import json
import math
import os
import re
import glob
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import dash
from dash import dcc, html, dash_table, Input, Output, State
import dash_bootstrap_components as dbc
from flask import send_from_directory, abort
# ---------------------------------------------------------------------------
# Paths
# ---------------------------------------------------------------------------
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
_JSON_PATH_GZ = os.path.join(BASE_DIR, "data", "proteins.json.gz")
_JSON_PATH = os.path.join(BASE_DIR, "data", "proteins.json")
JSON_PATH = _JSON_PATH_GZ if os.path.isfile(_JSON_PATH_GZ) else _JSON_PATH
METADATA_PATH = os.path.join(BASE_DIR, "data", "metadata.json")
GRID_BASE = os.path.join(BASE_DIR, "data", "clustering")
# ---------------------------------------------------------------------------
# Load protein data (includes PMID-level UniProt metadata)
# ---------------------------------------------------------------------------
def load_data():
import gzip as _gzip
_open = _gzip.open if JSON_PATH.endswith(".gz") else open
with _open(JSON_PATH, "rt", encoding="utf-8") as f:
raw = json.load(f)
with open(METADATA_PATH) as f:
meta = json.load(f)
rows = []
for pmid, entry in raw.items():
uniprot_ids = entry.get("Uniprot_IDS", []) or []
protein_names = entry.get("Protein_names", []) or []
organisms = entry.get("Organisms", []) or []
sequences = entry.get("Sequences", []) or []
n_collected = entry.get("Number_of_proteins_collected", 0)
# Paper-level metadata from metadata.json
m = meta.get(str(pmid), {})
groups = m.get("groups", [])
title = m.get("title", "")
pub_date = m.get("pub_date", "")
source = m.get("source", "")
url = m.get("url", "")
for protein in entry.get("proteins", []):
row = {"pmid": pmid}
row.update({k: (v if v is not None else "") for k, v in protein.items()})
row["n_uniprot_entries"] = len(uniprot_ids)
row["uniprot_ids"] = ", ".join(uniprot_ids)
row["n_proteins_collected"] = n_collected
row["groups"] = ", ".join(groups) if groups else "unknown"
row["title"] = title
row["pub_date"] = pub_date
row["source"] = source
row["url"] = url
rows.append(row)
return pd.DataFrame(rows), raw, meta
df, RAW, META = load_data()
ALL_GROUPS = sorted({
g for entry in META.values() for g in entry.get("groups", [])
})
# PMID → set of groups, for fast cluster filtering
PMID_GROUPS = {
pmid: set(entry.get("groups", []))
for pmid, entry in META.items()
}
PMID_ERA_BINS = [
("pre-2001", 0, 10_000_000),
("2001–2005", 10_000_000, 15_000_000),
("2006–2009", 15_000_000, 20_000_000),
("2010–2013", 20_000_000, 25_000_000),
("2014–2017", 25_000_000, 30_000_000),
("2018–2020", 30_000_000, 35_000_000),
("2021–2023", 35_000_000, 40_000_000),
]
# Column sets
EXTRACTION_FIELDS = [
"pmid", "enzyme_name", "organism_source", "strain", "expression_strain",
"plasmid", "molecular_weight", "medium_name", "inducer",
"induction_temperature", "lysis_buffer", "elution_buffer", "desalting_process",
]
PAPER_FIELDS = ["title", "pub_date", "source", "groups"]
UNIPROT_FIELDS = ["uniprot_ids", "n_uniprot_entries", "n_proteins_collected"]
TABLE_FIELDS = EXTRACTION_FIELDS + PAPER_FIELDS + UNIPROT_FIELDS
# ---------------------------------------------------------------------------
# Scan grid directory structure
# ---------------------------------------------------------------------------
def scan_grid():
models, mins, thresholds, fields = set(), set(), set(), set()
for pattern in (
os.path.join(GRID_BASE, "model=*", "min=*", "t=*_ALL_FIELDS.csv.gz"),
os.path.join(GRID_BASE, "model=*", "min=*", "t=*_ALL_FIELDS.csv"),
):
for fpath in glob.glob(pattern):
parts = fpath.split(os.sep)
for p in parts:
if p.startswith("model="):
models.add(p.replace("model=", ""))
elif p.startswith("min="):
mins.add(p.replace("min=", ""))
fname = os.path.basename(fpath)
m = re.match(r"t=([\d.]+)_ALL_FIELDS\.csv", fname)
if m:
thresholds.add(m.group(1))
# Discover fields from sample ALL_FIELDS files — try until one succeeds
_FALLBACK_FIELDS = [
"desalting_process", "elution_buffer", "enzyme_name", "expression_strain",
"inducer", "induction_temperature", "lysis_buffer", "medium_name",
"molecular_weight", "organism_source", "plasmid", "source_key", "strain",
]
for sample in (
glob.glob(os.path.join(GRID_BASE, "model=*", "min=*", "t=*_ALL_FIELDS.csv.gz")) +
glob.glob(os.path.join(GRID_BASE, "model=*", "min=*", "t=*_ALL_FIELDS.csv"))
):
try:
compression = "gzip" if sample.endswith(".gz") else None
fields = set(pd.read_csv(sample, compression=compression, usecols=["field"])["field"].unique())
if fields:
break
except Exception:
continue
if not fields:
fields = set(_FALLBACK_FIELDS)
return (
sorted(models),
sorted(mins, key=float),
sorted(thresholds, key=float),
sorted(fields),
)
MODELS, MINS, THRESHOLDS, CLUSTER_FIELDS = scan_grid()
print(f"[startup] GRID_BASE={GRID_BASE}")
print(f"[startup] Models found: {MODELS}")
print(f"[startup] Fields found: {CLUSTER_FIELDS}")
METRIC_TYPES = ["silhouette_cosine", "davies_bouldin", "n_clusters"]
# ---------------------------------------------------------------------------
# Load all metrics CSVs into one DataFrame at startup
# ---------------------------------------------------------------------------
def load_all_metrics():
rows = []
for fpath in glob.glob(os.path.join(GRID_BASE, "model=*", "min=*", "t=*_FIELD_CLUSTER_METRICS.csv")):
parts = fpath.replace(GRID_BASE + os.sep, "").split(os.sep)
model = parts[0].replace("model=", "")
try:
chunk = pd.read_csv(fpath)
chunk["model"] = model
rows.append(chunk)
except Exception:
pass
if not rows:
return pd.DataFrame()
return pd.concat(rows, ignore_index=True)
METRICS_DF = load_all_metrics()
METRIC_FIELDS = sorted(METRICS_DF["field"].unique()) if not METRICS_DF.empty else []
METRIC_MODELS = sorted(METRICS_DF["model"].unique()) if not METRICS_DF.empty else []
# ---------------------------------------------------------------------------
# Evaluation data
# ---------------------------------------------------------------------------
_EVAL_BASE = os.path.join(BASE_DIR, "data", "evaluation_llm_results")
_PURIF_FIELDS = [
"enzyme_name", "organism_source", "strain", "expression_strain", "plasmid",
"molecular_weight", "medium_name", "inducer", "induction_temperature",
"lysis_buffer", "elution_buffer", "desalting_process",
]
_NLP_METRICS = {
"bertscore_f1": "BERTScore F1",
"rouge1_f": "ROUGE-1 F",
"bleu": "BLEU",
"meteor": "METEOR",
"cosine_similarity":"Cosine Similarity",
}
# BLEU and METEOR are stored 0–100; divide by 100 to normalise to 0–1 for display
_NLP_SCALE = {"bleu": 100.0, "meteor": 100.0}
_LLM_EVAL_CONFIGS = [
("azoreductases","gpt-4.1", False, os.path.join(_EVAL_BASE,"cleaned","results_azo_gpt-4.1_cleaned.json")),
("azoreductases","gpt-5-mini",False,os.path.join(_EVAL_BASE,"cleaned","results_azo_gpt-5-mini_cleaned.json")),
("azoreductases","gpt-5", False, os.path.join(_EVAL_BASE,"cleaned","results_azo_gpt-5_cleaned.json")),
("azoreductases","gpt-4.1", True, os.path.join(_EVAL_BASE,"cleaned","results_azo_gpt-4.1_rag_600_100_cleaned.json")),
("azoreductases","gpt-5-mini",True, os.path.join(_EVAL_BASE,"cleaned","results_azo_gpt-5-mini_rag_600_100_cleaned.json")),
("azoreductases","gpt-5", True, os.path.join(_EVAL_BASE,"cleaned","results_azo_gpt-5_rag_600_100_cleaned.json")),
("sams","gpt-4.1", False, os.path.join(_EVAL_BASE,"sams","cleaned","sams_gpt-4.1_cleaned.json")),
("sams","gpt-5-mini",False, os.path.join(_EVAL_BASE,"sams","cleaned","sams_gpt-5-mini_cleaned.json")),
("sams","gpt-5", False, os.path.join(_EVAL_BASE,"sams","cleaned","sams_gpt-5_cleaned.json")),
("sams","gpt-4.1", True, os.path.join(_EVAL_BASE,"sams","cleaned","sams_gpt-4.1_rag_600_100_cleaned.json")),
("sams","gpt-5-mini",True, os.path.join(_EVAL_BASE,"sams","cleaned","sams_gpt-5-mini_rag_600_100_cleaned.json")),
("sams","gpt-5", True, os.path.join(_EVAL_BASE,"sams","cleaned","sams_gpt-5_rag_600_100_cleaned.json")),
]
_NLP_EVAL_CONFIGS = [
("azoreductases","gpt-4.1", False, os.path.join(_EVAL_BASE,"results_azo_purification_nlp_gpt-4.1_cleaned.json")),
("azoreductases","gpt-5-mini",False,os.path.join(_EVAL_BASE,"results_azo_purification_nlp_gpt-5-mini_cleaned.json")),
("azoreductases","gpt-5", False, os.path.join(_EVAL_BASE,"results_azo_purification_nlp_gpt-5_cleaned.json")),
("azoreductases","gpt-4.1", True, os.path.join(_EVAL_BASE,"results_azo_purification_nlp_gpt-4.1_rag_600_100_cleaned.json")),
("azoreductases","gpt-5-mini",True, os.path.join(_EVAL_BASE,"results_azo_purification_nlp_gpt-5-mini_rag_600_100_cleaned.json")),
("azoreductases","gpt-5", True, os.path.join(_EVAL_BASE,"results_azo_purification_nlp_gpt-5_rag_600_100_cleaned.json")),
("sams","gpt-4.1", False, os.path.join(_EVAL_BASE,"sams","results_nlp_sams_purification_gpt-4.1_cleaned.json")),
("sams","gpt-5-mini",False, os.path.join(_EVAL_BASE,"sams","results_nlp_sams_purification_gpt-5-mini_cleaned.json")),
("sams","gpt-5", False, os.path.join(_EVAL_BASE,"sams","results_nlp_sams_purification_gpt-5_cleaned.json")),
("sams","gpt-4.1", True, os.path.join(_EVAL_BASE,"sams","results_nlp_sams_purification_gpt-4.1_rag_600_100_cleaned.json")),
("sams","gpt-5-mini",True, os.path.join(_EVAL_BASE,"sams","results_nlp_sams_purification_gpt-5-mini_rag_600_100_cleaned.json")),
("sams","gpt-5", True, os.path.join(_EVAL_BASE,"sams","results_nlp_sams_purification_gpt-5_rag_600_100_cleaned.json")),
]
_EVAL_LABELS = [
"gpt-4.1 (no-RAG)", "gpt-4.1 (RAG)",
"gpt-5-mini (no-RAG)", "gpt-5-mini (RAG)",
"gpt-5 (no-RAG)", "gpt-5 (RAG)",
]
def _flatten_purif_eval(configs, score_key):
rows = []
for group, model, rag, fpath in configs:
if not os.path.exists(fpath):
continue
try:
with open(fpath) as f:
data = json.load(f)
except Exception:
continue
label = f"{model} ({'RAG' if rag else 'no-RAG'})"
for pmid, entry in data.items():
for pair in entry.get("evaluated_protein_pairs", []):
gt_prot = pair.get("gt_protein", {})
llm_prot = pair.get("llm_protein", {})
for field, scores in pair.get("evaluation_result", {}).items():
if field not in _PURIF_FIELDS or not isinstance(scores, dict):
continue
val = scores.get(score_key)
if val is None:
continue
rows.append({
"group": group, "model": model, "rag": rag,
"label": label, "pmid": pmid, "field": field,
score_key: float(val),
"gt_text": str(gt_prot.get(field) or ""),
"llm_text": str(llm_prot.get(field) or ""),
"explanation": str(scores.get("explanation", "")),
})
return pd.DataFrame(rows) if rows else pd.DataFrame(
columns=["group","model","rag","label","pmid","field",score_key,
"gt_text","llm_text","explanation"])
def load_eval_data():
llm_df = _flatten_purif_eval(_LLM_EVAL_CONFIGS, "similarity_score")
nlp_rows = []
for group, model, rag, fpath in _NLP_EVAL_CONFIGS:
if not os.path.exists(fpath):
continue
try:
with open(fpath) as f:
data = json.load(f)
except Exception:
continue
label = f"{model} ({'RAG' if rag else 'no-RAG'})"
for pmid, entry in data.items():
for pair in entry.get("evaluated_protein_pairs", []):
for field, scores in pair.get("evaluation_result", {}).items():
if field not in _PURIF_FIELDS or not isinstance(scores, dict):
continue
gt_text = str(scores.get("GT", "") or "")
llm_text = str(scores.get("LLM", "") or "")
for metric in _NLP_METRICS:
val = scores.get(metric)
if val is None:
continue
nlp_rows.append({"group": group, "model": model, "rag": rag,
"label": label, "pmid": pmid, "field": field,
"metric": metric, "value": float(val),
"gt_text": gt_text, "llm_text": llm_text})
nlp_df = pd.DataFrame(nlp_rows) if nlp_rows else pd.DataFrame(
columns=["group","model","rag","label","pmid","field","metric","value"])
_classif_sources = [
("azoreductases", os.path.join(os.path.dirname(_EVAL_BASE),
"results_method_extraction",
"results_azo_method_extraction_metrics.json")),
("sams", os.path.join(BASE_DIR, "data", "sams", "extracted_methods_sams_metrics.json")),
]
classif_rows = []
for group, fpath in _classif_sources:
if not os.path.exists(fpath):
continue
try:
with open(fpath) as f:
data = json.load(f)
for pmid, scores in data.items():
if not isinstance(scores, dict):
continue
gt_text = str(scores.get("GT", "") or "")
llm_text = str(scores.get("LLM", "") or "")
for metric in _NLP_METRICS:
val = scores.get(metric)
if val is not None:
classif_rows.append({
"group": group, "pmid": pmid,
"metric": metric, "value": float(val),
"gt_text": gt_text, "llm_text": llm_text,
})
except Exception:
pass
classif_df = pd.DataFrame(classif_rows) if classif_rows else pd.DataFrame(
columns=["group","pmid","metric","value","gt_text","llm_text"])
return llm_df, nlp_df, classif_df
EVAL_LLM_DF, EVAL_NLP_DF, CLASSIF_DF = load_eval_data()
# ---------------------------------------------------------------------------
# Classification confusion matrices
# ---------------------------------------------------------------------------
_CONFMAT_AZO_PATH = os.path.join(BASE_DIR, "data", "azoreductases_gt", "azo_metadata.json")
_CONFMAT_SAMS_PATH = os.path.join(BASE_DIR, "data", "confusion_matrix", "confusion_matrix_counts.csv")
_SAMS_ALL_PATH = os.path.join(BASE_DIR, "data", "sams", "sam_pdfs_enzymology.json")
_SAMS_FILTERED_PATH = os.path.join(BASE_DIR, "data", "sams", "filtered.json")
def _compute_metrics(tp, fp, fn, tn):
precision = tp / (tp + fp) if (tp + fp) > 0 else None
recall = tp / (tp + fn) if (tp + fn) > 0 else None
f1 = (2 * precision * recall / (precision + recall)
if precision is not None and recall is not None
and (precision + recall) > 0 else None)
accuracy = (tp + tn) / (tp + fp + fn + tn) if (tp + fp + fn + tn) > 0 else None
return {"TP": tp, "FP": fp, "FN": fn, "TN": tn,
"Precision": precision, "Recall": recall, "F1": f1, "Accuracy": accuracy}
def load_confusion_matrices():
result = {}
# Azoreductases: all 31 GT papers classified as enzymology
try:
with open(_CONFMAT_AZO_PATH) as f:
azo_meta = json.load(f)
n = len(azo_meta)
result["azoreductases"] = _compute_metrics(tp=n, fp=0, fn=0, tn=0)
result["azoreductases"]["note"] = (
f"All {n} ground-truth papers were classified as enzymology. "
"No negative set available, so TN and FP are not applicable."
