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| """ | |
| AI Reviewer Finder — Dark Theme UI | |
| Dark glassmorphism + neon accents + premium feel. | |
| """ | |
| from __future__ import annotations | |
| import streamlit as st | |
| import pandas as pd | |
| import os, sys, time, re, html as html_mod | |
| from datetime import datetime | |
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) | |
| st.set_page_config( | |
| page_title="ReviewerFinder AI", | |
| page_icon="🔬", | |
| layout="wide", | |
| initial_sidebar_state="collapsed", | |
| ) | |
| # ── helpers (BEFORE main flow) ──────────────────────────────────────────── | |
| def extract_text_from_pdf(file): | |
| from PyPDF2 import PdfReader | |
| return "\n".join((p.extract_text() or "") for p in PdfReader(file).pages[:15]) | |
| def extract_text_from_docx(file): | |
| from docx import Document | |
| return "\n".join(p.text for p in Document(file).paragraphs) | |
| def parse_manuscript(text: str) -> dict: | |
| lines = [l.strip() for l in text.split("\n") if l.strip()] | |
| title = abstract = "" | |
| keywords: list[str] = [] | |
| authors: list[str] = [] | |
| author_institutions: list[str] = [] | |
| for line in lines[:10]: | |
| if len(line) > 15 and not line.lower().startswith(("abstract","keyword","introduction","http","doi")): | |
| title = line; break | |
| full = text.lower() | |
| # Extract authors: lines between title and abstract that contain names/emails | |
| title_found = False | |
| for line in lines[:20]: | |
| if line == title: | |
| title_found = True; continue | |
| if not title_found: | |
| continue | |
| ll = line.lower() | |
| if ll.startswith(("abstract","a b s t r a c t")): | |
| break | |
| # Skip lines that are just "Corresponding author:" references | |
| if re.match(r'^corresponding\s*(author)?', ll): | |
| continue | |
| # Look for author-like lines (contain names, possibly with affiliations) | |
| # Extract name part before comma/institution info | |
| if len(line) > 3 and len(line) < 300 and not ll.startswith(("keywords","key words","e-mail","email","doi","http","received","accepted","published")): | |
| # Try to extract just the name portion | |
| name_part = re.split(r'[,;]', line)[0].strip() | |
| # Remove email addresses | |
| name_part = re.sub(r'\S+@\S+', '', name_part).strip() | |
| # Remove E-mail: prefix and trailing text | |
| name_part = re.sub(r'E-mail:.*', '', name_part, flags=re.IGNORECASE).strip() | |
| # Remove common suffixes | |
| name_part = re.sub(r'\b(Ph\.?D|M\.?D|Dr\.?|Prof\.?)\b', '', name_part, flags=re.IGNORECASE).strip() | |
| name_part = name_part.strip('., ') | |
| if name_part and len(name_part) > 3 and len(name_part) < 80 and ' ' in name_part: | |
| # Check it looks like a name (mostly letters) | |
| alpha_ratio = sum(c.isalpha() or c == ' ' for c in name_part) / max(len(name_part), 1) | |
| if alpha_ratio > 0.8: | |
| authors.append(name_part) | |
| # Try to extract institution/affiliation — the part right after the name | |
| parts = re.split(r'[,;]', line) | |
| for pi, raw_part in enumerate(parts[1:]): | |
| part = raw_part.strip() | |
| # Remove emails, addresses (numbers), "E-mail:" text | |
| part = re.sub(r'\S+@\S+', '', part).strip() | |
| part = re.sub(r'E-mail:.*', '', part, flags=re.IGNORECASE).strip() | |
| part = part.strip('., ') | |
| if not part or len(part) < 4: | |
| continue | |
| # Check for institution keywords OR treat 2nd comma-part as org name | |
| inst_keywords = ["university","institute","college","laboratory","lab","department", | |
| "dept","school","centre","center","hospital","corporation","corp", | |
| "survey","agency","research","national","organisation","organization"] | |
| if any(ik in part.lower() for ik in inst_keywords) and len(part) > 5: | |
| author_institutions.append(part) | |
| break | |
| # If it's the first part after the name and looks like an org (has uppercase, > 4 chars) | |
| elif pi == 0 and len(part) > 5 and part[0].isupper() and not any(c.isdigit() for c in part[:5]): | |
| author_institutions.append(part) | |
| break | |
| for marker in ["abstract","a b s t r a c t"]: | |
| idx = full.find(marker) | |
| if idx != -1: | |
| at = text[idx+len(marker):idx+len(marker)+3000].strip() | |
| at = re.sub(r'^[\s:\-—\.]+','',at) | |
| for em in ["keywords","key words","introduction","1.","1 ","index terms"]: | |
| ei = at.lower().find(em) | |
| if ei > 50: at = at[:ei].strip(); break | |
| abstract = at[:2000]; break | |
| for marker in ["keywords","key words","index terms"]: | |
| idx = full.find(marker) | |
| if idx != -1: | |
| kt = text[idx+len(marker):idx+len(marker)+500].strip() | |
| kt = re.sub(r'^[\s:\-—\.]+','',kt) | |
| for em in ["\n\n","introduction","1.","1 "]: | |
| ei = kt.lower().find(em) | |
| if ei > 5: kt = kt[:ei]; break | |
| keywords = [k.strip().rstrip('.') for k in re.split(r'[;,\n]',kt) if 2<len(k.strip())<60][:10] | |
