuracy; MATLAB + multiplatform web interface.
iDAScore (Vitrolife)
Fully automated time-lapse analysis; ranks embryos by likelihood of fetal heartbeat at day 2/3/blastocyst stage. Widely cited in bovine ET literature.
EmbryoScope / Primo Vision / Eeva
Time-lapse incubation systems with integrated kinetics and cleavage-symmetry evaluation software; Primo Vision used to record bovine blastulation timing (tSB, tB).
ML Embryo Stage / Transferability Classifier
81.7% agreement with expert embryologists on developmental stage; 95.2% agreement on transferability decision.
Multi-Task DL + Dynamic Programming
2D CNN per time-lapse frame with dynamic-programming post-processing enforcing monotonic cell-count progression for embryo cell-stage classification.
Supervised Contrastive Learning Model
Benchmarked on public Bovine Embryo CS dataset and NYU Mouse Embryo dataset for cell-stage classification using contrastive CNN.
CNN Oocyte / Embryo Scoring (Mana et al.)
CNN-based scoring of 269 oocytes and 269 corresponding embryos from 104 women; preliminary high-quality embryo classification study.
ML Live-Birth Prediction (Miyagi et al.)
Multiple ML algorithms compared (LR, Naïve Bayes, KNN, RF, Neural Network, SVM) for predicting probability of live birth from blastocyst-stage images.
AI Pregnancy-Probability Platform (Khosravi et al.)
AI platform trained on embryologist-scored blastocyst images; predicted pregnancy chance 13.8–66.3% depending on blastocyst/patient factors.
Sperm / Semen Analysis (CASA + AI)
BGM — Open-Access CASA Software
Open-access CASA software validated against commercial Hamilton-Thorne (HTM) system for cattle and buffalo sperm motility/kinematics. Most directly relevant tool for cattle + buffalo work.
BSPMCsvm3casa
SVM-based bull sperm motility classifier using three CASA kinematic parameters (VCL, VSL, LIN); outperforms prior static-threshold classification methods.
Tracking-Grid + Mean-Angle Motion Tracker
Predicts position of sperm with failed detection using mean motion angle + Tracking-Grid; 5% fewer ID-switches and +15.6 MOTAL points vs Deep SORT baseline.
Hamilton-Thorne CASA (IVOS / HTM Series)
Industry-standard CASA combining camera, microscope, and pixel-recognition software for motility/velocity/morphology evaluation across cattle and buffalo.
DL Bovine Sperm Morphology Classifier (IFC)
Deep learning morphology analysis trained on ~1.8 million imaging flow cytometry images across 6 bulls, fresh and frozen-thawed semen.
AI Bull Sperm Morphology Evaluation System
AI algorithm benchmarked against manual morphology assessment in bull semen analysis; confirmed potential applicability for automated sperm morphology evaluation.
Animal Identification (Cattle / Buffalo)
Watch list
Add a search term
Terms are saved to Supabase and used for the next crawl.
") pubcrawler_term_input = gr.Textbox(label="Term", placeholder="e.g. bovine oocyte CRISPR") with gr.Row(): pubcrawler_save_btn = gr.Button("Save Term", variant="primary") pubcrawler_refresh_btn = gr.Button("Reload", variant="secondary") pubcrawler_status = gr.Markdown("") with gr.Column(scale=3, min_width=420): gr.HTML("Stored terms
PubCrawler Watch List
Select a row to remove that term.
") pubcrawler_table = gr.Dataframe(value=get_pubcrawler_terms_df(), headers=['ID', 'Term'], datatype=['number', 'str'], interactive=False, wrap=True, max_height=240) selected_pubcrawler_id = gr.State("") with gr.Row(): pubcrawler_delete_btn = gr.Button("Remove Selected Term", variant="stop") pubcrawler_selection_status = gr.Markdown("") gr.HTML("Literature feed
Articles Matching Saved Terms
Every saved term is searched across the literature sources for the past 30 days. Duplicates are merged.