)
# All are TP
result["azoreductases"]["papers"] = {
pmid: {"actual": True, "predicted": True,
"title": v.get("title", ""),
"pub_date": v.get("pub_date", ""),
"source": v.get("source", "")}
for pmid, v in azo_meta.items()
}
except Exception:
pass
# SAMs: full confusion matrix from CSV (rows=Actual, cols=Predicted)
try:
cm = pd.read_csv(_CONFMAT_SAMS_PATH, index_col=0)
cm.index = cm.index.map(lambda v: bool(v) if not isinstance(v, bool) else v)
cm.columns = cm.columns.map(lambda v: bool(v) if v == "True" else (False if v == "False" else v))
tp = int(cm.loc[True, True])
fn = int(cm.loc[True, False])
fp = int(cm.loc[False, True])
tn = int(cm.loc[False, False])
result["sams"] = _compute_metrics(tp=tp, fp=fp, fn=fn, tn=tn)
result["sams"]["matrix"] = cm
# Per-paper category assignments
with open(_SAMS_ALL_PATH) as f:
all_papers = json.load(f)
with open(_SAMS_FILTERED_PATH) as f:
filtered = json.load(f)
pred_pos = set(filtered.keys())
papers = {}
for pmid, v in all_papers.items():
actual = bool(v.get("Count_Enzymology", False))
predicted = pmid in pred_pos
papers[pmid] = {
"actual": actual,
"predicted": predicted,
"title": v.get("title", ""),
"pub_date": v.get("pub_date", ""),
"source": v.get("source", ""),
"url": v.get("url", ""),
}
result["sams"]["papers"] = papers
except Exception as e:
print("SAMs confmat load error:", e)
return result
CONF_MATRICES = load_confusion_matrices()
# ---------------------------------------------------------------------------
# Dash app
# ---------------------------------------------------------------------------
CUSTOM_CSS = """
/* ── Global ── */
body { background-color: #f4f6f9; }
/* ── Navbar ── */
.navbar-brand { font-size: 1.2rem; font-weight: 700; letter-spacing: 0.02em; }
.navbar-subtitle { font-size: 0.75rem; opacity: 0.75; display: block; line-height: 1.2; }
/* ── Tabs ── */
.nav-tabs .nav-link { color: #495057; font-weight: 500; border-radius: 6px 6px 0 0; }
.nav-tabs .nav-link.active { color: #1a73e8; font-weight: 700; border-bottom: 3px solid #1a73e8; }
.nav-tabs .nav-link:hover { color: #1a73e8; }
/* ── Filter panel ── */
.filter-panel {
background: #ffffff;
border: 1px solid #dee2e6;
border-radius: 8px;
padding: 16px 20px 12px;
margin-bottom: 16px;
box-shadow: 0 1px 4px rgba(0,0,0,.06);
}
/* ── Contact avatar ── */
.avatar-circle {
width: 56px; height: 56px; border-radius: 50%;
display: flex; align-items: center; justify-content: center;
font-size: 1.2rem; font-weight: 700; color: #fff;
margin-bottom: 10px;
}
.person-card { transition: transform .15s, box-shadow .15s; }
.person-card:hover { transform: translateY(-3px); box-shadow: 0 6px 18px rgba(0,0,0,.12) !important; }
/* ── Accordion ── */
.accordion-button { font-weight: 600; }
.accordion-item { border-left: 4px solid #1a73e8 !important; margin-bottom: 6px; border-radius: 6px !important; }
/* ── Tab fade-in animation ── */
@keyframes tabFadeIn {
from { opacity: 0; transform: translateY(8px); }
to { opacity: 1; transform: translateY(0); }
}
#tab-content > * {
animation: tabFadeIn 0.25s ease forwards;
}
.tab-content > .tab-pane.active {
animation: tabFadeIn 0.25s ease forwards;
}
"""
app = dash.Dash(
__name__,
external_stylesheets=[dbc.themes.LUX],
suppress_callback_exceptions=True,
title="ProtoPure",
)
app.index_string = app.index_string.replace(
"", f""
)
server = app.server
@server.route("/grid_files/")
def serve_grid_file(filepath):
full = os.path.realpath(os.path.join(GRID_BASE, filepath))
if not full.startswith(os.path.realpath(GRID_BASE)):
abort(403)
if not os.path.isfile(full):
abort(404)
return send_from_directory(os.path.dirname(full), os.path.basename(full))
# ---------------------------------------------------------------------------
# Layout helpers
# ---------------------------------------------------------------------------
def make_dropdown(label, id_, options, value=None, multi=False, clearable=True):
return dbc.Col([
html.Label(label, className="fw-semibold small mb-1"),
dcc.Dropdown(
id=id_,
options=[{"label": o, "value": o} for o in options],
value=value if value is not None else (options[0] if options else None),
multi=multi,
clearable=clearable,
style={"fontSize": "13px"},
),
])
def detail_field(label, value):
"""Single labeled field for the detail panel."""
if not value:
return None
return html.Div([
html.Span(label + ": ", className="fw-semibold text-muted small"),
html.Span(str(value), className="small"),
], className="mb-1")
# ---------------------------------------------------------------------------
# Tab layouts
# ---------------------------------------------------------------------------
DEFAULT_COLS = ["pmid", "enzyme_name", "organism_source"]
FIELD_LABELS = {c: c.replace("_", " ").title() for c in TABLE_FIELDS}
FIELD_LABELS.update({
"pmid": "PMID", "enzyme_name": "Enzyme Name", "organism_source": "Organism",
"expression_strain": "Expression Strain", "uniprot_ids": "UniProt IDs",
"n_uniprot_entries": "# UniProt", "n_proteins_collected": "# Proteins",
"pub_date": "Publication Date", "source": "Journal",
})
def proteins_tab():
search_fields = [
{"label": "PMID", "value": "pmid"},
{"label": "Enzyme name", "value": "enzyme_name"},
{"label": "Organism", "value": "organism_source"},
{"label": "Expression strain", "value": "expression_strain"},
{"label": "Plasmid", "value": "plasmid"},
{"label": "Inducer", "value": "inducer"},
{"label": "UniProt ID", "value": "uniprot_ids"},
{"label": "Journal", "value": "source"},
{"label": "Paper title", "value": "title"},
]
col_options = [{"label": FIELD_LABELS.get(c, c), "value": c} for c in TABLE_FIELDS]
return dbc.Container([
html.Div([
dbc.Row([
dbc.Col([
html.Label("Filter by group", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="filter-group",
options=[{"label": g, "value": g} for g in ALL_GROUPS],
multi=True,
placeholder="All groups",
style={"fontSize": "13px"},
),
], width=4),
dbc.Col([
html.Label("Search by", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="search-field",
options=search_fields,
value="enzyme_name",
clearable=False,
style={"fontSize": "13px"},
),
], width=2),
dbc.Col([
html.Label("Search value", className="fw-semibold small mb-1"),
dbc.InputGroup([
dbc.Input(id="search-value", placeholder="Type to filter…", debounce=True, size="sm"),
dbc.Button("✕ Clear", id="clear-search", size="sm", color="secondary", outline=True),
]),
], width=6),
], className="mb-2"),
dbc.Row([
dbc.Col([
html.Label("Cross-filter: field", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="table-cross-field",
options=[{"label": f.replace("_"," ").title(), "value": f}
for f in EXTRACTION_FIELDS[1:]],
placeholder="Select a field…",
clearable=True,
style={"fontSize": "13px"},
),
], width=3),
dbc.Col([
html.Label("Cross-filter: value", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="table-cross-value",
options=[],
placeholder="Select a value…",
clearable=True,
style={"fontSize": "13px"},
),
], width=5),
dbc.Col([
html.Label("Show columns", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="col-selector",
options=col_options,
value=DEFAULT_COLS,
multi=True,
placeholder="Select columns…",
style={"fontSize": "13px"},
),
], width=4),
]),
], className="filter-panel"),
dbc.Row([
dbc.Col(html.Div(id="protein-count", className="text-muted small"), width=10),
dbc.Col(
dbc.Button("⬇ Download CSV", id="download-btn", size="sm", color="success", outline=True),
width=2, className="text-end",
),
], className="mb-2 align-items-center"),
dcc.Download(id="download-csv"),
# Table
dash_table.DataTable(
id="protein-table",
columns=[{"name": FIELD_LABELS.get(c, c), "id": c} for c in TABLE_FIELDS],
hidden_columns=[c for c in TABLE_FIELDS if c not in DEFAULT_COLS],
data=df[TABLE_FIELDS].to_dict("records"),
page_size=25,
page_action="native",
sort_action="native",
sort_by=[{"column_id": "pmid", "direction": "asc"}],
filter_action="none",
row_selectable="single",
selected_rows=[],
style_table={"overflowX": "auto"},
style_cell={
"fontSize": "12px",
"padding": "6px 10px",
"textAlign": "left",
"maxWidth": "220px",
"overflow": "hidden",
"textOverflow": "ellipsis",
"whiteSpace": "nowrap",
},
style_header={
"fontWeight": "700",
"backgroundColor": "#1a3a5c",
"color": "#ffffff",
"borderBottom": "2px solid #1a3a5c",
"fontSize": "11px",
"textTransform": "uppercase",
"letterSpacing": "0.04em",
},
style_data_conditional=[
{"if": {"row_index": "odd"}, "backgroundColor": "#eef4fd"},
{"if": {"state": "selected"}, "backgroundColor": "#cfe2ff", "border": "1px solid #9ec5fe"},
],
tooltip_delay=0,
tooltip_duration=None,
),
# Detail panel (shown on row click)
html.Div(id="detail-panel", className="mt-3"),
], fluid=True, className="pt-3")
def clustering_tab():
return dbc.Container([
dbc.Row([
make_dropdown("Model", "dd-model", MODELS, value="kamalkraj__BioSimCSE-BioLinkBERT-BASE" if "kamalkraj__BioSimCSE-BioLinkBERT-BASE" in MODELS else (MODELS[0] if MODELS else None)),
make_dropdown("Min community size", "dd-min", MINS, value="2" if "2" in MINS else (MINS[0] if MINS else None)),
make_dropdown("Threshold", "dd-threshold", THRESHOLDS, value="0.9" if "0.9" in THRESHOLDS else (THRESHOLDS[0] if THRESHOLDS else None)),
make_dropdown("Field", "dd-field", CLUSTER_FIELDS, value="organism_source" if "organism_source" in CLUSTER_FIELDS else (CLUSTER_FIELDS[0] if CLUSTER_FIELDS else None)),
], className="mb-3 g-3"),
dbc.Row([
dbc.Col([
html.Label("Filter by group", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="cluster-group-filter",
options=[{"label": g, "value": g} for g in ALL_GROUPS],
multi=True,
placeholder="All groups",
style={"fontSize": "13px"},
),
], width=3),
dbc.Col([
html.Label("Cross-filter: field", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="dd-cross-field",
options=[{"label": f.replace("_"," ").title(), "value": f}
for f in CLUSTER_FIELDS],
placeholder="No cross-filter",
clearable=True,
style={"fontSize": "13px"},
),
], width=3),
dbc.Col([
html.Label("Cross-filter: value", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="dd-cross-value",
options=[],
placeholder="Select a value…",
clearable=True,
disabled=True,
style={"fontSize": "13px"},
),
], width=3),
dbc.Col([
dbc.RadioItems(
id="plot-type",
options=[
{"label": " UMAP", "value": "cluster"},
{"label": " Distribution", "value": "distribution"},
],
value="cluster",
inline=True,
className="mb-2 mt-4",
),
], width=2),
dbc.Col([
html.Label("Show top N clusters", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="top-n-clusters",
options=[{"label": str(n), "value": n} for n in [10, 15, 20, 30, 50]] +
[{"label": "All", "value": 0}],
value=20,
clearable=False,
style={"fontSize": "13px"},
),
], width=2),
], className="mb-2 align-items-end"),
dbc.Row([
dbc.Col([
html.Label("Filter by publication era", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="dd-era-filter",
options=[{"label": label, "value": label}
for label, _, _ in PMID_ERA_BINS],
placeholder="All time periods",
clearable=True,
style={"fontSize": "13px"},
),
], width=4),
], className="mb-2"),
html.Div(id="diversity-cards", className="mb-2"),
dbc.Row([dbc.Col(html.Div(id="plot-status", className="text-danger small mb-1"))]),
dbc.Row([
dbc.Col(
dcc.Loading(
dcc.Graph(
id="cluster-graph",
config={"displayModeBar": True, "toImageButtonOptions": {"format": "svg"}},
style={"height": "650px"},
),
type="circle", color="#1a73e8",
),
width=12,
),
]),
html.Div(id="cluster-point-detail", className="mt-2"),
dcc.Store(id="cluster-table-store"),
dcc.Download(id="download-cluster-csv"),
html.Hr(),
html.H6("Cluster metrics for selected parameters", className="mt-2 mb-2 fw-semibold"),
dbc.Row([dbc.Col(html.Div(id="metrics-table-container"), width=12)]),
], fluid=True, className="pt-3")
_METRIC_LABELS = {
"silhouette_cosine": "Silhouette Score",
"davies_bouldin": "Davies-Bouldin Index",
"n_clusters": "Number of Clusters",
}
_OVERVIEW_METRICS = {
"silhouette_cosine": ("Silhouette Score", True, "RdYlGn", "Mean
Silhouette"),
"davies_bouldin": ("Davies-Bouldin Index", False, "RdYlGn_r", "Mean
Davies-Bouldin"),
"n_clusters": ("N Clusters", True, "Blues", "Mean
N Clusters"),
}
def _build_grid_summary_figure(field=None, min_size=None, metric="silhouette_cosine"):
"""Heatmap of a clustering metric per (model, threshold).
field=None/'__global__' → average over all fields.
min_size=None/'__all__' → average over all min community sizes.
metric: one of silhouette_cosine | davies_bouldin | n_clusters.