| break | |
| # Auto-generate keywords from title + abstract if none were found | |
| if not keywords and (title or abstract): | |
| keywords = _extract_keywords_from_text(title, abstract) | |
| # Also try to extract institution from author lines with email patterns | |
| if not author_institutions and authors: | |
| for line in lines[:20]: | |
| for inst_kw in ["university","institute","college","laboratory","lab","centre","center","survey","corporation"]: | |
| if inst_kw in line.lower() and len(line) > 10: | |
| # Extract the institution part | |
| parts = re.split(r'[,;]', line) | |
| for part in parts: | |
| part = part.strip() | |
| if inst_kw in part.lower() and len(part) > 5: | |
| clean = re.sub(r'\S+@\S+', '', part).strip().rstrip('., ') | |
| if clean and len(clean) > 5: | |
| author_institutions.append(clean) | |
| break | |
| return { | |
| "title": title, | |
| "abstract": abstract, | |
| "keywords": keywords, | |
| "authors": authors, | |
| "author_institutions": list(dict.fromkeys(author_institutions)), # deduplicate | |
| } | |
| def _extract_keywords_from_text(title: str, abstract: str) -> list[str]: | |
| """Extract relevant keywords from title and abstract using NLP heuristics.""" | |
| text = f"{title} {abstract}".lower() | |
| words = re.findall(r'\b[a-z]{3,}\b', text) | |
| # Stopwords for academic text | |
| stops = { | |
| "the","and","for","are","but","not","you","all","can","had","her","was", | |
| "one","our","out","has","have","been","from","this","that","with","they", | |
| "will","each","make","like","into","over","such","than","them","then", | |
| "these","some","would","other","about","which","their","there","could", | |
| "more","also","most","here","both","after","those","using","used","based", | |
| "show","shown","well","however","between","through","where","while", | |
| "during","before","should","results","paper","study","method","methods", | |
| "approach","propose","proposed","present","presented","demonstrate", | |
| "existing","recent","first","second","new","novel","different","important", | |
| "significant","provide","provides","including","across","within","without", | |
| "performance","compared","model","models","data","analysis","often", | |
| "when","does","being","value","values","case","cases","effect","effects", | |
| "test","tests","suggests","suggesting","particularly","may","terms", | |
| "strongly","simple","address","begin","begins","combination","attempt", | |
| "attempts","prior","assumption","derive","derived","improve","improved", | |
| "practical","application","confirmed","offer","offering","serve", | |
| "serving","useful","efficient","transparent","alternative", | |
| } | |
| # Extract bigrams (two-word phrases) | |
| bigrams = [] | |
| for i in range(len(words) - 1): | |
| if words[i] not in stops and words[i+1] not in stops: | |
| bigrams.append(f"{words[i]} {words[i+1]}") | |
| # Count frequencies | |
| bigram_freq = {} | |
| for bg in bigrams: | |
| bigram_freq[bg] = bigram_freq.get(bg, 0) + 1 | |
| word_freq = {} | |
| for w in words: | |
| if w not in stops and len(w) > 3: | |
| word_freq[w] = word_freq.get(w, 0) + 1 | |
| # Top bigrams as keywords | |
| top_bigrams = sorted(bigram_freq.items(), key=lambda x: x[1], reverse=True) | |
| keywords = [bg for bg, cnt in top_bigrams if cnt >= 2][:6] | |
| # Add top single words if we don't have enough | |
| if len(keywords) < 4: | |
| top_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True) | |
| for w, _ in top_words: | |
| if w not in " ".join(keywords) and len(keywords) < 8: | |
| keywords.append(w) | |
| return keywords[:8] | |
| def _to_excel(df): | |
| from io import BytesIO | |
| buf = BytesIO() | |
| with pd.ExcelWriter(buf, engine="openpyxl") as w: | |
| df.to_excel(w, index=False, sheet_name="Reviewers") | |
| return buf.getvalue() | |
| def esc(s: str) -> str: | |
| return html_mod.escape(str(s)) | |
| def _step_indicator(active: int): | |
| labels = ["Upload", "Review", "Results"] | |
| dots = [] | |
| for i, label in enumerate(labels, 1): | |
| if i < active: | |
| cls, num = "done", "✓" | |
| elif i == active: | |
| cls, num = "active", str(i) | |
| else: | |
| cls, num = "pending", str(i) | |
| dots.append(f'<div class="step {cls}"><div class="step-dot {cls}">{num}</div>{label}</div>') | |
| steps_html = '<span class="step-arrow">›</span>'.join(dots) | |
| st.markdown(f""" | |
| <div class="unified-header"> | |
| <div class="navbar-left"> | |
| <div class="navbar-logo">RF</div> | |
| <div class="navbar-title">ReviewerFinder</div> | |
| </div> | |
| <div class="steps-inline">{steps_html}</div> | |
| <div class="navbar-badge">AI Powered</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| def _score_bars(scores: dict): | |
| """Render all 4 score bars as a single compact HTML snippet.""" | |
| items = [("Topic","topic_score"),("Method","methodology_score"),("Seniority","seniority_score"),("Recency","recency_score")] | |