Save one or more terms, then refresh the article feed.
")
# ── TAB 7: Administrative Portal ───────────────────────────────────────
with gr.TabItem("Administrative Portal"):
with gr.Column(visible=True, elem_classes=["admin-login"]) as login_panel:
gr.HTML("""
Restricted access
Administrative Portal
Sign in with a junior or senior administrator account. Junior access is limited to DOI review; senior access includes database management, PubCrawler, reviewed-article management, and homepage editing.
Junior administration
DOI review access
Your account can mark literature DOIs as read. The database administration console, homepage editor, PubCrawler, and reviewed-article deletion require senior administrator access.
Administration
Admin Console
Manage dataset records, reviewed DOIs, and site content from one workspace.
Data ingestion
Upload Records
Upload a CSV or Excel dataset and assign its biological metadata.
") file_input = gr.File(label="CSV or Excel", file_types=[".csv", ".xlsx"]) upload_species = gr.Dropdown(choices=["Bos taurus", "Buffalo"], label="Animal Type", value="Bos taurus") upload_tissue = gr.Dropdown(choices=["Oocyte", "Blastocyst", "Sperm"], label="Tissue Type", value="Oocyte") upload_dataset = gr.Radio(choices=["GEO", "SRA"], label="Dataset Type", value="SRA") submit_btn = gr.Button("Upload Records", variant="primary") with gr.Group(elem_classes=["admin-section"]): gr.HTML("Record removal
Delete Selected Record
Select a row from the database table before deleting.
") delete_btn = gr.Button("Delete Selected Row", variant="stop") status_output = gr.Textbox(label="Console Log", placeholder="Awaiting action...") with gr.Column(scale=2, min_width=520): with gr.Group(elem_classes=["admin-section"]): gr.HTML("Database browser
Filter and Browse Records
")
admin_species = gr.Dropdown(choices=["Bos taurus", "Buffalo"], label="Animal Type", value="Bos taurus")
admin_tissue = gr.Dropdown(choices=["Oocyte", "Blastocyst", "Sperm"], label="Tissue Type", value="Oocyte")
admin_tool = gr.Radio(choices=["GEO", "SRA"], label="Source", value="SRA")
with gr.Row():
admin_search_btn = gr.Button("Run Filter", variant="secondary")
admin_reset_btn = gr.Button("Reload All", variant="secondary")
admin_table_view = gr.Dataframe(value=BLANK_TEMPLATE_DF, interactive=False, wrap=False)
with gr.Group(elem_classes=["admin-section"]):
gr.HTML("Review management
Mark DOI as Read
Store a reviewed DOI in Supabase.
") with gr.Row(): manual_doi_input = gr.Textbox(label="DOI", placeholder="10.1093/nar/gkac123") manual_doi_btn = gr.Button("Mark as Read", variant="primary") manual_doi_status = gr.Markdown("") with gr.Group(elem_classes=["admin-section"]): gr.HTML("Review history
Marked Articles
Select an article, then remove it to clear the reviewed status.