"""
if METRICS_DF.empty:
return go.Figure()
if metric not in _OVERVIEW_METRICS:
metric = "silhouette_cosine"
metric_label, higher_better, colorscale, cb_title = _OVERVIEW_METRICS[metric]
def _short(m):
return m.split("__")[-1] if "__" in m else m
src = METRICS_DF.dropna(subset=[metric])
if field and field != "__global__":
src = src[src["field"] == field]
if src.empty:
return go.Figure()
if min_size and min_size != "__all__":
try:
src = src[src["min_community_size"] == int(min_size)]
except (ValueError, TypeError):
pass
if src.empty:
return go.Figure()
agg = (
src
.groupby(["model", "threshold"])[metric]
.mean()
.reset_index()
)
agg["short_model"] = agg["model"].apply(_short)
pivot = agg.pivot_table(
index="short_model", columns="threshold",
values=metric, aggfunc="mean",
)
# Always sort rows by silhouette score so all three heatmaps share the same model order
sil_src = METRICS_DF.dropna(subset=["silhouette_cosine"])
if field and field != "__global__":
sil_src = sil_src[sil_src["field"] == field]
if min_size and min_size != "__all__":
try:
sil_src = sil_src[sil_src["min_community_size"] == int(min_size)]
except (ValueError, TypeError):
pass
sil_agg = sil_src.groupby("model")["silhouette_cosine"].mean()
sil_agg.index = sil_agg.index.map(_short)
# Models present in pivot but missing from silhouette get ranked last
row_order = (
sil_agg.reindex(pivot.index, fill_value=0)
.sort_values(ascending=True) # ascending=True → worst at bottom, best at top in Plotly
.index.tolist()
)
pivot = pivot.loc[row_order]
# Identify best cell and embed star in cell text
best_idx = agg[metric].idxmax() if higher_better else agg[metric].idxmin()
best = agg.loc[best_idx]
best_short = _short(best["model"])
best_thresh = best["threshold"]
rows_list = list(pivot.index)
cols_list = list(pivot.columns)
fmt = ".0f" if metric == "n_clusters" else ".3f"
text = []
for ri, row_name in enumerate(rows_list):
row_text = []
for ci, col_val in enumerate(cols_list):
v = pivot.iloc[ri, ci]
if pd.notna(v):
cell = f"★ {v:{fmt}}" if (row_name == best_short and col_val == best_thresh) else f"{v:{fmt}}"
else:
cell = ""
row_text.append(cell)
text.append(row_text)
field_label = "all fields" if (not field or field == "__global__") else field.replace("_", " ").title()
min_label = "all min-sizes" if (not min_size or min_size == "__all__") else f"min={min_size}"
n_models = len(pivot.index)
fig = go.Figure(go.Heatmap(
z=pivot.values,
x=[str(c) for c in pivot.columns],
y=list(pivot.index),
colorscale=colorscale,
text=text,
texttemplate="%{text}",
textfont=dict(size=11),
colorbar=dict(title=dict(text=cb_title, font=dict(size=11)), thickness=14),
hovertemplate=f"Model: %{{y}}
Threshold: %{{x}}
{metric_label}: %{{z:{fmt}}}",
))
better_str = "higher = better" if higher_better else "lower = better"
fig.update_layout(
title=dict(
text=f"Grid Search Overview — {metric_label} ({better_str}) · {field_label} · {min_label} ★ = best",
font=dict(size=13),
),
xaxis=dict(title="Threshold", type="category", tickfont=dict(size=11)),
yaxis=dict(title="", type="category", tickfont=dict(size=10)),
paper_bgcolor="#ffffff",
plot_bgcolor="#f9fafc",
height=max(360, n_models * 30 + 100),
margin=dict(l=240, r=30, t=55, b=50),
)
return fig
_SUMMARY_FIELD_OPTIONS = (
[{"label": "All fields (mean)", "value": "__global__"}]
+ [{"label": f.replace("_", " ").title(), "value": f} for f in sorted(METRIC_FIELDS)]
)
_SUMMARY_MINSIZE_OPTIONS = (
[{"label": "All min-sizes (mean)", "value": "__all__"}]
+ [{"label": f"min = {m}", "value": str(m)}
for m in sorted(METRICS_DF["min_community_size"].unique().tolist())
if not METRICS_DF.empty]
)
def grid_metrics_tab():
if METRICS_DF.empty:
return dbc.Container([html.P("No metrics data found.", className="text-muted mt-3")])
return dbc.Container([
# ── Grid search overview (callback-driven) ────────────────────────────
dbc.Card([
dbc.CardBody([
dbc.Row([
dbc.Col([
html.Label("Metric", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="dd-summary-metric",
options=[{"label": v[0], "value": k}
for k, v in _OVERVIEW_METRICS.items()],
value="silhouette_cosine",
clearable=False,
style={"fontSize": "13px"},
),
], width=3),
dbc.Col([
html.Label("Field", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="dd-summary-field",
options=_SUMMARY_FIELD_OPTIONS,
value="__global__",
clearable=False,
style={"fontSize": "13px"},
),
], width=4),
dbc.Col([
html.Label("Min community size", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="dd-summary-minsize",
options=_SUMMARY_MINSIZE_OPTIONS,
value="__all__",
clearable=False,
style={"fontSize": "13px"},
),
], width=3),
], className="mb-2"),
dcc.Loading(
dcc.Graph(
id="metrics-summary-graph",
config={"displayModeBar": True,
"toImageButtonOptions": {"format": "svg", "filename": "grid_search_summary"}},
),
type="circle", color="#1a73e8",
),
], className="p-2"),
], className="mb-3 shadow-sm border-0"),
html.Hr(className="my-2"),
html.H6("Detailed view — drill down by field", className="fw-semibold mb-2"),
html.Div([
dbc.Row([
dbc.Col([
html.Label("Field", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="dd-metrics-field",
options=[{"label": f.replace("_", " ").title(), "value": f}
for f in METRIC_FIELDS],
value=METRIC_FIELDS[0] if METRIC_FIELDS else None,
clearable=False,
style={"fontSize": "13px"},
),
], width=4),
dbc.Col([
html.Label("Metric", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="dd-metrics-type",
options=[{"label": v, "value": k} for k, v in _METRIC_LABELS.items()],
value="silhouette_cosine",
clearable=False,
style={"fontSize": "13px"},
),
], width=4),
dbc.Col([
html.Label("Model (heatmap)", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="dd-metrics-model",
options=[{"label": m, "value": m} for m in METRIC_MODELS],
value=METRIC_MODELS[0] if METRIC_MODELS else None,
clearable=False,
style={"fontSize": "13px"},
),
], width=4),
]),
], className="filter-panel"),
dcc.Loading(
dbc.Row([
dbc.Col(
dcc.Graph(id="metrics-heatmap",
config={"displayModeBar": True,
"toImageButtonOptions": {"format": "svg"}},
style={"height": "420px"}),
width=6,
),
dbc.Col(
dcc.Graph(id="metrics-model-compare",
config={"displayModeBar": True,
"toImageButtonOptions": {"format": "svg"}},
style={"height": "420px"}),
width=6,
),
], className="mt-3"),
type="circle", color="#1a73e8",
),
html.Hr(),
html.H6("Best parameter combinations", className="fw-semibold mt-2 mb-2"),
dcc.Loading(html.Div(id="metrics-best-table"), type="circle", color="#1a73e8"),
], fluid=True, className="pt-3")
AVATAR_COLORS = ["#1a73e8", "#e8711a", "#1aa85c", "#8e1ae8"]
def person_card(name, email, role, departments, color="#1a73e8"):
initials = "".join(p[0].upper() for p in name.split()[:2])
return dbc.Card([
dbc.CardBody([
html.Div(initials, className="avatar-circle", style={"backgroundColor": color}),
html.H6(name, className="mb-0 fw-bold"),
html.A(email, href=f"mailto:{email}", className="text-muted small d-block mb-2"),
html.Span(role, className="badge rounded-pill mb-2",
style={"backgroundColor": color, "fontSize": "11px"}),
html.Ul([html.Li(d, className="small text-muted") for d in departments],
className="mb-0 ps-3") if departments else None,
])
], className="h-100 shadow-sm person-card border-0")
TEAM = [
dict(
name="Ricardo Almada Monter",
email="ralmadamonter@ucsd.edu",
role="Graduate Student Researcher",
departments=["Department of Chemistry & Biochemistry, UC San Diego"],
),
dict(
name="Jose Martinez Lomeli",
email="lomeli90@gmail.com",
role="Independent Researcher",
departments=[],
),
dict(
name="Erika Garay",
email="ecgaray@health.ucsd.edu",
role="Staff Scientist",
departments=[
"Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego",
],
),
dict(
name="Adrian Jinich, PhD",
email="ajinich@health.ucsd.edu",
role="Assistant Professor",
departments=[
"Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego",
"Department of Chemistry & Biochemistry, UC San Diego",
],
),
]
def contact_cards():
return dbc.Row(
[dbc.Col(person_card(**m, color=AVATAR_COLORS[i % len(AVATAR_COLORS)]), width=3)
for i, m in enumerate(TEAM)],
className="g-4",
)
def pipeline_tab():
code_block = (
"bash scripts/run_pipeline_extraction.sh \\\n"
" -s scripts/ \\\n"
" -u uniprot_tables/your_table.tsv.gz \\\n"
" -a api_keys.txt \\\n"
" -l api_keys_llama.txt \\\n"
" -e \"your@email.com\" \\\n"
" -U \"your@email.com\" \\\n"
" -j jsons/output/ \\\n"
" -o artifacts/ \\\n"
" -g norag \\\n"
" -M gpt-4.1-mini"
)
cluster_block = (
"python scripts/create_clustering_plots.py \\\n"
" -j jsons/output/your_table_purification_methods_no_rag.json \\\n"
" -o clusters/my_run \\\n"
" -m neuml/pubmedbert-base-embeddings \\\n"
" --clustering \\\n"
" -min 5 \\\n"
" -t 0.75"
)
return dbc.Container([
html.H3("How to Run the LLM Protein Purification Extraction Pipeline", className="mt-3 mb-1"),
html.P([
"Step-by-step instructions for running the automated extraction pipeline — "
"from a UniProt table to structured protein purification conditions. "
"All code is available at ",
html.A("github.com/jinichlab/llm_extractor",
href="https://github.com/jinichlab/llm_extractor",
target="_blank"),
".",
], className="text-muted mb-4"),
dbc.Accordion([
# ── Step 0: prerequisites ───────────────────────────────────────
dbc.AccordionItem(title="0 · Prerequisites", children=[
dbc.ListGroup([
dbc.ListGroupItem([html.Code("conda"), " installed (Anaconda or Miniconda)."]),
dbc.ListGroupItem([
html.Strong("OpenAI API key"), " set as environment variable:",
html.Pre("export OPENAI_API_KEY=\"sk-...\"",
className="bg-light p-2 rounded mt-1 mb-0"),
]),
dbc.ListGroupItem([
html.Strong("LlamaCloud API key"), " — sign in at ",
html.A("cloud.llamaindex.ai", href="https://cloud.llamaindex.ai",
target="_blank"),
", generate a key, and save it to ", html.Code("api_keys_llama.txt"), ".",
]),
dbc.ListGroupItem([
html.Strong("Publisher API keys"), " (Elsevier / Wiley) — save them to ",
html.Code("api_keys.txt"), " one per line:",
html.Pre("elsevier your-key\nwiley your-key",
className="bg-light p-2 rounded mt-1 mb-0"),
]),
], flush=True),
]),
# ── Step 1: install ─────────────────────────────────────────────
dbc.AccordionItem(title="1 · Install the environment", children=[
html.Pre(
"conda env create -f environment.yml\nconda activate llm_extractor_enviroment",
className="bg-light p-3 rounded mb-0",
),
]),
# ── Step 2: prepare input ───────────────────────────────────────
dbc.AccordionItem(title="2 · Prepare the UniProt input table", children=[
html.P([
"Download a UniProt table for your protein family (TSV or TSV.GZ) and place it in ",
html.Code("uniprot_tables/"),
". The table must include a ",
html.Code("PubMed ID"),
" column so the pipeline can fetch the papers.",
], className="mb-0"),
]),
# ── Step 3: run the pipeline ────────────────────────────────────
dbc.AccordionItem(title="3 · Run the full pipeline", children=[
html.P("From the repository root:", className="mb-2"),
html.Pre(code_block, className="bg-light p-3 rounded mb-3"),
dbc.Table([
html.Thead(html.Tr([html.Th("Flag"), html.Th("Required"), html.Th("Description")])),
html.Tbody([
html.Tr([html.Td(html.Code("-s")), html.Td("yes"), html.Td("Path to the scripts/ directory")]),
html.Tr([html.Td(html.Code("-u")), html.Td("yes"), html.Td("UniProt table (.tsv or .tsv.gz)")]),
html.Tr([html.Td(html.Code("-a")), html.Td("yes"), html.Td("Publisher API keys file (api_keys.txt)")]),
html.Tr([html.Td(html.Code("-l")), html.Td("yes"), html.Td("LlamaCloud API key file (api_keys_llama.txt)")]),
html.Tr([html.Td(html.Code("-e")), html.Td("yes"), html.Td("Email for NCBI Entrez")]),
html.Tr([html.Td(html.Code("-U")), html.Td("yes"), html.Td("User hint passed to the extraction step")]),
html.Tr([html.Td(html.Code("-j")), html.Td("yes"), html.Td("Output directory for all JSON files")]),
html.Tr([html.Td(html.Code("-o")), html.Td("yes"), html.Td("Output directory for PDFs and artifacts")]),
html.Tr([html.Td(html.Code("-g")), html.Td("yes"), html.Td("Extraction mode: rag or norag")]),
html.Tr([html.Td(html.Code("-m")), html.Td("no"), html.Td("Max papers to download (default: 15)")]),
html.Tr([html.Td(html.Code("-M")), html.Td("no"), html.Td("OpenAI model name (default: gpt-4.1-mini)")]),
]),
], bordered=True, size="sm", className="mb-0"),
]),
# ── Step 4: pipeline stages ─────────────────────────────────────
dbc.AccordionItem(title="4 · What the pipeline does (stages)", children=[
dbc.ListGroup([
dbc.ListGroupItem([html.Strong("1. Download papers"), " — fetches PDFs/XMLs from PubMed via paperscraper."]),
dbc.ListGroupItem([html.Strong("2. Classify papers"), " — LlamaParse decides whether each paper reports experimental enzymology."]),
dbc.ListGroupItem([html.Strong("3. Filter positives"), " — keeps only papers classified as enzymology."]),
dbc.ListGroupItem([html.Strong("4. Extract Methods sections"), " — OpenAI structured output identifies the Methods text."]),
dbc.ListGroupItem([
html.Strong("5. Extract purification conditions"), " — structured JSON with 12 fields per protein "
"(organism, strain, plasmid, inducer, buffers, etc.). ",
html.Span("norag", className="badge bg-secondary me-1"),
"sends the full Methods text; ",
html.Span("rag", className="badge bg-primary"),
" retrieves relevant chunks from a ChromaDB vector store first.",
]),
], flush=True),
]),
# ── Step 5: clustering ──────────────────────────────────────────
dbc.AccordionItem(title="5 · Run clustering (optional)", children=[
html.P(
"After extraction, embed and cluster each field with a biomedical language model. "
"Outputs are loaded by this dashboard.",
className="mb-2",
),
html.Pre(cluster_block, className="bg-light p-3 rounded mb-3"),
dbc.ListGroup([
dbc.ListGroupItem([html.Code("-t"), " — cosine similarity threshold (lower = broader clusters)"]),
dbc.ListGroupItem([html.Code("-min"), " — minimum entries to form a cluster (lower = more clusters)"]),
dbc.ListGroupItem([html.Code("-m"), " — embedding model; default ",
html.Code("neuml/pubmedbert-base-embeddings"),
" is optimised for biomedical text"]),
], flush=True),
]),
# ── Output files ────────────────────────────────────────────────
dbc.AccordionItem(title="Output files", children=[
dbc.Table([
html.Thead(html.Tr([html.Th("File"), html.Th("Description")])),
html.Tbody([
html.Tr([html.Td(html.Code("*_papers.json")), html.Td("Paper metadata and download status")]),
html.Tr([html.Td(html.Code("df_classification_*.json")),html.Td("LlamaCloud classification results")]),
html.Tr([html.Td(html.Code("filtered_*.json")), html.Td("Enzymology-positive papers only")]),
html.Tr([html.Td(html.Code("*_method_extraction.json")),html.Td("Extracted Methods sections")]),
html.Tr([html.Td(html.Code("*_purification_methods_*.json")), html.Td("Final structured purification data")]),
html.Tr([html.Td(html.Code("pdfs_*/")), html.Td("Downloaded PDF/XML files")]),
html.Tr([html.Td(html.Code("*_FIELD_CLUSTER_METRICS.csv")), html.Td("Silhouette / Davies-Bouldin scores per field")]),
html.Tr([html.Td(html.Code("*_ALL_FIELDS.csv")), html.Td("Combined clustering table across all fields")]),
]),
], bordered=True, size="sm", className="mb-0"),
]),
], start_collapsed=True, className="mb-4"),
], fluid=True, className="pt-3")
def evaluation_tab():
label_options = [{"label": lbl, "value": lbl} for lbl in _EVAL_LABELS]
return dbc.Container([
html.H3("Evaluation Results", className="mt-3 mb-1"),
html.P(
"Pipeline evaluation across protein groups and GPT models: "
"methods extraction quality (NLP metrics) and purification conditions accuracy "
"(LLM-based scoring and NLP metrics).",
className="text-muted mb-4",
),
dbc.Accordion([
# ── Section 1: Purification conditions ──────────────────────────
dbc.AccordionItem(
title="1 · Purification Conditions Evaluation",
children=[
# Controls row
dbc.Row([
dbc.Col([
html.Label("Protein group", className="fw-semibold small mb-1"),
dcc.RadioItems(
id="eval-group",
options=[
{"label": " Azoreductases", "value": "azoreductases"},
{"label": " SAMs", "value": "sams"},
],
value="azoreductases",
inline=True,
inputStyle={"marginRight": "4px"},
labelStyle={"marginRight": "16px"},
),
], width=12, md=3),
dbc.Col([
html.Label("Models / configurations", className="fw-semibold small mb-1"),
dcc.Checklist(
id="eval-model-checklist",
options=label_options,
value=_EVAL_LABELS,
inline=True,
inputStyle={"marginRight": "4px"},
labelStyle={"marginRight": "14px", "fontSize": "0.85rem"},
),
], width=12, md=9),
], className="mb-4 align-items-start"),
# Sub-tabs
dbc.Tabs([
dbc.Tab(label="LLM-based Evaluation", tab_id="eval-tab-llm", children=[
html.P(
"Mean LLM similarity score (0–10) per extraction field. "
"Higher = more similar to the ground truth. "
"Click a bar to see examples.",
className="text-muted small mt-2 mb-1",
),
dcc.Loading(
dcc.Graph(id="eval-llm-graph",
config={"displayModeBar": True, "toImageButtonOptions": {"format": "svg", "filename": "purification_conditions_llm"}},