| html = "" | |
| for label, key in items: | |
| v = scores.get(key, 0) | |
| pct = min(v * 10, 100) | |
| if v >= 7: | |
| grad = "linear-gradient(90deg, #10b981, #34d399)" | |
| tc = "#34d399" | |
| elif v >= 5: | |
| grad = "linear-gradient(90deg, #f59e0b, #fbbf24)" | |
| tc = "#fbbf24" | |
| else: | |
| grad = "linear-gradient(90deg, #ef4444, #f87171)" | |
| tc = "#f87171" | |
| html += f"""<div style="margin-bottom:12px;"> | |
| <div style="display:flex;justify-content:space-between;align-items:baseline;margin-bottom:5px;"> | |
| <span style="font-size:11px;font-weight:600;color:#94a3b8;text-transform:uppercase;letter-spacing:.05em;">{label}</span> | |
| <span style="font-size:12px;font-weight:800;color:{tc};">{v:.1f}</span> | |
| </div> | |
| <div style="height:7px;background:rgba(255,255,255,0.06);border-radius:99px;overflow:hidden;"> | |
| <div style="height:100%;width:{pct}%;background:{grad};border-radius:99px;transition:width .8s cubic-bezier(.4,0,.2,1);"></div> | |
| </div> | |
| </div>""" | |
| st.markdown(html, unsafe_allow_html=True) | |
| # ── session state ───────────────────────────────────────────────────────── | |
| for k, v in {"stage": "upload", "parsed": {}, "results": None, "invite_open": {}, "invite_sent": {}}.items(): | |
| if k not in st.session_state: | |
| st.session_state[k] = v | |
| # ── global styles (dark theme) ─────────────────────────────────────────── | |
| st.markdown("""<style> | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800;900&display=swap'); | |
| #MainMenu, footer, header {visibility:hidden} | |
| .stDeployButton {display:none} | |
| section[data-testid="stSidebar"] {display:none} | |
| .stApp { | |
| font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif; | |
| background: #0b0f1a; | |
| } | |
| .block-container { | |
| max-width: 1040px !important; | |
| padding: 1.5rem 2rem 4rem !important; | |
| } | |
| /* ─── Typography ─── */ | |
| h1,h2,h3,h4 { | |
| font-family: 'Inter', sans-serif !important; | |
| color: #f1f5f9 !important; | |
| letter-spacing: -0.025em !important; | |
| } | |
| p,span,label,div { | |
| font-family: 'Inter', sans-serif; | |
| } | |
| /* ─── Gradient text ─── */ | |
| .gradient-text { | |
| background: linear-gradient(135deg, #818cf8 0%, #a78bfa 40%, #c084fc 70%, #818cf8 100%); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| background-clip: text; | |
| } | |
| /* ─── Unified header ─── */ | |
| .unified-header { | |
| display: flex; align-items: center; gap: 16px; | |
| padding: 12px 22px; margin-bottom: 24px; | |
| background: rgba(255,255,255,0.04); | |
| backdrop-filter: blur(20px); | |
| -webkit-backdrop-filter: blur(20px); | |
| border: 1px solid rgba(255,255,255,0.06); | |
| border-radius: 16px; | |
| box-shadow: 0 4px 24px rgba(0,0,0,.3); | |
| } | |
| .navbar-left { | |
| display: flex; align-items: center; gap: 10px; | |
| } | |
| .navbar-logo { | |
| width: 34px; height: 34px; border-radius: 10px; | |
| background: linear-gradient(135deg, #6366f1, #8b5cf6); | |
| display: flex; align-items: center; justify-content: center; | |
| color: #fff; font-size: 12px; font-weight: 800; | |
| box-shadow: 0 2px 12px rgba(99,102,241,.4); | |
| letter-spacing: -0.02em; flex-shrink: 0; | |
| } | |
| .navbar-title { | |
| font-size: 16px; font-weight: 800; color: #e2e8f0; | |
| letter-spacing: -0.02em; white-space: nowrap; | |
| } | |
| .steps-inline { | |
| display: flex; align-items: center; gap: 6px; | |
| margin-left: auto; | |
| } | |
| .navbar-badge { | |
| padding: 5px 14px; | |
| background: rgba(99,102,241,0.15); | |
| color: #a5b4fc; | |
| border: 1px solid rgba(99,102,241,0.2); | |
| border-radius: 99px; font-size: 11px; font-weight: 700; | |
| letter-spacing: .03em; text-transform: uppercase; | |
| white-space: nowrap; flex-shrink: 0; margin-left: 12px; | |
| } | |
| .step { | |
| display: flex; align-items: center; gap: 6px; | |
| font-size: 13px; font-weight: 600; | |
| transition: all .3s; | |
| } | |
| .step-dot { | |
| width: 24px; height: 24px; border-radius: 50%; | |
| display: flex; align-items: center; justify-content: center; | |
| font-size: 11px; font-weight: 700; | |
| transition: all .4s cubic-bezier(.4,0,.2,1); | |
| } | |
| .step-dot.done { background: linear-gradient(135deg, #10b981, #059669); color: #fff; box-shadow: 0 2px 8px rgba(16,185,129,.35); } | |
| .step-dot.active { background: linear-gradient(135deg, #6366f1, #8b5cf6); color: #fff; box-shadow: 0 2px 10px rgba(99,102,241,.4); } | |
| .step-dot.pending { background: rgba(255,255,255,0.06); color: #64748b; } | |
| .step.done { color: #34d399; } | |
| .step.active { color: #a5b4fc; } | |
| .step.pending { color: #475569; } | |
| .step-arrow { color: #334155; font-size: 16px; margin: 0 4px; font-weight: 300; } | |
| /* ─── Glass cards (dark) ─── */ | |
| .glass-card { | |