") reviewed_table_view = gr.Dataframe(value=get_reviewed_table_df(), headers=['DOI', 'Reviewed At', 'Link'], datatype=['str', 'str', 'str'], interactive=False, wrap=True, max_height=300) selected_reviewed_doi = gr.State("") with gr.Row(): remove_reviewed_btn = gr.Button("Remove Selected Article", variant="stop") reviewed_status = gr.Markdown("") login_btn.click( fn=check_upload_credentials_generator, inputs=[user_box, pass_box], outputs=[login_panel, upload_panel, status_output, admin_table_view, home_locked_panel, home_editor_panel, home_editor_component, is_authenticated, pm_doi_col, ls_doi_col, reviewed_table_view, pubcrawler_table, pubcrawler_articles_html, pubcrawler_tab, junior_access_panel] ) pass_box.submit( fn=check_upload_credentials_generator, inputs=[user_box, pass_box], outputs=[login_panel, upload_panel, status_output, admin_table_view, home_locked_panel, home_editor_panel, home_editor_component, is_authenticated, pm_doi_col, ls_doi_col, reviewed_table_view, pubcrawler_table, pubcrawler_articles_html, pubcrawler_tab, junior_access_panel] ) pubcrawler_save_btn.click( fn=save_pubcrawler_term, inputs=[pubcrawler_term_input], outputs=[pubcrawler_status, pubcrawler_table, pubcrawler_term_input, pubcrawler_articles_html] ) pubcrawler_refresh_btn.click( fn=lambda request: (get_pubcrawler_terms_df(), get_pubcrawler_articles_html(request)), inputs=[], outputs=[pubcrawler_table, pubcrawler_articles_html] ) pubcrawler_term_input.submit( fn=save_pubcrawler_term, inputs=[pubcrawler_term_input], outputs=[pubcrawler_status, pubcrawler_table, pubcrawler_term_input, pubcrawler_articles_html] ) pubcrawler_table.select( fn=pubcrawler_row_selection, inputs=[pubcrawler_table], outputs=[selected_pubcrawler_id, pubcrawler_selection_status] ) pubcrawler_delete_btn.click( fn=delete_pubcrawler_term, inputs=[selected_pubcrawler_id], outputs=[pubcrawler_status, pubcrawler_table, selected_pubcrawler_id, pubcrawler_articles_html] ) pubcrawler_articles_refresh_btn.click( fn=refresh_pubcrawler_articles, inputs=[], outputs=[pubcrawler_articles_html] ) save_home_btn.click( fn=update_homepage_string, inputs=[hidden_canvas_data], outputs=[home_save_status, home_view_component], js="() => { const ed = document.getElementById('homepage-canvas-editor'); return [ed ? ed.innerHTML : '']; }" ) admin_table_view.select( fn=handle_row_selection, inputs=[admin_table_view], outputs=[selected_accession, status_output] ) admin_search_btn.click(fn=search_supabase, inputs=[admin_species, admin_tissue, admin_tool], outputs=admin_table_view) admin_reset_btn.click(fn=load_entire_table, inputs=[], outputs=admin_table_view) def post_upload_refresh(file, species, tissue, dataset, request: gr.Request): log_msg = upload_data_to_supabase(file, species, tissue, dataset, request) return log_msg, load_entire_table() submit_btn.click(fn=post_upload_refresh, inputs=[file_input, upload_species, upload_tissue, upload_dataset], outputs=[status_output, admin_table_view]) delete_btn.click(fn=delete_record_from_supabase, inputs=[selected_accession, admin_table_view], outputs=[status_output, admin_table_view, selected_accession]) manual_doi_btn.click(fn=manual_mark_doi_and_refresh, inputs=[manual_doi_input], outputs=[manual_doi_status, reviewed_table_view]) reviewed_table_view.select(fn=reviewed_row_selection, inputs=[reviewed_table_view], outputs=[selected_reviewed_doi, reviewed_status]) remove_reviewed_btn.click(fn=remove_selected_reviewed_doi, inputs=[selected_reviewed_doi], outputs=[reviewed_status, reviewed_table_view, selected_reviewed_doi]) # ── Public API ────────────────────────────────────────────────────────── with gr.Row(visible=False): _api_in_doi = gr.Textbox(value="", elem_id="api-doi") _api_in_action = gr.Textbox(value="", elem_id="api-action") _api_out = gr.Textbox(value="", elem_id="api-out") _api_btn = gr.Button("api", elem_id="api-btn") _api_btn.click(fn=mark_article_api, inputs=[_api_in_doi, _api_in_action], outputs=[_api_out], api_name="mark_article_api") # Add parameters directly to launch() demo.launch(css=css, theme=light_theme)