style={"height": "460px"}),
type="circle", color="#1a73e8",
),
dcc.Loading(html.Div(id="eval-llm-examples", className="mt-3"), type="circle", color="#1a73e8"),
]),
dbc.Tab(label="NLP-based Evaluation", tab_id="eval-tab-nlp", children=[
dbc.Row([
dbc.Col([
html.Label("Metric", className="fw-semibold small mt-2 mb-1"),
dcc.Dropdown(
id="eval-nlp-metric",
options=[{"label": v, "value": k}
for k, v in _NLP_METRICS.items()],
value="bertscore_f1",
clearable=False,
),
], width=12, md=3),
], className="mb-2"),
dcc.Loading(
dcc.Graph(id="eval-nlp-graph",
config={"displayModeBar": True, "toImageButtonOptions": {"format": "svg", "filename": "purification_conditions_nlp"}},
style={"height": "460px"}),
type="circle", color="#1a73e8",
),
dcc.Loading(html.Div(id="eval-nlp-examples", className="mt-3"), type="circle", color="#1a73e8"),
]),
], id="eval-sub-tabs", active_tab="eval-tab-llm"),
],
),
# ── Section 2: Methods extraction ───────────────────────────────
dbc.AccordionItem(
title="2 · Methods Extraction Quality",
children=[
html.P(
"Average NLP metrics comparing the extracted Methods section text "
"to the curated ground truth. Click a bar to see the distribution "
"and examples.",
className="text-muted small mb-3",
),
dcc.RadioItems(
id="classif-group",
options=[
{"label": " Azoreductases", "value": "azoreductases"},
{"label": " SAMs", "value": "sams"},
],
value="azoreductases",
inline=True,
inputStyle={"marginRight": "4px"},
labelStyle={"marginRight": "16px"},
className="mb-3",
),
dcc.Loading(
dcc.Graph(id="eval-classif-graph",
config={"displayModeBar": False},
style={"height": "320px"}),
type="circle", color="#1a73e8",
),
dcc.Loading(html.Div(id="eval-classif-examples", className="mt-3"), type="circle", color="#1a73e8"),
],
),
# ── Section 3: Classification confusion matrix ───────────────────
dbc.AccordionItem(
title="3 · Classification Performance (Enzymology Detection)",
children=[
html.P(
"Confusion matrix and classification metrics for the LlamaParse "
"paper classification step (enzymology vs. non-enzymology).",
className="text-muted small mb-3",
),
dcc.RadioItems(
id="confmat-group",
options=[
{"label": " Azoreductases", "value": "azoreductases"},
{"label": " SAMs", "value": "sams"},
],
value="azoreductases",
inline=True,
inputStyle={"marginRight": "4px"},
labelStyle={"marginRight": "16px"},
className="mb-3",
),
dcc.Loading(html.Div(id="confmat-cards"), type="circle", color="#1a73e8"),
dcc.Loading(
dcc.Graph(id="confmat-heatmap",
config={"displayModeBar": False},
style={"height": "340px"}),
type="circle", color="#1a73e8",
),
dcc.Loading(html.Div(id="confmat-examples", className="mt-3"), type="circle", color="#1a73e8"),
],
),
], start_collapsed=False),
], fluid=True, className="pt-3")
def protocol_configs_tab():
_PC_FIELDS = ["expression_strain", "inducer", "medium_name",
"plasmid", "lysis_buffer", "elution_buffer"]
_PC_LABELS = {
"expression_strain": "Expression host",
"inducer": "Inducer",
"medium_name": "Growth medium",
"plasmid": "Plasmid",
"lysis_buffer": "Lysis buffer",
"elution_buffer": "Elution buffer",
}
topn_opts = [{"label": f"Top {n} clusters / field", "value": n}
for n in [10, 20, 30, 50]]
return dbc.Container([
html.H3("Protocol Configurations", className="mt-3 mb-1"),
html.P(
"Most common multi-field protocol combinations derived from BioSimCSE clusters. "
"Each line in the flow diagram represents proteins sharing the same cluster "
"assignments across fields.",
className="text-muted mb-3",
),
dbc.Row([
make_dropdown("Model", "pc-dd-model", MODELS,
value="kamalkraj__BioSimCSE-BioLinkBERT-BASE"
if "kamalkraj__BioSimCSE-BioLinkBERT-BASE" in MODELS
else (MODELS[0] if MODELS else None)),
make_dropdown("Min community size", "pc-dd-min", MINS,
value="2" if "2" in MINS else (MINS[0] if MINS else None)),
make_dropdown("Threshold", "pc-dd-threshold", THRESHOLDS,
value="0.9" if "0.9" in THRESHOLDS else (THRESHOLDS[0] if THRESHOLDS else None)),
dbc.Col([
html.Label("Clusters per field", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="pc-dd-topn",
options=topn_opts,
value=20,
clearable=False,
style={"fontSize": "13px"},
),
]),
], className="mb-3 g-2"),
dbc.Accordion([
dbc.AccordionItem(title="Filters", children=[
dbc.Row([
dbc.Col([
html.Label("Protein group", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="pc-filter-groups",
options=[{"label": g, "value": g} for g in ALL_GROUPS],
multi=True, placeholder="All groups",
style={"fontSize": "13px"},
),
], width=12, className="mb-2"),
]),
dbc.Row([
dbc.Col([
html.Label("Expression host", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="pc-filter-host",
options=[{"label": v, "value": v} for v in
["BL21(DE3)", "Rosetta", "C41/C43", "Other E. coli",
"Human/CHO", "Insect (Sf9)", "Yeast"]],
multi=True, placeholder="Any",
style={"fontSize": "13px"},
),
]),
dbc.Col([
html.Label("Inducer", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="pc-filter-inducer",
options=[{"label": v, "value": v} for v in
["IPTG", "Not reported", "Arabinose", "Other inducer"]],
multi=True, placeholder="Any",
style={"fontSize": "13px"},
),
]),
dbc.Col([
html.Label("Growth medium", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="pc-filter-medium",
options=[{"label": v, "value": v} for v in
["LB", "TB", "2xYT", "Auto-ind.", "Minimal",
"Other medium"]],
multi=True, placeholder="Any",
style={"fontSize": "13px"},
),
]),
dbc.Col([
html.Label("Plasmid", className="fw-semibold small mb-1"),
dcc.Dropdown(
id="pc-filter-plasmid",
options=[{"label": v, "value": v} for v in
["pET", "pGEX", "pQE", "pMAL",
"Other plasmid"]],
multi=True, placeholder="Any",
style={"fontSize": "13px"},
),
]),
], className="g-2"),
]),
], start_collapsed=True, className="mb-3"),
dcc.Loading(
dcc.Graph(id="pc-parcats-graph", style={"height": "560px"}),
type="circle", color="#1a73e8",
),
html.H5("Top configurations", className="mt-4 mb-2"),
html.P(
"Cluster labels are grouped into top-level categories (BL21, IPTG, LB, pET…) "
"to aggregate across fine-grained t=0.9 clusters. % is relative to all proteins with 4 core fields.",
className="text-muted small mb-2",
),
dcc.Loading(html.Div(id="pc-config-table"), type="circle", color="#1a73e8"),
], fluid=True, className="pt-3")
def contact_tab():
return dbc.Container([
html.H3("Research Team", className="mt-3 mb-4"),
contact_cards(),
], fluid=True, className="pt-3")
def readme_tab():
return dbc.Container([
html.H3("ProtoPure — Dashboard Guide", className="mt-3 mb-1"),
html.P(
"ProtoPure displays protein purification conditions extracted from the scientific "
"literature by an LLM pipeline. Use the tabs to explore the data, inspect "
"clustering results, compare models, and find instructions for running the "
"pipeline yourself. Click a section below to expand it.",
className="text-muted mb-4",
),
dbc.Accordion([
# ── Extraction Data ───────────────────────────────────────────────
dbc.AccordionItem(title="Extraction Data", children=[
html.P(
"One row per extracted protein. Each row combines the 12 structured "
"purification fields (organism, strain, plasmid, inducer, buffers, etc.) "
"with paper metadata and linked UniProt entries.",
className="mb-3",
),
html.H6("Filters", className="fw-bold"),
dbc.ListGroup([
dbc.ListGroupItem([
html.Span("Filter by group ", className="fw-semibold"),
"Restrict the table to one or more protein families "
"(azoreductases, sdrs, sams, etc.). Multiple groups can be selected simultaneously.",
]),
dbc.ListGroupItem([
html.Span("Search by / Search value ", className="fw-semibold"),
"Choose a field (PMID, Enzyme name, Organism, Expression strain, Plasmid, "
"Inducer, UniProt ID, Journal, or Paper title) and type any text. "
"Matching is case-insensitive and partial (e.g. 'coli' matches 'Escherichia coli').",
]),
dbc.ListGroupItem([
html.Span("✕ Clear ", className="fw-semibold"),
"Resets the search value and group filter, returning to the full dataset.",
]),
dbc.ListGroupItem([
html.Span("Cross-filter: field / value ", className="fw-semibold"),
"Select a purification field and one of its most common values to restrict "
"the table to proteins where that field matches. For example, set field = "
"Organism Source and value = 'Escherichia coli', then add Elution Buffer "
"to the visible columns to see all elution buffers used with E. coli proteins. "
"Combines with group and search filters.",
]),
], flush=True, className="mb-3"),
html.H6("Table", className="fw-bold mt-2"),
dbc.ListGroup([
dbc.ListGroupItem([
html.Span("Default columns ", className="fw-semibold"),
"PMID, Enzyme name, and Organism source are shown by default. "
"Use the column selector to add or hide any of the 12 extracted fields.",
]),
dbc.ListGroupItem([
html.Span("Hover ", className="fw-semibold"),
"over a truncated cell to see its full text.",
]),
dbc.ListGroupItem([
html.Span("Click a column header ", className="fw-semibold"),
"to sort ascending/descending.",
]),
dbc.ListGroupItem([
html.Span("Click a row ", className="fw-semibold"),
"to open the detail panel below the table. The panel shows four cards: "
"all extracted purification fields, linked UniProt entries (with links to uniprot.org), "
"paper metadata (title, journal, date, group badges, PubMed link, full-text link), "
"and a full conditions table for every protein in that paper.",
]),
], flush=True, className="mb-3"),
html.H6("Download CSV", className="fw-bold mt-2"),
html.P(
"Downloads the currently visible (filtered) table as a CSV. "
"Apply filters first — the download reflects exactly what is shown on screen.",
className="mb-0",
),
]),
# ── Clustering Explorer ───────────────────────────────────────────
dbc.AccordionItem(title="Clustering Explorer", children=[
html.P([
"Explore semantic clusters of extracted field values. Embeddings are computed "
"with biomedical language models; community detection groups semantically similar "
"entries into clusters. All plots are rendered natively in Plotly — hover, zoom, "
"and pan are fully interactive. ",
html.Span("Recommended settings: ", className="fw-semibold"),
"Model = BioSimCSE-BioLinkBERT-BASE, Threshold = 0.9, Min community size = 2 "
"(best mean silhouette across all fields).",
],
className="mb-3",
),
html.H6("Parameter dropdowns", className="fw-bold"),
dbc.ListGroup([
dbc.ListGroupItem([
html.Span("Model ", className="fw-semibold"),
"Sentence-embedding model used to encode field values. "
"BioSimCSE-BioLinkBERT-BASE achieves the highest mean silhouette (0.899) "
"and is selected by default.",
]),
dbc.ListGroupItem([
html.Span("Min community size ", className="fw-semibold"),
"Minimum entries required to form a cluster. Lower = more, smaller clusters.",
]),
dbc.ListGroupItem([
html.Span("Threshold ", className="fw-semibold"),
"Cosine similarity threshold for cluster membership. Higher = tighter clusters. "
"0.9 is the recommended value for BioSimCSE.",
]),
dbc.ListGroupItem([
html.Span("Field ", className="fw-semibold"),
"Which extraction field to display: enzyme name, organism, lysis buffer, "
"inducer, etc. Clustering is performed independently per field.",
]),
dbc.ListGroupItem([
html.Span("Filter by group ", className="fw-semibold"),
"Restrict the plot to entries from one or more protein families.",
]),
dbc.ListGroupItem([
html.Span("Show top N clusters ", className="fw-semibold"),
"The largest N clusters each get a distinct colour; all remaining clusters "
"are merged into a light-gray \"Other\" trace. Choose \"All\" to colour every "
"cluster individually (may be slow for large fields).",
]),
dbc.ListGroupItem([
html.Span("Cross-filter: field / value ", className="fw-semibold"),
"Restrict the plot to proteins that belong to a specific cluster in a "
"different field. For example, set cross-filter field = Organism Source "
"and value = 'Escherichia coli', then set Field = Elution Buffer — the "
"UMAP and distribution chart will show elution buffer clusters only for "
"E. coli proteins. The value dropdown is populated with the top 200 most "
"common cluster labels for the selected cross-filter field and current "
"model / threshold settings.",
]),
dbc.ListGroupItem([
html.Span("Filter by publication era ", className="fw-semibold"),
"Restrict all clusters to papers published in a specific time window "
"(approximated from PMID ranges: pre-2001 through 2021–2023). "
"Leave blank to include all time periods. "
"Use this to explore how purification practices evolved over time — "
"for example, set Field = Induction Temperature and step through eras "
"to see the shift from 37 °C to low-temperature induction.",
]),
], flush=True, className="mb-3"),
html.H6("UMAP cluster plot", className="fw-bold mt-2"),
dbc.ListGroup([
dbc.ListGroupItem(
"2D projection of all field values coloured by cluster. "
"Hover over a point to see the original text, PMID, and cluster label."
),
dbc.ListGroupItem(
"Noise points (not assigned to any cluster) are shown in light gray at low opacity."
),
dbc.ListGroupItem([
html.Span("Click a point ", className="fw-semibold"),
"to show a detail panel with that protein's full purification conditions.",
]),
], flush=True, className="mb-3"),
html.H6("Cluster distribution (bar chart)", className="fw-bold mt-2"),
dbc.ListGroup([
dbc.ListGroupItem(
"Bar chart of the top-N clusters sorted by size. "
"Quickly see which conditions are most common across the dataset."
),
dbc.ListGroupItem([
html.Span("Click a bar ", className="fw-semibold"),
"to show a table of all proteins in that cluster, with a Download CSV button.",
]),
], flush=True, className="mb-3"),
html.H6("Metrics table", className="fw-bold mt-2"),
html.P(
"Shows Silhouette score (cosine) and Davies-Bouldin index for every field "
"under the selected model / min-size / threshold combination. "
"Silhouette > 0.6 (highlighted green) indicates well-separated clusters; "
"lower Davies-Bouldin indicates more compact, better-separated clusters.",
className="mb-0",
),
]),
# ── Grid Metrics ──────────────────────────────────────────────────
dbc.AccordionItem(title="Grid Metrics", children=[
html.P(
"Interactive charts comparing clustering quality across all combinations of "
"embedding model, similarity threshold, and min community size "
"(14 models × 5 thresholds × 5 min-sizes). "
"Loaded from the pre-computed metrics CSVs at startup.",
className="mb-3",
),
html.H6("Grid Search Overview heatmap", className="fw-bold"),
html.P([
"The top heatmap shows mean silhouette score for every (model, threshold) pair. "
"Rows are sorted by best score; the best cell is marked ★. "
"Use the ",
html.Span("View by field ", className="fw-semibold"),
"dropdown to switch between a global view (mean over all fields and min-sizes) "
"and any individual extraction field (e.g. Elution Buffer, Induction Temperature). "
"The figure can be downloaded as SVG via the camera icon.",
], className="mb-3"),
html.H6("Detailed view controls", className="fw-bold"),
dbc.ListGroup([
dbc.ListGroupItem([
html.Span("Field ", className="fw-semibold"),
"Which extraction field to display metrics for.",
]),
dbc.ListGroupItem([
html.Span("Metric ", className="fw-semibold"),
html.Ul([
html.Li([html.Span("Silhouette Score ", className="fw-semibold"),
"— ranges −1 to 1; higher is better."]),
html.Li([html.Span("Davies-Bouldin Index ", className="fw-semibold"),
"— non-negative; lower is better."]),
html.Li([html.Span("Number of Clusters ", className="fw-semibold"),
"— how many clusters were found at each parameter combination."]),
], className="mb-0 mt-1"),
]),
dbc.ListGroupItem([
html.Span("Model (heatmap) ", className="fw-semibold"),
"Select the embedding model shown in the threshold × min-size heatmap below.",
]),
], flush=True, className="mb-3"),
html.H6("Detailed charts", className="fw-bold mt-2"),
dbc.ListGroup([
dbc.ListGroupItem([
html.Span("Threshold × min-size heatmap ", className="fw-semibold"),
"For the selected model and field: threshold (x) vs min community size (y) "
"coloured by the chosen metric. Hover to see exact values.",
]),
dbc.ListGroupItem([
html.Span("Model comparison bar chart ", className="fw-semibold"),
"Best metric value per model for the selected field, across all parameter combos.",
]),
dbc.ListGroupItem([
html.Span("Top-10 configurations table ", className="fw-semibold"),
"The ten parameter combinations with the best metric score for the selected field.",
]),
], flush=True),
]),
# ── Evaluation Results ────────────────────────────────────────────
dbc.AccordionItem(title="Evaluation Results", children=[
html.P(
"Pipeline evaluation across two protein groups (Azoreductases and SAMs) "
"and six GPT model configurations (gpt-4.1, gpt-5-mini, gpt-5 × no-RAG / RAG). "
"Three sections are available.",
className="mb-3",
),
html.H6("1 · Purification Conditions Evaluation", className="fw-bold"),
dbc.ListGroup([
dbc.ListGroupItem([
html.Span("LLM-based evaluation ", className="fw-semibold"),