| background: rgba(255,255,255,0.03); | |
| backdrop-filter: blur(12px); | |
| -webkit-backdrop-filter: blur(12px); | |
| border: 1px solid rgba(255,255,255,0.06); | |
| border-radius: 18px; | |
| padding: 28px; | |
| margin-bottom: 16px; | |
| box-shadow: 0 4px 24px rgba(0,0,0,.3); | |
| transition: all .3s cubic-bezier(.4,0,.2,1); | |
| } | |
| .glass-card:hover { | |
| box-shadow: 0 8px 32px rgba(0,0,0,.4); | |
| transform: translateY(-1px); | |
| border-color: rgba(99,102,241,.2); | |
| } | |
| /* ─── Feature cards ─── */ | |
| .feature-card { | |
| background: rgba(255,255,255,0.03); | |
| backdrop-filter: blur(12px); | |
| -webkit-backdrop-filter: blur(12px); | |
| border: 1px solid rgba(255,255,255,0.06); | |
| border-radius: 20px; | |
| padding: 32px 24px; | |
| text-align: center; | |
| box-shadow: 0 4px 24px rgba(0,0,0,.2); | |
| transition: all .35s cubic-bezier(.4,0,.2,1); | |
| } | |
| .feature-card:hover { | |
| box-shadow: 0 12px 40px rgba(99,102,241,.15), 0 4px 12px rgba(0,0,0,.3); | |
| border-color: rgba(99,102,241,.25); | |
| transform: translateY(-4px); | |
| } | |
| .feature-icon { | |
| width: 56px; height: 56px; border-radius: 16px; | |
| display: inline-flex; align-items: center; justify-content: center; | |
| font-size: 24px; margin-bottom: 16px; | |
| } | |
| /* ─── Inputs ─── */ | |
| .stTextInput label,.stTextArea label,.stSlider label,.stFileUploader label,.stSelectbox label { | |
| color:#94a3b8 !important;font-weight:600 !important;font-size:13px !important;font-family:'Inter',sans-serif !important; | |
| } | |
| .stTextInput input, | |
| .stTextArea textarea, | |
| .stTextInput > div > div > input, | |
| .stTextArea > div > div > textarea, | |
| [data-testid="stTextInput"] input, | |
| [data-testid="stTextArea"] textarea { | |
| background: #141825 !important; | |
| background-color: #141825 !important; | |
| border: 1.5px solid rgba(255,255,255,0.08) !important; | |
| border-radius: 12px !important; | |
| color: #e2e8f0 !important; font-size: 14px !important; | |
| font-family: 'Inter', sans-serif !important; | |
| transition: all .2s !important; | |
| -webkit-text-fill-color: #e2e8f0 !important; | |
| } | |
| .stTextInput input:focus, | |
| .stTextArea textarea:focus, | |
| .stTextInput > div > div > input:focus, | |
| .stTextArea > div > div > textarea:focus, | |
| [data-testid="stTextInput"] input:focus, | |
| [data-testid="stTextArea"] textarea:focus { | |
| border-color: #818cf8 !important; | |
| box-shadow: 0 0 0 3px rgba(129,140,248,.15) !important; | |
| background: #141825 !important; | |
| background-color: #141825 !important; | |
| } | |
| /* placeholder text */ | |
| .stTextInput input::placeholder, | |
| .stTextArea textarea::placeholder { | |
| color: #475569 !important; | |
| -webkit-text-fill-color: #475569 !important; | |
| } | |
| /* ─── Upload card ─── */ | |
| .upload-card { | |
| position: relative; | |
| border-radius: 20px; | |
| padding: 2px; | |
| margin-bottom: -8px; | |
| overflow: hidden; | |
| } | |
| .upload-glow { | |
| position: absolute; inset: 0; | |
| border-radius: 20px; | |
| background: conic-gradient(from 180deg, #6366f1, #8b5cf6, #c084fc, #6366f1); | |
| opacity: 0.25; | |
| animation: glowSpin 4s linear infinite; | |
| } | |
| @keyframes glowSpin { | |
| to { transform: rotate(360deg); } | |
| } | |
| .upload-inner { | |
| position: relative; | |
| background: #0e1220; | |
| border-radius: 18px; | |
| padding: 36px 28px 28px; | |
| text-align: center; | |
| } | |
| .upload-icon-wrap { | |
| width: 64px; height: 64px; border-radius: 18px; | |
| background: rgba(99,102,241,0.08); | |
| border: 1px solid rgba(99,102,241,0.15); | |
| display: inline-flex; align-items: center; justify-content: center; | |
| margin-bottom: 18px; | |
| } | |
| /* ─── File uploader: invisible overlay on upload card ─── */ | |
| [data-testid="stFileUploader"] { | |
| position: relative !important; | |
| margin-top: -180px !important; | |
| height: 180px !important; | |
| z-index: 20 !important; | |
| opacity: 0 !important; | |
| cursor: pointer !important; | |
| } | |
| [data-testid="stFileUploader"] * { | |
| cursor: pointer !important; | |
| } | |
| /* ─── Buttons ─── */ | |
| .stButton > button { | |
| background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%) !important; | |
| color: #fff !important; border: none !important; | |
| border-radius: 12px !important; | |
| padding: 12px 28px !important; | |
| font-weight: 700 !important; font-size: 14px !important; | |
| font-family: 'Inter', sans-serif !important; | |
| box-shadow: 0 4px 14px rgba(99,102,241,.35), inset 0 1px 0 rgba(255,255,255,.1) !important; | |
| transition: all .25s cubic-bezier(.4,0,.2,1) !important; | |
| letter-spacing: .01em !important; | |
| } | |
| .stButton > button:hover { | |
| transform: translateY(-2px) !important; | |
| box-shadow: 0 8px 24px rgba(99,102,241,.45), inset 0 1px 0 rgba(255,255,255,.15) !important; | |
| } | |
| .stButton > button:active { | |
| transform: translateY(0) !important; | |
| } | |
| /* ─── Download buttons ─── */ | |
| .stDownloadButton > button { | |
| background: rgba(255,255,255,0.04) !important; | |
| backdrop-filter: blur(8px) !important; | |
| color: #a5b4fc !important; | |
| border: 1.5px solid rgba(99,102,241,0.2) !important; | |
| border-radius: 12px !important; | |
| font-weight: 600 !important; font-family: 'Inter', sans-serif !important; | |
| box-shadow: 0 2px 8px rgba(0,0,0,.2) !important; | |
| transition: all .2s !important; | |
| } | |
| .stDownloadButton > button:hover { | |
| background: rgba(99,102,241,0.1) !important; | |
| border-color: rgba(99,102,241,0.35) !important; | |
| transform: translateY(-1px) !important; | |
| box-shadow: 0 4px 12px rgba(99,102,241,.15) !important; | |
| } | |
| /* ─── Expander ─── */ | |
| .streamlit-expanderHeader, | |
| [data-testid="stExpander"] summary { | |
| background: rgba(255,255,255,0.03) !important; | |
| border: 1.5px solid rgba(255,255,255,0.06) !important; | |
| border-radius: 12px !important; | |
| color: #cbd5e1 !important; font-weight: 600 !important; | |
| font-family: 'Inter', sans-serif !important; | |
| } | |
| .streamlit-expanderContent, | |
| [data-testid="stExpander"] > div[data-testid="stExpanderDetails"] { | |
| background: #0e1220 !important; | |
| border: 1.5px solid rgba(255,255,255,0.06) !important; | |
| border-top: none !important; | |
| } | |
| details summary span, | |
| [data-testid="stExpander"] summary span { | |
| color: #cbd5e1 !important; | |
| } | |
| /* ─── All generic Streamlit containers dark ─── */ | |
| [data-testid="stForm"], | |
| [data-testid="stVerticalBlock"], | |
| .element-container { | |
| color: #e2e8f0; | |
| } | |
| /* ─── Slider ─── */ | |
| .stSlider [data-baseweb="slider"] div[role="slider"] { | |
| background: linear-gradient(135deg, #6366f1, #8b5cf6) !important; | |
| border: none !important; | |
| box-shadow: 0 2px 8px rgba(99,102,241,.4) !important; | |
| } | |
| /* slider filled track */ | |
| .stSlider [data-baseweb="slider"] div[data-testid="stThumbValue"], | |
| .stSlider [data-testid="stTickBarMin"], | |
| .stSlider [data-testid="stTickBarMax"] { | |
| color: #94a3b8 !important; | |
| } | |
| /* track colors: override the red/default */ | |
| .stSlider [data-baseweb="slider"] > div > div { | |
| background: rgba(255,255,255,0.06) !important; | |
| } | |
| .stSlider [data-baseweb="slider"] > div > div > div:first-child { | |
| background: linear-gradient(90deg, #6366f1, #8b5cf6) !important; | |
| } | |
| .stSlider p, .stSlider label { | |
| color: #94a3b8 !important; | |
| } | |
| /* ─── Progress ─── */ | |
| .stProgress > div > div > div > div { | |
| background: linear-gradient(90deg, #6366f1, #8b5cf6, #c084fc) !important; | |
| background-size: 200% 200% !important; | |
| animation: shimmer 2s ease infinite !important; | |
| border-radius: 99px !important; | |
| } | |
| .stProgress > div > div > div { | |
| background: rgba(255,255,255,0.06) !important; | |
| } | |
| @keyframes shimmer { | |
| 0% { background-position: 200% 0; } | |
| 100% { background-position: -200% 0; } | |
| } | |
| /* ─── Spinner / status text ─── */ | |
| .stSpinner > div { color: #94a3b8 !important; } | |
| .stAlert { background: rgba(255,255,255,0.04) !important; border-color: rgba(255,255,255,0.08) !important; } | |
| /* ─── Mobile: stack Streamlit columns ─── */ | |
| @media (max-width: 768px) { | |
| [data-testid="stHorizontalBlock"] { | |
| flex-direction: column !important; | |
| } | |
| [data-testid="stHorizontalBlock"] > [data-testid="stColumn"] { | |
| width: 100% !important; | |
| flex: 1 1 100% !important; | |
| min-width: 100% !important; | |
| } | |
| } | |
| </style>""", unsafe_allow_html=True) | |
| st.markdown("""<style> | |
| /* ─── Stat cards ─── */ | |
| .stat-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 14px; margin-bottom: 24px; } | |
| .stat-card { | |
| background: rgba(255,255,255,0.03); | |
| backdrop-filter: blur(8px); | |
| border: 1px solid rgba(255,255,255,0.06); | |
| border-radius: 16px; padding: 22px; | |
| text-align: center; | |
| box-shadow: 0 2px 12px rgba(0,0,0,.2); | |
| transition: all .3s; | |
| } | |
| .stat-card:hover { transform: translateY(-2px); box-shadow: 0 8px 24px rgba(0,0,0,.3); } | |
| .stat-value { | |
| font-size: 30px; font-weight: 900; | |
| background: linear-gradient(135deg, #818cf8, #c084fc); | |
| -webkit-background-clip: text; -webkit-text-fill-color: transparent; | |
| letter-spacing: -0.03em; | |
| } | |
| .stat-label { font-size: 11px; font-weight: 600; color: #64748b; margin-top: 4px; text-transform: uppercase; letter-spacing: .05em; } | |
| /* ─── Tags ─── */ | |
| .tag { display: inline-block; padding: 4px 12px; border-radius: 8px; font-size: 12px; font-weight: 600; margin: 3px 4px 3px 0; transition: all .2s; } | |