"Mean LLM similarity score (0–10) per extraction field, with standard-error bars. "
"Click a bar to see the score distribution (box plot + histogram) and "
"the 5 worst / 5 best protein pairs for that field and model.",
]),
dbc.ListGroupItem([
html.Span("NLP-based evaluation ", className="fw-semibold"),
"Same layout using NLP metrics (BERTScore F1, ROUGE-1 F, BLEU, METEOR, "
"Cosine Similarity). All metrics normalised to 0–1. "
"Use the metric dropdown to switch between metrics. "
"Click a bar for distribution and examples.",
]),
], flush=True, className="mb-3"),
html.H6("2 · Methods Extraction Quality", className="fw-bold"),
html.P(
"Average NLP metrics comparing the extracted Methods section text to the curated "
"ground truth (Azoreductases: 31 papers; SAMs: 292 papers). "
"All metrics normalised to 0–1. "
"Click a bar to see the distribution and 5 worst / 5 best paper excerpts.",
className="mb-3",
),
html.H6("3 · Classification Performance (Enzymology Detection)", className="fw-bold"),
dbc.ListGroup([
dbc.ListGroupItem([
html.Span("Metric cards ", className="fw-semibold"),
"Precision, Recall, F1 Score, and Accuracy for the LlamaParse "
"enzymology classification step.",
]),
dbc.ListGroupItem([
html.Span("Confusion matrix ", className="fw-semibold"),
"Interactive heatmap (Actual × Predicted) with counts and percentages. "
"Click any cell (TP / FP / FN / TN) to see up to 10 example papers "
"from that category with title, date, journal, and PubMed link.",
]),
dbc.ListGroupItem([
html.Span("Azoreductases ", className="fw-semibold"),
"All 31 ground-truth papers were classified as enzymology "
"(Precision = Recall = F1 = 1.0; no negative set available).",
]),
dbc.ListGroupItem([
html.Span("SAMs ", className="fw-semibold"),
"Full 2×2 confusion matrix from 292 papers "
"(Precision ≈ 0.69, Recall ≈ 0.88, F1 ≈ 0.78).",
]),
], flush=True),
]),
# ── Extraction Pipeline Instructions ──────────────────────────────
dbc.AccordionItem(title="Extraction Pipeline Instructions", children=[
html.P([
"Step-by-step guide for running the LLM extraction pipeline locally "
"to produce your own dataset. Full details are in the ",
html.A("Extraction Pipeline Instructions",
href="#", id="readme-pipeline-link"),
" tab. Source code: ",
html.A("github.com/jinichlab/llm_extractor",
href="https://github.com/jinichlab/llm_extractor",
target="_blank"),
".",
], className="mb-3"),
dbc.ListGroup([
dbc.ListGroupItem([html.Span("0 · Prerequisites ", className="fw-semibold"),
"conda, OpenAI key, LlamaCloud key, publisher API keys."]),
dbc.ListGroupItem([html.Span("1 · Install ", className="fw-semibold"),
html.Code("conda env create -f environment.yml"),
" + ", html.Code("conda activate llm_extractor_enviroment"), "."]),
dbc.ListGroupItem([html.Span("2 · Input ", className="fw-semibold"),
"UniProt TSV with a PubMed ID column in ",
html.Code("uniprot_tables/"), "."]),
dbc.ListGroupItem([html.Span("3 · Run ", className="fw-semibold"),
html.Code("scripts/run_pipeline_extraction.sh"),
" — downloads papers, classifies them, extracts Methods, "
"and outputs structured purification JSON."]),
dbc.ListGroupItem([html.Span("4 · Cluster (optional) ", className="fw-semibold"),
html.Code("scripts/create_clustering_plots.py"),
" — embeds and clusters each field; outputs loaded by this dashboard."]),
], flush=True),
]),
# ── Contact ───────────────────────────────────────────────────────
dbc.AccordionItem(title="Contact", children=[contact_cards()]),
], start_collapsed=True, always_open=True),
], fluid=True, className="pt-3")
# ---------------------------------------------------------------------------
# App layout
# ---------------------------------------------------------------------------
app.layout = dbc.Container([
dbc.Navbar(
dbc.Container([
html.Div([
html.Span("⚗ ProtoPure", className="navbar-brand text-white"),
html.Span("LLM-Enhanced Systematic Extraction and Comparison of Protein Purification Conditions",
className="navbar-subtitle text-white"),
]),
], fluid=True),
color="#1a3a5c",
dark=True,
className="mb-3 rounded shadow-sm px-3 py-2",
),
dbc.Tabs([
dbc.Tab(label="Extraction Data", tab_id="tab-proteins"),
dbc.Tab(label="Clustering Explorer",tab_id="tab-clustering"),
dbc.Tab(label="Grid Metrics", tab_id="tab-grid-metrics"),
dbc.Tab(label="Protocol Configurations",tab_id="tab-protocol-configs"),
dbc.Tab(label="Evaluation Results", tab_id="tab-evaluation"),
dbc.Tab(label="Extraction Pipeline Instructions", tab_id="tab-pipeline"),
dbc.Tab(label="README", tab_id="tab-readme"),
dbc.Tab(label="Contact", tab_id="tab-contact"),
], id="main-tabs", active_tab="tab-proteins"),
html.Div(id="tab-content", className="mt-2"),
], fluid=True)
# ---------------------------------------------------------------------------
# Callbacks
# ---------------------------------------------------------------------------
@app.callback(Output("tab-content", "children"), Input("main-tabs", "active_tab"))
def render_tab(tab):
if tab == "tab-proteins":
return proteins_tab()
elif tab == "tab-readme":
return readme_tab()
elif tab == "tab-contact":
return contact_tab()
elif tab == "tab-clustering":
return clustering_tab()
elif tab == "tab-grid-metrics":
return grid_metrics_tab()
elif tab == "tab-protocol-configs":
return protocol_configs_tab()
elif tab == "tab-evaluation":
return evaluation_tab()
elif tab == "tab-pipeline":
return pipeline_tab()
return html.Div()
def _apply_filters(groups, search_field, search_value, cross_field=None, cross_value=None):
filtered = df.copy()
if groups:
filtered = filtered[filtered["groups"].apply(
lambda g: any(sel in g.split(", ") for sel in groups)
)]
if search_value and search_field and search_field in filtered.columns:
filtered = filtered[
filtered[search_field].astype(str).str.contains(search_value, case=False, na=False)
]
if cross_field and cross_value and cross_field in filtered.columns:
filtered = filtered[
filtered[cross_field].astype(str).str.contains(cross_value, case=False, na=False)
]
return filtered
@app.callback(
Output("table-cross-value", "options"),
Output("table-cross-value", "disabled"),
Output("table-cross-value", "value"),
Input("table-cross-field", "value"),
)
def populate_table_cross_values(cross_field):
if not cross_field or cross_field not in df.columns:
return [], True, None
vals = (
df[cross_field].dropna().astype(str)
.loc[lambda s: s.str.strip() != ""]
.value_counts()
.head(200)
.index.tolist()
)
return [{"label": v, "value": v} for v in vals], False, None
@app.callback(
Output("protein-table", "data"),
Output("protein-table", "hidden_columns"),
Output("protein-table", "tooltip_data"),
Output("protein-count", "children"),
Input("filter-group", "value"),
Input("search-field", "value"),
Input("search-value", "value"),
Input("col-selector", "value"),
Input("table-cross-field", "value"),
Input("table-cross-value", "value"),
)
def filter_proteins(groups, search_field, search_value, selected_cols, cross_field, cross_value):
filtered = _apply_filters(groups, search_field, search_value, cross_field, cross_value)
cols = selected_cols if selected_cols else DEFAULT_COLS
hidden = [c for c in TABLE_FIELDS if c not in cols]
records = filtered[TABLE_FIELDS].to_dict("records")
tooltips = [
{c: {"value": str(row.get(c, "")), "type": "markdown"} for c in cols}
for row in records
]
label = f"Showing {len(filtered):,} of {len(df):,} proteins"
return records, hidden, tooltips, label
@app.callback(
Output("detail-panel", "children"),
Input("protein-table", "selected_rows"),
Input("filter-group", "value"),
Input("search-field", "value"),
Input("search-value", "value"),
Input("table-cross-field", "value"),
Input("table-cross-value", "value"),
prevent_initial_call=True,
)
def show_detail(selected_rows, groups, search_field, search_value, cross_field, cross_value):
if not selected_rows:
return html.Div()
filtered = _apply_filters(groups, search_field, search_value, cross_field, cross_value)
clicked = selected_rows[0]
if clicked >= len(filtered):
return dash.no_update
row = filtered.iloc[clicked].to_dict()
pmid = str(row.get("pmid", ""))
entry = RAW.get(pmid, {})
uniprot_ids = entry.get("Uniprot_IDS", []) or []
protein_names = entry.get("Protein_names", []) or []
organisms = entry.get("Organisms", []) or []
sequences = entry.get("Sequences", []) or []
# --- Extracted protein card ---
extraction_items = []
for field in EXTRACTION_FIELDS[1:]: # skip pmid
val = row.get(field, "")
if val:
item = detail_field(field.replace("_", " ").title(), val)
if item:
extraction_items.append(item)
# Paper metadata from META
m = META.get(str(pmid), {})
pubmed_link = html.A(
f"PubMed: {pmid}",
href=f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/",
target="_blank",
className="small",
)
# --- Extracted protein card ---
extracted_card = dbc.Card([
dbc.CardHeader(html.Span("Extracted Protein", className="fw-bold")),
dbc.CardBody(extraction_items or [html.Span("No extraction data.", className="text-muted small")]),
], className="mb-3", color="light")
# --- Paper metadata card ---
paper_links = [pubmed_link]
if m.get("url"):
paper_links += [
html.Span(" · ", className="text-muted mx-1"),
html.A("Full text", href=m["url"], target="_blank", className="small"),
]
paper_body = []
if m.get("title"):
paper_body.append(html.P(m["title"], className="fw-semibold small mb-2"))
meta_line = " · ".join(filter(None, [m.get("source", ""), m.get("pub_date", "")]))
if meta_line:
paper_body.append(html.P(meta_line, className="text-muted small mb-1"))
if m.get("groups"):
paper_body.append(html.P(
[html.Span("Groups: ", className="fw-semibold")] +
[dbc.Badge(g, color="primary", className="me-1") for g in m["groups"]],
className="mb-0"
))
paper_card = dbc.Card([
dbc.CardHeader(html.Div(paper_links)),
dbc.CardBody(paper_body or [html.Span("No paper metadata available.", className="text-muted small")]),
], className="mb-3")
# --- UniProt entries card ---
if uniprot_ids:
uniprot_rows = []
for i, uid in enumerate(uniprot_ids):
name = protein_names[i] if i < len(protein_names) else "—"
org = organisms[i] if i < len(organisms) else "—"
seq = sequences[i] if i < len(sequences) else None
seq_info = f"{len(seq)} aa" if seq else "—"
uniprot_rows.append(
dbc.ListGroupItem([
dbc.Row([
dbc.Col([
html.A(uid,
href=f"https://www.uniprot.org/uniprot/{uid}",
target="_blank",
className="fw-bold small me-2"),
html.Span(org, className="text-muted small"),
], width=4),
dbc.Col(html.Span(name, className="small"), width=6),
dbc.Col(html.Span(seq_info, className="text-muted small"), width=2),
], align="center"),
])
)
uniprot_card = dbc.Card([
dbc.CardHeader(html.Span(
f"UniProt Entries for PMID {pmid} ({len(uniprot_ids)} entries)",
className="fw-bold",
)),
dbc.CardBody([
dbc.Row([
dbc.Col(html.Span("UniProt ID / Organism", className="fw-semibold small"), width=4),
dbc.Col(html.Span("Protein Name", className="fw-semibold small"), width=6),
dbc.Col(html.Span("Sequence", className="fw-semibold small"), width=2),
], className="px-3 mb-1"),
dbc.ListGroup(uniprot_rows, flush=True),
]),
], className="mb-3")
else:
uniprot_card = dbc.Card([
dbc.CardHeader("UniProt Entries"),
dbc.CardBody(html.Span("No UniProt entries linked to this PMID.", className="text-muted small")),
], className="mb-3", color="light")
# --- All proteins from this PMID (conditions table) ---
all_proteins = entry.get("proteins", []) or []
CONDITION_COLS = [
"enzyme_name", "organism_source", "strain", "expression_strain",
"plasmid", "molecular_weight", "medium_name", "inducer",
"induction_temperature", "lysis_buffer", "elution_buffer", "desalting_process",
]
cond_records = [
{c: (p.get(c) or "") for c in CONDITION_COLS}
for p in all_proteins
]
# Mark the selected protein by matching enzyme_name + organism_source
sel_idx = next(
(i for i, p in enumerate(all_proteins)
if str(p.get("enzyme_name", "") or "") == str(row.get("enzyme_name", "") or "")
and str(p.get("organism_source", "") or "") == str(row.get("organism_source", "") or "")),
None,
)
cond_col_defs = [
{"name": c.replace("_", " ").title(), "id": c} for c in CONDITION_COLS
]
conditions_card = dbc.Card([
dbc.CardHeader(
html.Span(
f"All purification conditions from PMID {pmid} ({len(all_proteins)} protein{'s' if len(all_proteins) != 1 else ''})",
className="fw-bold",
)
),
dbc.CardBody(
dash_table.DataTable(
columns=cond_col_defs,
data=cond_records,
page_size=10,
sort_action="native",
style_table={"overflowX": "auto"},
style_cell={
"fontSize": "12px",
"padding": "5px 10px",
"textAlign": "left",
"maxWidth": "260px",
"overflow": "hidden",
"textOverflow": "ellipsis",
"whiteSpace": "nowrap",
},
style_header={
"fontWeight": "700",
"backgroundColor": "#1a3a5c",
"color": "#ffffff",
"fontSize": "11px",
"textTransform": "uppercase",
"letterSpacing": "0.04em",
},
style_data_conditional=(
[{"if": {"row_index": "odd"}, "backgroundColor": "#eef4fd"}] +
([{"if": {"row_index": sel_idx}, "backgroundColor": "#cfe2ff",
"border": "1px solid #9ec5fe"}] if sel_idx is not None else [])
),
tooltip_data=[
{c: {"value": str(r.get(c, "")), "type": "markdown"} for c in CONDITION_COLS}
for r in cond_records
],
tooltip_delay=0,
tooltip_duration=None,
) if cond_records else html.Span("No protein conditions available.", className="text-muted small")
),
], className="mb-3")
return html.Div([
html.Hr(),
html.H6("Selected Row Detail", className="fw-semibold mb-3"),
dbc.Row([
dbc.Col(extracted_card, width=5),
dbc.Col(uniprot_card, width=7),
]),
dbc.Row([
dbc.Col(paper_card, width=12),
]),
dbc.Row([
dbc.Col(conditions_card, width=12),
]),
])
# Qualitative color palette — high-contrast, colorblind-friendly base
_PALETTE = [
"#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd",
"#8c564b", "#e377c2", "#17becf", "#bcbd22", "#393b79",
"#637939", "#8c6d31", "#843c39", "#7b4173", "#3182bd",
"#e6550d", "#31a354", "#756bb1", "#636363", "#6baed6",
]
_NOISE_COLOR = "#c0c0c0"
def _uniprot_card(pmid):
"""Build a UniProt entries card for a given PMID."""
entry = RAW.get(str(pmid), {})
uniprot_ids = entry.get("Uniprot_IDS", []) or []
protein_names = entry.get("Protein_names", []) or []
organisms = entry.get("Organisms", []) or []
sequences = entry.get("Sequences", []) or []
if not uniprot_ids:
return dbc.Card([
dbc.CardHeader(html.Span("UniProt Entries", className="fw-bold")),
dbc.CardBody(html.Span("No UniProt entries linked to this PMID.",
className="text-muted small")),
], className="mb-3", color="light")
rows = []
for i, uid in enumerate(uniprot_ids):
name = protein_names[i] if i < len(protein_names) else "—"
org = organisms[i] if i < len(organisms) else "—"
seq = sequences[i] if i < len(sequences) else None
seq_info = f"{len(seq)} aa" if seq else "—"
rows.append(dbc.ListGroupItem([
dbc.Row([
dbc.Col([
html.A(uid, href=f"https://www.uniprot.org/uniprot/{uid}",
target="_blank", className="fw-bold small me-2"),
html.Span(org, className="text-muted small"),
], width=4),
dbc.Col(html.Span(name, className="small"), width=6),
dbc.Col(html.Span(seq_info, className="text-muted small"), width=2),
], align="center"),
]))
return dbc.Card([
dbc.CardHeader(html.Span(
f"UniProt Entries for PMID {pmid} ({len(uniprot_ids)} entries)",
className="fw-bold",
)),
dbc.CardBody([
dbc.Row([
dbc.Col(html.Span("UniProt ID / Organism", className="fw-semibold small"), width=4),
dbc.Col(html.Span("Protein Name", className="fw-semibold small"), width=6),
dbc.Col(html.Span("Sequence", className="fw-semibold small"), width=2),
], className="px-3 mb-1"),
dbc.ListGroup(rows, flush=True),
]),
], className="mb-3")
_HF_REPO = "richiam/ProtoPure"
_HF_APP_ROOT = "/app"
def _resolve_xet_file(local_path):
"""If local_path is an HF Xet pointer, download actual content and overwrite it."""
try:
with open(local_path, "rb") as f:
magic = f.read(2)
if magic == b"\x1f\x8b":
return # already proper gzip
rel = os.path.relpath(local_path, _HF_APP_ROOT)
print(f"[xet] Downloading {rel} from HF Hub...", flush=True)
from huggingface_hub import hf_hub_download
import shutil
actual = hf_hub_download(repo_id=_HF_REPO, filename=rel, repo_type="space")
shutil.copy2(actual, local_path)
print(f"[xet] Cached {rel}", flush=True)
except Exception as e:
print(f"[xet] Failed to resolve {local_path}: {e}", flush=True)
def _load_cluster_csv(model, min_val, threshold):
"""Return the ALL_FIELDS dataframe or None. Supports .csv.gz and .csv."""
base = os.path.join(GRID_BASE, f"model={model}", f"min={min_val}",
f"t={threshold}_ALL_FIELDS")
for ext in (".csv.gz", ".csv"):
path = base + ext
if not os.path.isfile(path):
continue
if ext == ".csv.gz":
_resolve_xet_file(path)
try:
return pd.read_csv(path, compression="gzip" if ext == ".csv.gz" else None)
except Exception:
try:
return pd.read_csv(path, compression=None)
except Exception:
continue
return None
def _umap_figure(cdf, field_name, top_n=20):
"""Build a UMAP scatter figure from a filtered cluster DataFrame."""