| .tag:hover { transform: translateY(-1px); box-shadow: 0 2px 8px rgba(0,0,0,.2); } | |
| .tag-purple { background: rgba(139,92,246,0.12); color: #c4b5fd; border: 1px solid rgba(139,92,246,0.15); } | |
| .tag-blue { background: rgba(59,130,246,0.12); color: #93c5fd; border: 1px solid rgba(59,130,246,0.15); } | |
| .tag-green { background: rgba(16,185,129,0.12); color: #6ee7b7; border: 1px solid rgba(16,185,129,0.15); } | |
| .tag-sm { padding: 3px 10px; font-size: 11px; border-radius: 6px; } | |
| /* ─── Reviewer card ─── */ | |
| .reviewer-divider { | |
| height: 1px; | |
| background: linear-gradient(90deg, transparent, rgba(255,255,255,0.06) 20%, rgba(255,255,255,0.06) 80%, transparent); | |
| margin: 12px 0 16px; | |
| } | |
| /* ─── Score overall badge ─── */ | |
| .score-badge { | |
| display: inline-flex; align-items: center; justify-content: center; | |
| width: 52px; height: 52px; border-radius: 50%; | |
| font-size: 16px; font-weight: 900; | |
| box-shadow: 0 2px 8px rgba(0,0,0,.3); | |
| } | |
| /* ─── Invite ─── */ | |
| .invite-header { | |
| background: rgba(99,102,241,0.08); | |
| backdrop-filter: blur(8px); | |
| border: 1px solid rgba(99,102,241,0.15); | |
| border-radius: 14px; padding: 14px 18px; | |
| margin: 10px 0 8px; | |
| } | |
| .sent-badge { | |
| display: inline-flex; align-items: center; gap: 6px; | |
| background: rgba(16,185,129,0.12); | |
| color: #34d399; padding: 6px 16px; border-radius: 99px; | |
| font-size: 13px; font-weight: 700; | |
| border: 1px solid rgba(16,185,129,0.2); | |
| box-shadow: 0 2px 8px rgba(16,185,129,.1); | |
| } | |
| /* ─── Section labels ─── */ | |
| .section-label { | |
| font-size: 11px; font-weight: 700; color: #64748b; | |
| text-transform: uppercase; letter-spacing: .06em; margin-bottom: 14px; | |
| } | |
| .section-heading { | |
| font-size: 16px; font-weight: 700; color: #e2e8f0; | |
| margin-bottom: 18px; display: flex; align-items: center; gap: 8px; | |
| } | |
| /* ─── COI ─── */ | |
| .coi-flag { | |
| padding: 8px 14px; border-radius: 10px; font-size: 13px; | |
| font-weight: 500; margin-top: 8px; | |
| display: flex; align-items: center; gap: 6px; | |
| } | |
| /* ─── Profile Links ─── */ | |
| .profile-link { | |
| display: inline-block; | |
| padding: 4px 12px; | |
| background: rgba(255,255,255,0.04); | |
| border: 1px solid rgba(255,255,255,0.08); | |
| border-radius: 8px; | |
| font-size: 11px; | |
| font-weight: 600; | |
| color: #a5b4fc; | |
| text-decoration: none; | |
| margin-right: 4px; | |
| transition: all .2s; | |
| } | |
| .profile-link:hover { | |
| background: rgba(99,102,241,0.1); | |
| border-color: rgba(99,102,241,0.25); | |
| } | |
| /* ─── AI Inferred Badge ─── */ | |
| .ai-inferred-badge { | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 3px; | |
| padding: 2px 8px; | |
| background: rgba(245,158,11,0.12); | |
| border: 1px solid rgba(245,158,11,0.25); | |
| border-radius: 6px; | |
| font-size: 10px; | |
| font-weight: 700; | |
| color: #fbbf24; | |
| letter-spacing: .03em; | |
| margin-left: 4px; | |
| } | |
| /* ─── Fade-in animation ─── */ | |
| @keyframes fadeUp { | |
| from { opacity: 0; transform: translateY(12px); } | |
| to { opacity: 1; transform: translateY(0); } | |
| } | |
| .fade-up { animation: fadeUp .5s ease-out both; } | |
| .fade-up-d1 { animation: fadeUp .5s ease-out .1s both; } | |
| .fade-up-d2 { animation: fadeUp .5s ease-out .2s both; } | |
| .fade-up-d3 { animation: fadeUp .5s ease-out .3s both; } | |
| /* ─── Mobile Responsive ─── */ | |
| @media (max-width: 768px) { | |
| .block-container { | |
| padding: 1rem 1rem 3rem !important; | |
| } | |
| /* Header: stack logo + steps vertically */ | |
| .unified-header { | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| padding: 10px 14px; | |
| } | |
| .steps-inline { | |
| margin-left: 0; | |
| order: 3; | |
| width: 100%; | |
| justify-content: center; | |
| padding-top: 6px; | |
| border-top: 1px solid rgba(255,255,255,0.05); | |
| } | |
| .navbar-badge { margin-left: auto; } | |
| /* Hero text smaller */ | |
| .fade-up h1, | |
| .fade-up [style*="font-size:42px"] { | |
| font-size: 28px !important; | |
| } | |
| .fade-up p { | |
| font-size: 14px !important; | |
| } | |
| /* Stat grid: 1 column */ | |
| .stat-grid { | |
| grid-template-columns: 1fr !important; | |
| } | |
| /* Feature cards: handled by Streamlit columns collapsing naturally */ | |
| /* Upload card */ | |
| .upload-inner { padding: 24px 16px 20px; } | |
| .upload-icon-wrap { width: 52px; height: 52px; border-radius: 14px; } | |
| [data-testid="stFileUploader"] { | |
| margin-top: -150px !important; | |
| height: 150px !important; | |
| } | |
| /* Glass cards */ | |
| .glass-card { padding: 18px; border-radius: 14px; } | |
| .feature-card { padding: 22px 16px; border-radius: 14px; } | |
| /* Score badge */ | |
| .score-badge { width: 44px; height: 44px; font-size: 14px; } | |