cdf = cdf.reset_index(drop=True)
noise_mask = cdf["cluster_id"] == -1
non_noise = cdf[~noise_mask]
# Rank clusters by size; optionally cap at top_n
cluster_sizes = non_noise.groupby("cluster_id").size().sort_values(ascending=False)
if top_n and top_n > 0:
top_ids = set(cluster_sizes.index[:top_n])
else:
top_ids = set(cluster_sizes.index)
fig = go.Figure()
# NOISE — thin gray, low opacity, drawn first
if noise_mask.any():
nd = cdf[noise_mask]
fig.add_trace(go.Scattergl(
x=nd["x"], y=nd["y"],
mode="markers",
name="Noise",
marker=dict(color=_NOISE_COLOR, size=4, opacity=0.25),
hovertemplate="Noise
%{customdata[0]}
PMID: %{text}",
customdata=list(zip(nd["value"].str[:80].tolist(),
nd["protein_index"].astype(str).tolist())),
text=nd["key"].astype(str),
showlegend=True,
))
# "Other clusters" bucket — light gray, slightly more visible than noise
other_mask = ~noise_mask & ~cdf["cluster_id"].isin(top_ids)
if other_mask.any():
od = cdf[other_mask]
fig.add_trace(go.Scattergl(
x=od["x"], y=od["y"],
mode="markers",
name=f"Other ({len(cluster_sizes) - len(top_ids)} clusters)",
marker=dict(color="#adb5bd", size=5, opacity=0.35),
hovertemplate="%{customdata[0]}
%{customdata[2]}
PMID: %{text}",
customdata=list(zip(
("Cluster " + od["cluster_id"].astype(str)).tolist(),
od["protein_index"].astype(str).tolist(),
od["value"].str[:80].tolist(),
)),
text=od["key"].astype(str),
showlegend=True,
))
# Top-N named clusters — distinct colors, larger markers
for rank, cid in enumerate(cluster_sizes.index[:len(top_ids)]):
cd = non_noise[non_noise["cluster_id"] == cid]
label = cd["cluster_label_short"].iloc[0][:35] if len(cd) else f"C{cid}"
color = _PALETTE[rank % len(_PALETTE)]
fig.add_trace(go.Scattergl(
x=cd["x"], y=cd["y"],
mode="markers",
name=f"[{cid}] {label}",
marker=dict(color=color, size=7, opacity=0.80,
line=dict(width=0.4, color="rgba(255,255,255,0.6)")),
hovertemplate=(
"[%{meta}] %{customdata[0]}
"
"%{customdata[2]}
"
"PMID: %{text}"
),
meta=cid,
customdata=list(zip(
cd["cluster_label_short"].str[:50].tolist(),
cd["protein_index"].astype(str).tolist(),
cd["value"].str[:100].tolist(),
)),
text=cd["key"].astype(str),
))
n_total = len(cluster_sizes)
n_shown = len(top_ids)
n_noise = noise_mask.sum()
title_txt = (
f"UMAP — {field_name.replace('_', ' ').title()}"
f" · top {n_shown}/{n_total} clusters shown · {n_noise:,} noise pts"
)
fig.update_layout(
title=dict(text=title_txt, font=dict(size=13)),
plot_bgcolor="#f9fafc",
paper_bgcolor="#ffffff",
xaxis=dict(showgrid=False, zeroline=False, showticklabels=False, title=""),
yaxis=dict(showgrid=False, zeroline=False, showticklabels=False, title=""),
legend=dict(
title=dict(text="Cluster", font=dict(size=11)),
font=dict(size=10),
itemsizing="constant",
bordercolor="#dee2e6", borderwidth=1,
tracegroupgap=1,
),
updatemenus=[dict(
type="buttons",
showactive=False,
direction="right",
x=1.01, xanchor="left",
y=1.06, yanchor="top",
pad={"r": 4, "t": 0},
bgcolor="#f8f9fa",
bordercolor="#ced4da",
font=dict(size=11),
buttons=[
dict(
label="Deselect all",
method="restyle",
args=[{"visible": "legendonly"}],
),
dict(
label="Select all",
method="restyle",
args=[{"visible": True}],
),
],
)],
margin=dict(l=20, r=200, t=50, b=20),
hoverlabel=dict(bgcolor="white", font_size=12, namelength=-1),
)
return fig
def _distribution_figure(cdf, field_name, top_n=20):
"""Build a cluster-size bar chart from a filtered cluster DataFrame."""
cdf = cdf.reset_index(drop=True)
counts = (
cdf[cdf["cluster_id"] != -1]
.groupby(["cluster_id", "cluster_label_short"], sort=False)
.size()
.reset_index(name="count")
.sort_values("count", ascending=False)
)
if top_n and top_n > 0:
counts = counts.head(top_n)
colors = [_PALETTE[i % len(_PALETTE)] for i in range(len(counts))]
labels = counts["cluster_label_short"].str[:45]
fig = go.Figure(go.Bar(
x=labels,
y=counts["count"],
marker_color=colors,
marker_line_color="rgba(255,255,255,0.6)",
marker_line_width=0.8,
opacity=0.88,
customdata=counts["cluster_id"].tolist(),
hovertemplate="%{x}
Count: %{y:,}
Click to see proteins",
))
fig.update_layout(
title=dict(
text=f"Cluster Sizes — {field_name.replace('_', ' ').title()}"
+ (f" (top {top_n})" if top_n else ""),
font=dict(size=13),
),
plot_bgcolor="#f9fafc",
paper_bgcolor="#ffffff",
xaxis=dict(
showgrid=False, zeroline=False,
tickangle=-45, tickfont=dict(size=10),
),
yaxis=dict(
showgrid=True, gridcolor="#e5e7eb", zeroline=False,
title="# entries",
),
margin=dict(l=50, r=20, t=50, b=160),
hoverlabel=dict(bgcolor="white", font_size=12),
bargap=0.25,
)
return fig
@app.callback(
Output("dd-cross-value", "options"),
Output("dd-cross-value", "disabled"),
Output("dd-cross-value", "value"),
Input("dd-cross-field", "value"),
Input("dd-model", "value"),
Input("dd-min", "value"),
Input("dd-threshold", "value"),
)
def populate_cross_filter_values(cross_field, model, min_val, threshold):
if not cross_field or not all([model, min_val, threshold]):
return [], True, None
cdf = _load_cluster_csv(model, min_val, threshold)
if cdf is None:
return [], True, None
labels = (
cdf[(cdf["field"] == cross_field) & (cdf["cluster_id"] != -1)]["cluster_label"]
.value_counts()
.head(200)
.index.tolist()
)
options = [{"label": l, "value": l} for l in labels]
return options, False, None
def _compute_diversity_cards(field_df):
"""Return a dbc.Row of diversity metric cards from a filtered field DataFrame."""
n_total = len(field_df)
if n_total == 0:
return html.Div()
clustered = field_df[field_df["cluster_id"] != -1]
n_clustered = len(clustered)
coverage = n_clustered / n_total if n_total > 0 else 0.0
if n_clustered == 0:
return _diversity_row(n_total, 0, 0, 0.0, 0.0, coverage, 0.0)
sizes = clustered["cluster_id"].value_counts()
n_clusters = len(sizes)
p = sizes / n_clustered
entropy = -sum(pi * math.log(pi) for pi in p if pi > 0)
effective_n = math.exp(entropy) if entropy > 0 else 1.0
dominance = sizes.iloc[0] / n_clustered # sizes is sorted descending
return _diversity_row(n_total, n_clustered, n_clusters, entropy, effective_n, coverage, dominance)
def _diversity_row(n_total, n_clustered, n_clusters, entropy, effective_n, coverage, dominance):
def _card(label, value, tooltip, color="#1a3a5c"):
return dbc.Col(
dbc.Card(
dbc.CardBody([
html.Div(label, className="text-muted mb-1",
style={"fontSize": "11px", "textTransform": "uppercase",
"letterSpacing": "0.05em"}),
html.Div(value, className="fw-bold",
style={"fontSize": "20px", "color": color}),
html.Div(tooltip, className="text-muted",
style={"fontSize": "10px", "lineHeight": "1.3"}),
], className="p-2 text-center"),
className="border-0 shadow-sm h-100",
),
xs=6, sm=4, md=2,
)
return dbc.Row([
_card("Entries", f"{n_total:,}",
"total in field after filters"),
_card("Clusters", f"{n_clusters:,}",
"distinct clusters (excl. noise)"),
_card("Coverage", f"{coverage:.1%}",
"assigned to a cluster (not noise)",
"#198754" if coverage >= 0.7 else "#dc3545"),
_card("Shannon H", f"{entropy:.2f}",
"entropy of cluster-size distribution"),
_card("Effective N", f"{effective_n:.1f}",
"exp(H) — diversity-adjusted cluster count"),
_card("Dominance", f"{dominance:.1%}",
"entries in the single largest cluster",
"#dc3545" if dominance >= 0.5 else "#1a3a5c"),
], className="g-2")
@app.callback(
Output("cluster-graph", "figure"),
Output("plot-status", "children"),
Output("diversity-cards", "children"),
Input("dd-model", "value"),
Input("dd-min", "value"),
Input("dd-threshold", "value"),
Input("dd-field", "value"),
Input("plot-type", "value"),
Input("top-n-clusters", "value"),
Input("cluster-group-filter", "value"),
Input("dd-cross-field", "value"),
Input("dd-cross-value", "value"),
Input("dd-era-filter", "value"),
)
def update_cluster_plot(model, min_val, threshold, field, plot_type, top_n, groups, cross_field, cross_value, era):
empty_fig = go.Figure()
empty_fig.update_layout(paper_bgcolor="#ffffff", plot_bgcolor="#f9fafc")
no_cards = html.Div()
if not all([model, min_val, threshold, field, plot_type]):
return empty_fig, "Select all parameters above.", no_cards
cdf = _load_cluster_csv(model, min_val, threshold)
if cdf is None:
return empty_fig, f"Data file not found for model={model} min={min_val} t={threshold}", no_cards
# Apply era filter based on PMID as publication year proxy
if era:
era_range = {label: (lo, hi) for label, lo, hi in PMID_ERA_BINS}
if era in era_range:
lo, hi = era_range[era]
pmid_num = pd.to_numeric(cdf["key"], errors="coerce")
cdf = cdf[(pmid_num >= lo) & (pmid_num < hi)]
if cdf.empty:
return empty_fig, f"No data found for era '{era}'.", no_cards
# Apply cross-field filter: keep only (key, protein_index) in the selected cross-field cluster
if cross_field and cross_value:
keep = cdf[(cdf["field"] == cross_field) & (cdf["cluster_label"] == cross_value)][["key", "protein_index"]]
if keep.empty:
return empty_fig, f"No proteins found for {cross_field} = '{cross_value}'.", no_cards
cdf = cdf.merge(keep, on=["key", "protein_index"], how="inner")
field_df = cdf[cdf["field"] == field].copy()
if field_df.empty:
return empty_fig, f"No data for field '{field}' in this parameter combination.", no_cards
# Filter by group if selected
if groups:
sel = set(groups)
field_df = field_df[
field_df["key"].astype(str).apply(
lambda pmid: bool(PMID_GROUPS.get(pmid, set()) & sel)
)
]
if field_df.empty:
return empty_fig, f"No data for the selected group(s) in this field.", no_cards
diversity = _compute_diversity_cards(field_df)
n = top_n or 0
suffix_parts = []
if era:
suffix_parts.append(era)
if groups:
suffix_parts.append(", ".join(groups))
if cross_field and cross_value:
suffix_parts.append(f"{cross_field.replace('_',' ')}={cross_value}")
suffix = f" — {' | '.join(suffix_parts)}" if suffix_parts else ""
if plot_type == "cluster":
return _umap_figure(field_df, field + suffix, top_n=n), "", diversity
else:
return _distribution_figure(field_df, field + suffix, top_n=n), "", diversity
_CONDITION_COLS = [
"enzyme_name", "organism_source", "strain", "expression_strain",
"plasmid", "molecular_weight", "medium_name", "inducer",
"induction_temperature", "lysis_buffer", "elution_buffer", "desalting_process",
]
@app.callback(
Output("cluster-point-detail", "children"),
Output("cluster-table-store", "data"),
Input("cluster-graph", "clickData"),
State("dd-field", "value"),
State("dd-model", "value"),
State("dd-min", "value"),
State("dd-threshold", "value"),
State("plot-type", "value"),
)
def show_cluster_point_detail(click_data, field, model, min_val, threshold, plot_type):
if not click_data:
return dash.no_update, dash.no_update
point = click_data["points"][0]
# ── Bar chart click: show all proteins in that cluster ──────────────────
if plot_type == "distribution":
cluster_id = point.get("customdata")
cluster_label = str(point.get("x", ""))
count = point.get("y", 0)
cdf = _load_cluster_csv(model, min_val, threshold)
if cdf is None:
return html.Div("Could not load cluster data.", className="text-muted small mt-2"), None
members = cdf[(cdf["field"] == field) & (cdf["cluster_id"] == cluster_id)]
rows = []
for _, r in members.iterrows():
pmid = str(r["key"])
pidx = int(r["protein_index"])
entry = RAW.get(pmid, {})
proteins = entry.get("proteins", []) or []
protein = proteins[pidx] if pidx < len(proteins) else {}
m = META.get(pmid, {})
row = {"pmid": pmid}
row.update({c: str(protein.get(c) or "") for c in _CONDITION_COLS})
row["field_value"] = str(r.get("value", ""))
row["journal"] = m.get("source", "")
row["pub_date"] = m.get("pub_date", "")
rows.append(row)
# Column order: pmid, enzyme_name, organism_source, [clustered field], rest, journal, pub_date
fixed = ["pmid", "enzyme_name", "organism_source"]
field_col = "field_value"
remaining = [c for c in _CONDITION_COLS if c not in fixed and c != field]
table_cols = fixed + [field_col] + remaining + ["journal", "pub_date"]
field_label = field.replace("_", " ").title()
col_defs = [
{"name": (field_label if c == field_col else c.replace("_", " ").title()), "id": c}
for c in table_cols
]
return html.Div([
dbc.Card([
dbc.CardHeader(
dbc.Row([
dbc.Col([
html.Span("Proteins in cluster — ", className="fw-bold"),
html.Span(f'"{cluster_label}"', className="fst-italic"),
dbc.Badge(f"{count} proteins", color="primary", className="ms-2"),
], width=10),
dbc.Col(
dbc.Button("⬇ Download CSV", id="download-cluster-btn",
size="sm", color="success", outline=True),
width=2, className="text-end",
),
], align="center"),
),
dbc.CardBody(
dash_table.DataTable(
columns=col_defs,
data=rows,
page_size=15,
sort_action="native",
style_table={"overflowX": "auto"},
style_cell={
"fontSize": "12px",
"padding": "5px 10px",
"textAlign": "left",
"maxWidth": "260px",
"overflow": "hidden",
"textOverflow": "ellipsis",
"whiteSpace": "nowrap",
},
style_cell_conditional=[
{"if": {"column_id": field_col},
"backgroundColor": "#fff8e1", "fontWeight": "500"},
],
style_header={
"fontWeight": "700",
"backgroundColor": "#1a3a5c",
"color": "#ffffff",
"fontSize": "11px",
"textTransform": "uppercase",
"letterSpacing": "0.04em",
},
style_data_conditional=[
{"if": {"row_index": "odd"}, "backgroundColor": "#eef4fd"},
],
tooltip_data=[
{c: {"value": str(r.get(c, "")), "type": "markdown"} for c in table_cols}
for r in rows
],
tooltip_delay=0,
tooltip_duration=None,
) if rows else html.Span("No proteins found.", className="text-muted small")
),
], className="mb-3"),
]), rows
# ── UMAP scatter click: show single protein conditions ──────────────────
pmid = str(point.get("text", ""))
customdata = point.get("customdata", [])
# customdata layout: [label_or_value, protein_index, value_text] (noise: [value, protein_index])
try:
protein_index = int(customdata[1])
except (IndexError, ValueError, TypeError):
protein_index = 0
entry = RAW.get(pmid, {})
proteins = entry.get("proteins", []) or []
if not proteins:
return html.Div(f"No protein data for PMID {pmid}.", className="text-muted small mt-2")
protein_index = min(protein_index, len(proteins) - 1)
protein = proteins[protein_index]
# Field value that was clicked (used for context header)
clicked_value = str(customdata[0] if customdata else "")
# Conditions table: condition → value, skip empty
cond_rows = [
{"Condition": c.replace("_", " ").title(), "Value": str(protein.get(c) or "")}
for c in _CONDITION_COLS
if protein.get(c)
]
m = META.get(pmid, {})
pubmed_link = html.A(f"PMID {pmid}", href=f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/",
target="_blank", className="small")
paper_info = " · ".join(filter(None, [m.get("source", ""), m.get("pub_date", "")]))
return html.Div([
dbc.Card([
dbc.CardHeader([
html.Span("Purification Conditions — ", className="fw-bold"),
pubmed_link,
html.Span(f" · {paper_info}", className="text-muted small") if paper_info else None,
html.Span(f" · {field.replace('_', ' ').title()}: ", className="text-muted small ms-2"),
html.Span(f'"{clicked_value[:80]}"', className="small fst-italic"),
]),
dbc.CardBody(
dash_table.DataTable(
columns=[{"name": c, "id": c} for c in ["Condition", "Value"]],
data=cond_rows,
style_table={"overflowX": "auto"},
style_cell={
"fontSize": "12px",
"padding": "5px 10px",
"textAlign": "left",
},
style_cell_conditional=[
{"if": {"column_id": "Condition"},
"fontWeight": "600", "width": "200px", "minWidth": "200px",
"backgroundColor": "#f8f9fa"},
{"if": {"column_id": "Value"},
"whiteSpace": "normal", "height": "auto"},
],
style_header={
"fontWeight": "700",
"backgroundColor": "#1a3a5c",
"color": "#ffffff",
"fontSize": "11px",
"textTransform": "uppercase",
"letterSpacing": "0.04em",
},
style_data_conditional=[
{"if": {"row_index": "odd"}, "backgroundColor": "#eef4fd"},
],
) if cond_rows else html.Span("No conditions recorded for this protein.", className="text-muted small")
),
], className="mb-3"),
_uniprot_card(pmid),
]), None
@app.callback(
Output("download-cluster-csv", "data"),
Input("download-cluster-btn", "n_clicks"),
State("cluster-table-store", "data"),
prevent_initial_call=True,
)
def download_cluster_table(n_clicks, rows):
if not n_clicks or not rows:
return None
return dcc.send_data_frame(pd.DataFrame(rows).to_csv, "cluster_proteins.csv", index=False)
@app.callback(
Output("metrics-summary-graph", "figure"),
Input("dd-summary-metric", "value"),
Input("dd-summary-field", "value"),
Input("dd-summary-minsize", "value"),
)
def update_summary_graph(metric, field, min_size):
return _build_grid_summary_figure(field, min_size, metric)
@app.callback(
Output("metrics-table-container", "children"),
Input("dd-model", "value"),
Input("dd-min", "value"),
Input("dd-threshold", "value"),
)
def update_metrics_table(model, min_val, threshold):
if not all([model, min_val, threshold]):
return html.Div()
csv_path = os.path.join(
GRID_BASE, f"model={model}", f"min={min_val}", f"t={threshold}_FIELD_CLUSTER_METRICS.csv"
)
if not os.path.isfile(csv_path):
return html.Div("Metrics file not found.", className="text-muted small")
metrics_df = pd.read_csv(csv_path).round(4)
return dash_table.DataTable(
columns=[{"name": c.replace("_", " ").title(), "id": c} for c in metrics_df.columns],
data=metrics_df.to_dict("records"),
sort_action="native",
style_table={"overflowX": "auto"},
style_cell={"fontSize": "12px", "padding": "5px 10px", "textAlign": "left"},
style_header={"fontWeight": "bold", "backgroundColor": "#f8f9fa"},
style_data_conditional=[
{"if": {"row_index": "odd"}, "backgroundColor": "#f8f9fa"},
{
"if": {"filter_query": "{silhouette_cosine} > 0.6", "column_id": "silhouette_cosine"},
"color": "#198754", "fontWeight": "bold",
},
{
"if": {"filter_query": "{silhouette_cosine} < 0.3", "column_id": "silhouette_cosine"},
"color": "#dc3545",
},
],
)
_PC_PROTOCOL_FIELDS = ["expression_strain", "inducer", "medium_name",
"plasmid", "lysis_buffer", "elution_buffer"]
_PC_FIELD_LABELS = {
"expression_strain": "Expression host",
"inducer": "Inducer",
"medium_name": "Growth medium",
"plasmid": "Plasmid",
"lysis_buffer": "Lysis buffer",
"elution_buffer": "Elution buffer",
}
def _grp(field, label):
"""Map a cluster label_short to a readable top-level category."""
if not label or label == "Other clusters":
return None
sl = str(label).lower()
if field == "expression_strain":
if "bl21" in sl: return "BL21(DE3)"
if "rosetta" in sl: return "Rosetta"
if "c41" in sl or "c43" in sl: return "C41/C43"
if "hek" in sl or "cho" in sl or "293" in sl: return "Human/CHO"
if "sf9" in sl or "sf21" in sl: return "Insect (Sf9)"
if "yeast" in sl or "pichia" in sl: return "Yeast"
return "Other E. coli"
if field == "inducer":
if "not mentioned" in sl: return "Not reported"
if "iptg" in sl: return "IPTG"
if "arabinose" in sl: return "Arabinose"
return "Other inducer"
if field == "medium_name":
if re.search(r"\blb\b|luria.bertani|luria broth", sl): return "LB"
if "terrific" in sl: return "TB"
if "2xyt" in sl or "2x yt" in sl: return "2xYT"
if "minimal" in sl or "m9" in sl: return "Minimal"
return "Other medium"
if field == "plasmid":
if re.search(r"\bpet", sl): return "pET"
if "pgex" in sl: return "pGEX"
if "pqe" in sl: return "pQE"
if "pmal" in sl: return "pMAL"
return "Other plasmid"
if field == "lysis_buffer":
if re.search(r"\btris\b", sl): return "Tris"
if "pbs" in sl: return "PBS"
if "hepes" in sl: return "HEPES"
if re.search(r"\bphosphate\b", sl): return "Phosphate"
return "Other lysis"
if field == "elution_buffer":
if "imidazole" in sl: return "Imidazole"
if "glutathione" in sl: return "Glutathione"
if "maltose" in sl: return "Maltose"
return "Other elution"
return label
_PC_COLORS = [
"#1a73e8", "#4db8ff", "#80ccff", "#1aa85c", "#f4a55a",
"#e8711a", "#c0392b", "#8e1ae8", "#2ecc71", "#adb5bd",
"#dee2e6",
]
def _pc_build_wide(all_fields_df, top_n):
"""Pivot ALL_FIELDS into wide format, keeping top_n clusters per field."""
sub = all_fields_df[all_fields_df["field"].isin(_PC_PROTOCOL_FIELDS)].copy()
rows = []
for field in _PC_PROTOCOL_FIELDS:
fd = sub[sub["field"] == field]
clustered = fd[fd["cluster_id"] != -1]
# Top N clusters by size
top_ids = (
clustered.groupby("cluster_id").size()
.sort_values(ascending=False)
.head(top_n).index
)
def label_row(r):
if r["cluster_id"] == -1:
return None
if r["cluster_id"] in top_ids:
lbl = r["cluster_label_short"]
return lbl[:40] + "…" if len(lbl) > 40 else lbl
return "Other clusters"
fd = fd.copy()
fd["cat"] = fd.apply(label_row, axis=1)
rows.append(fd[["key", "protein_index", "cat"]].rename(columns={"cat": field}))
wide = rows[0]
for r in rows[1:]:
wide = wide.merge(r, on=["key", "protein_index"], how="outer")
return wide
@app.callback(
Output("pc-parcats-graph", "figure"),
Output("pc-config-table", "children"),
Input("pc-dd-model", "value"),
Input("pc-dd-min", "value"),
Input("pc-dd-threshold", "value"),
Input("pc-dd-topn", "value"),
Input("pc-filter-groups", "value"),
Input("pc-filter-host", "value"),
Input("pc-filter-inducer", "value"),
Input("pc-filter-medium", "value"),
Input("pc-filter-plasmid", "value"),
)
def update_protocol_configs(model, min_val, threshold, top_n,
filter_groups, filter_host, filter_inducer,
filter_medium, filter_plasmid):
if not all([model, min_val, threshold, top_n]):
empty = go.Figure()
empty.update_layout(paper_bgcolor="white",
annotations=[dict(text="Select model, min and threshold.",
showarrow=False, font=dict(size=14))])
return empty, html.Div()
adf = _load_cluster_csv(model, min_val, threshold)
if adf is None:
empty = go.Figure()
empty.update_layout(paper_bgcolor="white",
annotations=[dict(text="Data file not found.",
showarrow=False, font=dict(size=14))])
return empty, html.Div("Data file not found.", className="text-muted small")
# ── Apply filters ────────────────────────────────────────────────────────
# Group filter: keep only PMIDs belonging to selected groups
if filter_groups:
allowed_pmids = {int(pmid) for pmid, grps in PMID_GROUPS.items()
if grps & set(filter_groups)}
adf = adf[adf["key"].isin(allowed_pmids)]
# Per-field category pre-filters: build category col for core fields,
# then restrict to rows whose category matches the selected values.
_field_filter_map = {
"expression_strain": filter_host,
"inducer": filter_inducer,
"medium_name": filter_medium,
"plasmid": filter_plasmid,
}
for fld, sel in _field_filter_map.items():
if not sel:
continue
fld_sub = adf[adf["field"] == fld].copy()
fld_sub["cat"] = fld_sub["cluster_label_short"].apply(
lambda v, _f=fld: _grp(_f, v)
)
keep = fld_sub[fld_sub["cat"].isin(sel)][["key", "protein_index"]].drop_duplicates()
adf = adf.merge(keep, on=["key", "protein_index"], how="inner")
if adf.empty:
empty = go.Figure()
empty.update_layout(paper_bgcolor="white",
annotations=[dict(text="No data for selected filters.",
showarrow=False, font=dict(size=14))])
return empty, html.Div("No proteins match the selected filters.",
className="text-muted small")
wide = _pc_build_wide(adf, top_n)
core = _PC_PROTOCOL_FIELDS[:4] # host, inducer, medium, plasmid
w4 = wide.dropna(subset=core)
# ── Parcats figure ──────────────────────────────────────────────────────
dims = []
for field in _PC_PROTOCOL_FIELDS:
if field not in wide.columns:
continue
col = w4[field].fillna("N/A") if field in w4.columns else None
if col is None:
continue
from collections import Counter as _Ctr
cnt = _Ctr(col.tolist())
cat_order = [c for c, _ in cnt.most_common() if c != "N/A"] + ["N/A"]
dims.append(go.parcats.Dimension(
values=col.tolist(),
label=_PC_FIELD_LABELS.get(field, field),
categoryorder="array",
categoryarray=cat_order,
))
# Color by expression host category index
host_col = w4["expression_strain"].fillna("N/A").tolist()
all_hosts = list(dict.fromkeys(host_col))
host_idx = {h: i for i, h in enumerate(all_hosts)}
color_vals = [host_idx.get(h, 0) for h in host_col]
n_hosts = max(len(all_hosts), 1)
colorscale = [[i / max(n_hosts - 1, 1), _PC_COLORS[i % len(_PC_COLORS)]]
for i in range(n_hosts)]
fig = go.Figure(go.Parcats(
dimensions=dims,
line=dict(color=color_vals, colorscale=colorscale, shape="hspline"),
labelfont=dict(size=12, family="Arial"),
tickfont=dict(size=10, family="Arial"),
arrangement="freeform",
hoverinfo="count+probability",
))
short_model = model.split("__")[-1] if "__" in model else model
fig.update_layout(
title=dict(
text=f"Protocol configuration flows — {short_model} t={threshold} min={min_val}",
font=dict(size=13, family="Arial"), x=0.5,
),
paper_bgcolor="white",
font=dict(family="Arial", size=11),
margin=dict(l=60, r=60, t=60, b=40),
)
# ── Top-config table — category-level grouping ──────────────────────────
# Map fine-grained cluster labels to readable top-level categories so
# combinations survive the specificity of t=0.9 clusters.
# Build category table from all clustered entries (not just top-N)
# to get meaningful combination counts.
sub_all = adf[adf["field"].isin(core) & (adf["cluster_id"] != -1)].copy()
sub_all["cat"] = sub_all.apply(
lambda r: _grp(r["field"], r["cluster_label_short"]), axis=1
)
sub_all = sub_all[sub_all["cat"].notna()]
wide_cat = sub_all.pivot_table(
index=["key", "protein_index"], columns="field",
values="cat", aggfunc="first"
).reset_index()
w4_grp = wide_cat.dropna(subset=core)
from collections import Counter as _Ctr
combos = _Ctr(tuple(r) for r in w4_grp[core].itertuples(index=False))
total = len(w4_grp) # proteins with all 4 fields in a named cluster
table_rows = []
for rank, (combo, cnt) in enumerate(combos.most_common(20), 1):
row = {"Rank": rank, "Count": cnt, "%": f"{cnt/total*100:.1f}%"}
for field, val in zip(core, combo):
row[_PC_FIELD_LABELS[field]] = val
table_rows.append(row)
cols = ["Rank", "Count", "%"] + [_PC_FIELD_LABELS[f] for f in core]
table = dash_table.DataTable(
columns=[{"name": c, "id": c} for c in cols],
data=table_rows,
sort_action="native",
style_table={"overflowX": "auto"},
style_cell={"fontSize": "12px", "padding": "5px 10px", "textAlign": "left",
"maxWidth": "220px", "overflow": "hidden", "textOverflow": "ellipsis"},
style_header={"fontWeight": "bold", "backgroundColor": "#f8f9fa"},
style_data_conditional=[
{"if": {"row_index": "odd"}, "backgroundColor": "#f8f9fa"},
{"if": {"row_index": 0}, "backgroundColor": "#e8f0fe", "fontWeight": "600"},
],
tooltip_data=[
{c: {"value": str(row.get(c, "")), "type": "markdown"} for c in cols}
for row in table_rows
],
tooltip_duration=None,
)
return fig, table
@app.callback(
Output("metrics-heatmap", "figure"),
Output("metrics-model-compare", "figure"),
Output("metrics-best-table", "children"),
Input("dd-metrics-field", "value"),
Input("dd-metrics-type", "value"),
Input("dd-metrics-model", "value"),
)
def update_grid_metrics(field, metric, model):
empty = go.Figure()
empty.update_layout(paper_bgcolor="#ffffff", plot_bgcolor="#f9fafc")
if METRICS_DF.empty or not field or not metric or not model:
return empty, empty, html.Div()
metric_label = _METRIC_LABELS.get(metric, metric)
higher_better = metric == "silhouette_cosine"
# ── Heatmap: threshold × min_size for selected model + field ─────────────
sub = METRICS_DF[(METRICS_DF["model"] == model) & (METRICS_DF["field"] == field)]
if not sub.empty:
pivot = sub.pivot_table(index="min_community_size", columns="threshold",
values=metric, aggfunc="mean")
pivot = pivot.sort_index(ascending=False)
colorscale = "RdYlGn" if higher_better else "RdYlGn_r"
heatmap_fig = go.Figure(go.Heatmap(
z=pivot.values,
x=[str(c) for c in pivot.columns],
y=[str(r) for r in pivot.index],
colorscale=colorscale,
text=[[f"{v:.3f}" for v in row] for row in pivot.values],
texttemplate="%{text}",
hovertemplate="Threshold: %{x}
Min size: %{y}
" + metric_label + ": %{z:.3f}",
colorbar=dict(title=metric_label, thickness=14),
))
heatmap_fig.update_layout(
title=dict(text=f"{metric_label} — {field.replace('_',' ').title()}
"
f"{model}", font=dict(size=13)),
xaxis=dict(title="Threshold", type="category"),
yaxis=dict(title="Min community size", type="category"),
paper_bgcolor="#ffffff", plot_bgcolor="#f9fafc",
margin=dict(l=60, r=20, t=70, b=50),
)
else:
heatmap_fig = empty
# ── Bar chart: compare all models for selected field at best threshold ────
field_df = METRICS_DF[METRICS_DF["field"] == field]
if not field_df.empty:
best = (field_df.groupby("model")[metric]
.apply(lambda x: x.max() if higher_better else x.min())
.reset_index()
.sort_values(metric, ascending=not higher_better))
colors = [_PALETTE[i % len(_PALETTE)] for i in range(len(best))]
compare_fig = go.Figure(go.Bar(
x=best["model"],
y=best[metric],
marker_color=colors,
marker_line_color="rgba(255,255,255,0.6)",
marker_line_width=0.8,
opacity=0.88,
hovertemplate="%{x}
" + metric_label + ": %{y:.3f}",
))
compare_fig.update_layout(
title=dict(text=f"Best {metric_label} per model — {field.replace('_',' ').title()}",
font=dict(size=13)),
xaxis=dict(tickangle=-35, tickfont=dict(size=10)),
yaxis=dict(title=metric_label, gridcolor="#e5e7eb"),
paper_bgcolor="#ffffff", plot_bgcolor="#f9fafc",
margin=dict(l=60, r=20, t=60, b=120),
showlegend=False,
)
else:
compare_fig = empty
# ── Best combinations table ───────────────────────────────────────────────
top = (METRICS_DF[METRICS_DF["field"] == field]
.sort_values(metric, ascending=not higher_better)
.head(10)[["model", "threshold", "min_community_size", "n_clusters",
"silhouette_cosine", "davies_bouldin"]]
.round(4))
best_table = dash_table.DataTable(
columns=[{"name": c.replace("_", " ").title(), "id": c} for c in top.columns],
data=top.to_dict("records"),
sort_action="native",
style_table={"overflowX": "auto"},
style_cell={"fontSize": "12px", "padding": "5px 10px", "textAlign": "left"},
style_header={
"fontWeight": "700", "backgroundColor": "#1a3a5c",
"color": "#ffffff", "fontSize": "11px",
"textTransform": "uppercase", "letterSpacing": "0.04em",
},
style_data_conditional=[
{"if": {"row_index": "odd"}, "backgroundColor": "#eef4fd"},
{"if": {"row_index": 0}, "backgroundColor": "#d4edda", "fontWeight": "bold"},
{"if": {"filter_query": "{silhouette_cosine} > 0.6", "column_id": "silhouette_cosine"},
"color": "#198754", "fontWeight": "bold"},
{"if": {"filter_query": "{silhouette_cosine} < 0.3", "column_id": "silhouette_cosine"},
"color": "#dc3545"},
],
)
return heatmap_fig, compare_fig, best_table
# ---------------------------------------------------------------------------
@app.callback(
Output("search-value", "value"),
Output("filter-group", "value"),
Input("clear-search", "n_clicks"),
prevent_initial_call=True,
)
def clear_filters(_):
return "", []
@app.callback(
Output("download-csv", "data"),
Input("download-btn", "n_clicks"),
State("protein-table", "data"),
prevent_initial_call=True,
)
def download_csv(_, table_data):
filtered_df = pd.DataFrame(table_data)
return dcc.send_data_frame(filtered_df.to_csv, "llm_extractor_results.csv", index=False)
@app.callback(
Output("eval-classif-graph", "figure"),
Input("classif-group", "value"),
)
def update_classif_chart(group):
if CLASSIF_DF.empty or not group:
return go.Figure()
sub = CLASSIF_DF[CLASSIF_DF["group"] == group]
if sub.empty:
return go.Figure()
grp = sub.groupby("metric")["value"]
avg = grp.mean().rename("value").reset_index()
sem = grp.sem().rename("sem").reset_index()
avg = avg.merge(sem, on="metric")
avg["label"] = avg["metric"].map(_NLP_METRICS)
# Normalise BLEU / METEOR from 0–100 → 0–1
avg["scale"] = avg["metric"].map(lambda m: _NLP_SCALE.get(m, 1.0))
avg["value"] = avg["value"] / avg["scale"]
avg["sem"] = avg["sem"] / avg["scale"]
fig = go.Figure(go.Bar(
x=avg["label"], y=avg["value"],
customdata=avg[["metric"]].values.tolist(),
error_y=dict(type="data", array=avg["sem"].fillna(0).tolist(), visible=True),
marker_color="#1a6090",
hovertemplate="%{x}
mean: %{y:.3f}
SEM: %{error_y.array:.3f}",
))
fig.update_layout(
yaxis=dict(title="Mean score (0–1)", range=[0, 1], gridcolor="#eeeeee"),
xaxis_title="Metric",
margin=dict(t=20, b=40, l=60, r=20),
plot_bgcolor="white", paper_bgcolor="white",
)
return fig
@app.callback(
Output("eval-classif-examples", "children"),
Input("eval-classif-graph", "clickData"),
State("classif-group", "value"),
prevent_initial_call=True,
)
def show_classif_examples(click_data, group):
if not click_data or CLASSIF_DF.empty:
return dash.no_update
pt = click_data["points"][0]
metric = pt["customdata"][0]
pool = CLASSIF_DF[
(CLASSIF_DF["group"] == group) &
(CLASSIF_DF["metric"] == metric)
]
if pool.empty:
return html.P("No examples found.", className="text-muted small")
metric_label = _NLP_METRICS.get(metric, metric)
scale = _NLP_SCALE.get(metric, 1.0)
values = (pool["value"] / scale).tolist()
dist_fig = _dist_figure(values, x_label=f"{metric_label} (0–1)")
sub = _extremes(pool, "value")
rows = []
prev_rank = None
for _, r in sub.iterrows():
if r["_rank"] != prev_rank:
label_text = "5 Worst" if r["_rank"] == "worst" else "5 Best"
color = "danger" if r["_rank"] == "worst" else "success"
rows.append(html.Tr([
html.Td(dbc.Badge(label_text, color=color, className="me-1"),
colSpan=4, className="fw-semibold small py-1 table-active"),
]))
prev_rank = r["_rank"]
rows.append(html.Tr([
html.Td(r["pmid"], className="text-muted small", style={"whiteSpace": "nowrap"}),
html.Td(r["gt_text"][:300], className="small"),
html.Td(r["llm_text"][:300], className="small"),
html.Td(f"{r['value']/scale:.3f}", className="small text-center"),
]))
return html.Div([
html.H6(f"Examples · {metric_label} · {group.title()}", className="fw-semibold mb-2"),
dcc.Graph(figure=dist_fig, config={"displayModeBar": False}),
html.P(f"Methods text truncated to 300 chars for display.",
className="text-muted small mb-2 mt-3"),
dbc.Table([
html.Thead(html.Tr([
html.Th("PMID"), html.Th("Ground truth (excerpt)"),
html.Th("LLM extraction (excerpt)"), html.Th(metric_label),
])),
html.Tbody(rows),
], bordered=True, size="sm", hover=True, responsive=True),
])
def _eval_bar_figure(stats_df, y_col, err_col, y_label, models):
"""Grouped bar chart over _PURIF_FIELDS with standard-error bars."""