| /* Section heading */ | |
| .section-heading { font-size: 14px; } | |
| } | |
| @media (max-width: 480px) { | |
| .block-container { | |
| padding: 0.75rem 0.75rem 2rem !important; | |
| } | |
| .unified-header { | |
| padding: 8px 12px; | |
| border-radius: 12px; | |
| } | |
| .navbar-logo { width: 28px; height: 28px; font-size: 10px; border-radius: 8px; } | |
| .navbar-title { font-size: 14px; } | |
| .step { font-size: 11px; } | |
| .step-dot { width: 20px; height: 20px; font-size: 9px; } | |
| .fade-up h1, | |
| .fade-up [style*="font-size:42px"] { | |
| font-size: 24px !important; | |
| } | |
| .stat-value { font-size: 24px; } | |
| .stat-label { font-size: 10px; } | |
| .tag { font-size: 10px; padding: 3px 8px; } | |
| } | |
| </style>""", unsafe_allow_html=True) | |
| # ═══════════════════════════════════════════════════════════════════════════ | |
| # STAGE 1 — UPLOAD | |
| # ═══════════════════════════════════════════════════════════════════════════ | |
| if st.session_state.stage == "upload": | |
| _step_indicator(1) | |
| # Animated hero with gradient text | |
| st.markdown(""" | |
| <div class="fade-up" style="text-align:center; padding: 48px 0 12px;"> | |
| <h1 style="font-size:42px; font-weight:900; letter-spacing:-0.045em; margin-bottom:12px; line-height:1.1;"> | |
| Find the <span class="gradient-text">perfect reviewers</span><br>for your research | |
| </h1> | |
| <p style="font-size:16px; color:#64748b; max-width:500px; margin:0 auto; line-height:1.75;"> | |
| Upload your manuscript and our AI matches you with expert | |
| peer reviewers based on semantic understanding of your work. | |
| </p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.markdown("<div style='height:32px'></div>", unsafe_allow_html=True) | |
| # Upload card | |
| st.markdown(""" | |
| <div class="upload-card fade-up-d1"> | |
| <div class="upload-glow"></div> | |
| <div class="upload-inner"> | |
| <div class="upload-icon-wrap"> | |
| <svg width="32" height="32" viewBox="0 0 24 24" fill="none" stroke="#818cf8" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"> | |
| <path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4"/> | |
| <polyline points="17 8 12 3 7 8"/> | |
| <line x1="12" y1="3" x2="12" y2="15"/> | |
| </svg> | |
| </div> | |
| <div style="font-size:17px;font-weight:700;color:#e2e8f0;margin-bottom:6px;">Upload your manuscript</div> | |
| <div style="font-size:13px;color:#64748b;margin-bottom:4px;">PDF, DOCX, or TXT — we'll extract title, abstract & keywords automatically</div> | |
| </div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| uploaded = st.file_uploader( | |
| "upload_hidden_label", | |
| type=["pdf", "docx", "doc", "txt"], | |
| label_visibility="collapsed", | |
| ) | |
| if uploaded: | |
| with st.spinner("Extracting title, abstract, and keywords..."): | |
| fname = uploaded.name.lower() | |
| if fname.endswith(".pdf"): | |
| raw = extract_text_from_pdf(uploaded) | |
| elif fname.endswith((".docx", ".doc")): | |
| raw = extract_text_from_docx(uploaded) | |
| else: | |
| raw = uploaded.read().decode("utf-8", errors="ignore") | |
| st.session_state.parsed = parse_manuscript(raw) | |
| st.session_state.stage = "review" | |
| st.rerun() | |
| # Feature cards | |
| st.markdown("<div style='height:40px'></div>", unsafe_allow_html=True) | |
| fc1, fc2, fc3 = st.columns(3) | |
| with fc1: | |
| st.markdown(""" | |
| <div class="feature-card fade-up-d1"> | |
| <div class="feature-icon" style="background:rgba(139,92,246,0.1);">🧠</div> | |
| <div style="font-size:15px;font-weight:700;color:#e2e8f0;margin-bottom:6px;">Semantic Matching</div> | |
| <div style="font-size:13px;color:#64748b;line-height:1.65;">AI understands your research context to find reviewers with deep domain expertise</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| with fc2: | |
| st.markdown(""" | |
| <div class="feature-card fade-up-d2"> | |
| <div class="feature-icon" style="background:rgba(59,130,246,0.1);">📊</div> | |
| <div style="font-size:15px;font-weight:700;color:#e2e8f0;margin-bottom:6px;">Multi-Signal Scoring</div> | |
| <div style="font-size:13px;color:#64748b;line-height:1.65;">Topic, methodology, seniority, and recency combined into a single relevance score</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| with fc3: | |
| st.markdown(""" | |
| <div class="feature-card fade-up-d3"> | |
| <div class="feature-icon" style="background:rgba(16,185,129,0.1);">✉️</div> | |
| <div style="font-size:15px;font-weight:700;color:#e2e8f0;margin-bottom:6px;">One-Click Invites</div> | |
| <div style="font-size:13px;color:#64748b;line-height:1.65;">Send peer review invitations directly with pre-filled professional email templates</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # ═══════════════════════════════════════════════════════════════════════════ | |
| # STAGE 2 — REVIEW & CONFIGURE | |