fig = go.Figure()
for lbl in models:
d = stats_df[stats_df["label"] == lbl].copy()
d = d.set_index("field").reindex(_PURIF_FIELDS).reset_index()
fig.add_trace(go.Bar(
name=lbl,
x=d["field"],
y=d[y_col],
customdata=[[lbl]] * len(d),
error_y=dict(type="data", array=d[err_col].fillna(0).tolist(), visible=True),
hovertemplate="%{x}
mean: %{y:.3f}
SEM: %{error_y.array:.3f}" + lbl + "",
))
fig.update_layout(
barmode="group",
yaxis_title=y_label,
xaxis_title="Field",
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
margin=dict(t=60, b=110, l=60, r=20),
plot_bgcolor="white",
paper_bgcolor="white",
yaxis=dict(gridcolor="#eeeeee"),
)
fig.update_xaxes(tickangle=35)
return fig
@app.callback(
Output("eval-llm-graph", "figure"),
Input("eval-group", "value"),
Input("eval-model-checklist", "value"),
)
def update_eval_llm(group, models):
if EVAL_LLM_DF.empty or not group or not models:
return go.Figure()
sub = EVAL_LLM_DF[
(EVAL_LLM_DF["group"] == group) & (EVAL_LLM_DF["label"].isin(models))
]
if sub.empty:
return go.Figure()
grp = sub.groupby(["label", "field"])["similarity_score"]
stats = grp.mean().rename("similarity_score").reset_index()
stats["sem"] = grp.sem().values
return _eval_bar_figure(stats, "similarity_score", "sem",
"Mean similarity score (0–10)", models)
@app.callback(
Output("eval-nlp-graph", "figure"),
Input("eval-group", "value"),
Input("eval-model-checklist", "value"),
Input("eval-nlp-metric", "value"),
)
def update_eval_nlp(group, models, metric):
if EVAL_NLP_DF.empty or not group or not models or not metric:
return go.Figure()
sub = EVAL_NLP_DF[
(EVAL_NLP_DF["group"] == group) &
(EVAL_NLP_DF["label"].isin(models)) &
(EVAL_NLP_DF["metric"] == metric)
]
if sub.empty:
return go.Figure()
grp = sub.groupby(["label", "field"])["value"]
stats = grp.mean().rename("value").reset_index()
stats["sem"] = grp.sem().values
scale = _NLP_SCALE.get(metric, 1.0)
stats["value"] /= scale
stats["sem"] /= scale
metric_label = _NLP_METRICS.get(metric, metric)
return _eval_bar_figure(stats, "value", "sem",
f"Mean {metric_label} (0–1)", models)
def _examples_header(field, label):
return html.H6(
f"Examples · {field.replace('_', ' ').title()} · {label}",
className="fw-semibold mb-2",
)
def _dist_figure(values, x_label, x_range=None, nbinsx=20):
"""Box plot (top) + histogram (bottom) for a 1-D series of values."""
fig = make_subplots(
rows=2, cols=1,
shared_xaxes=True,
row_heights=[0.25, 0.75],
vertical_spacing=0.04,
)
color = "#1a6090"
fig.add_trace(go.Box(
x=values, orientation="h",
marker_color=color, line_color=color,
boxmean="sd",
hovertemplate=(
"min: %{x[0]:.3f}
"
"Q1: %{x[1]:.3f}
"
"median: %{x[2]:.3f}
"
"Q3: %{x[3]:.3f}
"
"max: %{x[4]:.3f}"
),
), row=1, col=1)
mean_val = float(pd.Series(values).mean())
fig.add_trace(go.Histogram(
x=values, nbinsx=nbinsx,
marker_color=color, opacity=0.8,
hovertemplate=f"{x_label}: %{{x:.3f}}
Count: %{{y}}",
), row=2, col=1)
fig.add_vline(x=mean_val, line_dash="dash", line_color="crimson",
annotation_text=f"mean={mean_val:.3f}",
annotation_position="top right")
layout = dict(
showlegend=False,
xaxis2_title=x_label,
yaxis2_title="Count",
margin=dict(t=20, b=40, l=50, r=20),
height=280,
plot_bgcolor="white",
paper_bgcolor="white",
yaxis=dict(showticklabels=False, gridcolor="#eeeeee"),
yaxis2=dict(gridcolor="#eeeeee"),
)
if x_range:
layout["xaxis2"] = dict(title=x_label, range=x_range)
layout["xaxis"] = dict(range=x_range)
fig.update_layout(**layout)
return fig
def _extremes(df, score_col, n=5):
"""Return bottom-n and top-n rows by score_col, labelled."""
worst = df.nsmallest(n, score_col).copy()
best = df.nlargest(n, score_col).copy()
worst["_rank"] = "worst"
best["_rank"] = "best"
return pd.concat([worst, best], ignore_index=True)
@app.callback(
Output("eval-llm-examples", "children"),
Input("eval-llm-graph", "clickData"),
State("eval-group", "value"),
prevent_initial_call=True,
)
def show_eval_llm_examples(click_data, group):
if not click_data or EVAL_LLM_DF.empty:
return dash.no_update
pt = click_data["points"][0]
field = pt["x"]
label = pt["customdata"][0]
pool = EVAL_LLM_DF[
(EVAL_LLM_DF["group"] == group) &
(EVAL_LLM_DF["label"] == label) &
(EVAL_LLM_DF["field"] == field)
]
if pool.empty:
return html.P("No examples found.", className="text-muted small")
# Distribution chart
dist_fig = _dist_figure(
pool["similarity_score"].tolist(),
x_label="Similarity score (0–10)",
x_range=[0, 10],
nbinsx=10,
)
# Examples table
sub = _extremes(pool, "similarity_score")
rows = []
prev_rank = None
for _, r in sub.iterrows():
if r["_rank"] != prev_rank:
label_text = "5 Worst" if r["_rank"] == "worst" else "5 Best"
color = "danger" if r["_rank"] == "worst" else "success"
rows.append(html.Tr([
html.Td(dbc.Badge(label_text, color=color, className="me-1"),
colSpan=5, className="fw-semibold small py-1 table-active"),
]))
prev_rank = r["_rank"]
rows.append(html.Tr([
html.Td(r["pmid"], className="text-muted small", style={"whiteSpace":"nowrap"}),
html.Td(r["gt_text"], className="small"),
html.Td(r["llm_text"], className="small"),
html.Td(f"{r['similarity_score']:.0f} / 10", className="small text-center"),
html.Td(r["explanation"], className="small text-muted"),
]))
return html.Div([
_examples_header(field, label),
dcc.Graph(figure=dist_fig, config={"displayModeBar": False}),
dbc.Table([
html.Thead(html.Tr([
html.Th("PMID"), html.Th("Ground truth"), html.Th("LLM extraction"),
html.Th("Score"), html.Th("Explanation"),
])),
html.Tbody(rows),
], bordered=True, size="sm", hover=True, responsive=True, className="mt-3"),
])
@app.callback(
Output("eval-nlp-examples", "children"),
Input("eval-nlp-graph", "clickData"),
State("eval-group", "value"),
State("eval-nlp-metric", "value"),
prevent_initial_call=True,
)
def show_eval_nlp_examples(click_data, group, metric):
if not click_data or EVAL_NLP_DF.empty:
return dash.no_update
pt = click_data["points"][0]
field = pt["x"]
label = pt["customdata"][0]
pool = EVAL_NLP_DF[
(EVAL_NLP_DF["group"] == group) &
(EVAL_NLP_DF["label"] == label) &
(EVAL_NLP_DF["field"] == field) &
(EVAL_NLP_DF["metric"] == metric)
]
if pool.empty:
return html.P("No examples found.", className="text-muted small")
metric_label = _NLP_METRICS.get(metric, metric)
scale = _NLP_SCALE.get(metric, 1.0)
# Distribution chart
dist_fig = _dist_figure(
(pool["value"] / scale).tolist(),
x_label=f"{metric_label} (0–1)",
)
# Examples table
sub = _extremes(pool, "value")
rows = []
prev_rank = None
for _, r in sub.iterrows():
if r["_rank"] != prev_rank:
label_text = "5 Worst" if r["_rank"] == "worst" else "5 Best"
color = "danger" if r["_rank"] == "worst" else "success"
rows.append(html.Tr([
html.Td(dbc.Badge(label_text, color=color, className="me-1"),
colSpan=4, className="fw-semibold small py-1 table-active"),
]))
prev_rank = r["_rank"]
rows.append(html.Tr([
html.Td(r["pmid"], className="text-muted small", style={"whiteSpace":"nowrap"}),
html.Td(r["gt_text"], className="small"),
html.Td(r["llm_text"], className="small"),
html.Td(f"{r['value']/scale:.3f}", className="small text-center"),
]))
return html.Div([
_examples_header(field, label),
dcc.Graph(figure=dist_fig, config={"displayModeBar": False}),
dbc.Table([
html.Thead(html.Tr([
html.Th("PMID"), html.Th("Ground truth"),
html.Th("LLM extraction"), html.Th(metric_label),
])),
html.Tbody(rows),
], bordered=True, size="sm", hover=True, responsive=True, className="mt-3"),
])
def _metric_card(label, value, color="primary"):
body = f"{value:.3f}" if isinstance(value, float) else (str(value) if value is not None else "N/A")
return dbc.Col(
dbc.Card([
dbc.CardBody([
html.P(label, className="text-muted small mb-1"),
html.H4(body, className=f"text-{color} mb-0 fw-bold"),
], className="text-center p-2"),
], className="shadow-sm"),
xs=6, sm=4, md=2,
)
@app.callback(
Output("confmat-cards", "children"),
Output("confmat-heatmap", "figure"),
Input("confmat-group", "value"),
)
def update_confmat(group):
data = CONF_MATRICES.get(group)
if not data:
return html.P("No data available.", className="text-muted"), go.Figure()
tp, fp, fn, tn = data["TP"], data["FP"], data["FN"], data["TN"]
cards = dbc.Row([
_metric_card("Precision", data["Precision"], "success"),
_metric_card("Recall", data["Recall"], "primary"),
_metric_card("F1 Score", data["F1"], "warning"),
_metric_card("Accuracy", data["Accuracy"], "info"),
_metric_card("TP", tp, "secondary"),
_metric_card("FP", fp, "danger"),
_metric_card("FN", fn, "danger"),
_metric_card("TN", tn, "secondary"),
], className="g-2 mb-3")
note = data.get("note")
if note:
fig = go.Figure()
fig.add_annotation(text=note, xref="paper", yref="paper",
x=0.5, y=0.5, showarrow=False,
font=dict(size=13), align="center")
fig.update_layout(plot_bgcolor="white", paper_bgcolor="white",
xaxis_visible=False, yaxis_visible=False)
else:
total = tp + fp + fn + tn
z = [[tn, fp], [fn, tp]]
cdata = [[[False, False], [False, True]],
[[True, False], [True, True]]]
text = [
[f"TN
{tn} ({tn/total*100:.1f}%)",
f"FP
{fp} ({fp/total*100:.1f}%)"],
[f"FN
{fn} ({fn/total*100:.1f}%)",
f"TP
{tp} ({tp/total*100:.1f}%)"],
]
fig = go.Figure(go.Heatmap(
z=z,
x=["Predicted: Non-Enzymology", "Predicted: Enzymology"],
y=["Actual: Non-Enzymology", "Actual: Enzymology"],
customdata=cdata,
text=text,
texttemplate="%{text}",
colorscale="Blues",
showscale=True,
hovertemplate=(
"%{y}
%{x}
Count: %{z}
"
"Click to see examples"
),
))
fig.update_layout(
margin=dict(t=20, b=60, l=160, r=20),
xaxis=dict(side="bottom"),
plot_bgcolor="white", paper_bgcolor="white",
)
return cards, fig
@app.callback(
Output("confmat-examples", "children"),
Input("confmat-heatmap", "clickData"),
State("confmat-group", "value"),
prevent_initial_call=True,
)
def show_confmat_examples(click_data, group):
if not click_data:
return dash.no_update
data = CONF_MATRICES.get(group, {})
papers = data.get("papers", {})
if not papers:
return html.P("No paper-level data available.", className="text-muted small")
pt = click_data["points"][0]
actual = "Non-Enzymology" not in pt.get("y", "")
predicted = "Non-Enzymology" not in pt.get("x", "")
# Label for the cell
cell_label = {
(True, True): ("TP", "success", "True Positives — correctly classified as enzymology"),
(True, False): ("FN", "warning", "False Negatives — enzymology papers missed by classifier"),
(False, True): ("FP", "danger", "False Positives — non-enzymology classified as enzymology"),
(False, False): ("TN", "secondary","True Negatives — correctly classified as non-enzymology"),
}.get((actual, predicted), ("?", "light", ""))
abbr, color, description = cell_label
subset = [(pmid, v) for pmid, v in papers.items()
if v["actual"] == actual and v["predicted"] == predicted]
if not subset:
return html.P("No papers in this category.", className="text-muted small")
# Show up to 10 examples
import random
sample = random.sample(subset, min(10, len(subset)))
rows = []
for pmid, v in sample:
url = v.get("url", "")
pmid_cell = html.A(pmid, href=url, target="_blank") if url else pmid
rows.append(html.Tr([
html.Td(pmid_cell, className="text-muted small", style={"whiteSpace": "nowrap"}),
html.Td(v["title"], className="small"),
html.Td(v["pub_date"], className="small text-muted", style={"whiteSpace": "nowrap"}),
html.Td(v["source"], className="small text-muted"),
]))
return html.Div([
html.H6([
dbc.Badge(abbr, color=color, className="me-2"),
description,
html.Span(f" ({len(subset)} total, showing {len(sample)})",
className="text-muted small fw-normal"),
], className="fw-semibold mb-2"),
dbc.Table([
html.Thead(html.Tr([
html.Th("PMID"), html.Th("Title"),
html.Th("Date"), html.Th("Journal"),
])),
html.Tbody(rows),
], bordered=True, size="sm", hover=True, responsive=True),
])
# Run
# ---------------------------------------------------------------------------
if __name__ == "__main__":
port = int(os.environ.get("PORT", 8050))
debug = os.environ.get("DASH_DEBUG", "false").lower() == "true"
app.run(debug=debug, host="0.0.0.0", port=port)