| # ═══════════════════════════════════════════════════════════════════════════ | |
| elif st.session_state.stage == "review": | |
| parsed = st.session_state.parsed | |
| _step_indicator(2) | |
| if st.button("← Back"): | |
| st.session_state.stage = "upload" | |
| st.session_state.parsed = {} | |
| st.rerun() | |
| st.markdown(""" | |
| <div class="fade-up"> | |
| <h2 style="font-size:28px;font-weight:900;margin:4px 0 2px;letter-spacing:-0.03em;">Review & Configure</h2> | |
| <p style="color:#64748b;font-size:14px;margin-bottom:22px;">Verify extracted details and tune your search parameters</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.markdown('<div class="section-heading">📝 Manuscript Details</div>', unsafe_allow_html=True) | |
| title = st.text_input("Title", value=parsed.get("title", "")) | |
| abstract = st.text_area("Abstract", value=parsed.get("abstract", ""), height=180) | |
| keywords_str = st.text_input("Keywords (comma-separated)", value=", ".join(parsed.get("keywords", []))) | |
| st.markdown("<div style='height:20px'></div>", unsafe_allow_html=True) | |
| st.markdown('<div class="section-heading">⚙️ Search Settings</div>', unsafe_allow_html=True) | |
| c1, c2 = st.columns(2) | |
| with c1: | |
| num_reviewers = st.slider("Number of reviewers", 3, 30, 10) | |
| with c2: | |
| num_candidates = st.slider("Search depth (candidates)", 20, 100, 50) | |
| st.markdown('<div class="section-heading">🛡️ Conflict of Interest — Excluded Authors</div>', unsafe_allow_html=True) | |
| # Pre-fill with extracted authors from the manuscript | |
| extracted_authors = parsed.get("authors", []) | |
| extracted_institutions = parsed.get("author_institutions", []) | |
| if extracted_authors: | |
| st.markdown(f""" | |
| <div style="background:rgba(239,68,68,0.08); border:1px solid rgba(239,68,68,0.2); | |
| border-radius:12px; padding:14px 18px; margin-bottom:14px;"> | |
| <div style="font-size:13px;font-weight:700;color:#f87171;margin-bottom:8px;"> | |
| Authors extracted from manuscript (will be excluded from results): | |
| </div> | |
| <div style="display:flex;flex-wrap:wrap;gap:8px;"> | |
| {''.join(f'<span style="background:rgba(239,68,68,0.15);color:#fca5a5;padding:4px 12px;border-radius:20px;font-size:12px;font-weight:600;">{esc(a)}</span>' for a in extracted_authors)} | |
| </div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| author_names_str = st.text_input( | |
| "Excluded author names (comma-separated)", | |
| value=", ".join(extracted_authors), | |
| help="These authors will be excluded from the reviewer results. Edit or add more names.", | |
| ) | |
| author_institutions_str = st.text_input( | |
| "Excluded institutions (comma-separated)", | |
| value=", ".join(extracted_institutions), | |
| help="Reviewers from these institutions will be flagged as COI.", | |
| ) | |
| additional_exclude = st.text_area( | |
| "Additional authors to exclude (one per line)", | |
| height=80, | |
| help="Add any other names you want to exclude from the results, e.g. known collaborators.", | |
| ) | |
| st.markdown("<div style='height:14px'></div>", unsafe_allow_html=True) | |
| if st.button("🔍 Find Reviewers", type="primary", use_container_width=True): | |
| if not title or not abstract: | |
| st.error("Please provide both a title and an abstract.") | |
| else: | |
| kw = [k.strip() for k in keywords_str.split(",") if k.strip()] | |
| an = [n.strip() for n in author_names_str.split(",") if n.strip()] if author_names_str else [] | |
| # Add additional exclusions | |
| if additional_exclude: | |
| an += [n.strip() for n in additional_exclude.split("\n") if n.strip()] | |
| an = list(dict.fromkeys(an)) # deduplicate | |
| ai = [i.strip() for i in author_institutions_str.split(",") if i.strip()] if author_institutions_str else [] | |
| progress = st.progress(0, text="Initializing search pipeline...") | |
| try: | |
| from services.search_service import find_reviewers | |
| progress.progress(15, text="Generating query embeddings...") | |
| time.sleep(0.15) | |
| progress.progress(35, text="Searching vector database...") | |
| results = find_reviewers( | |
| title=title, abstract=abstract, keywords=kw, | |
| author_names=an, author_institutions=ai, | |
| num_reviewers=num_reviewers, num_vector_candidates=num_candidates, | |
| ) | |
| progress.progress(75, text="Scoring and ranking candidates...") | |
| time.sleep(0.15) | |
| progress.progress(90, text="Enriching contact information...") | |
| time.sleep(0.15) | |
| progress.progress(100, text="Complete!") | |
| time.sleep(0.3) | |
| progress.empty() | |
| st.session_state.results = results | |
| st.session_state.stage = "results" | |
| st.rerun() | |
| except Exception as e: | |
| progress.empty() | |
| st.error(f"Search failed: {e}") | |
| st.exception(e) | |
| # ═══════════════════════════════════════════════════════════════════════════ | |
| # STAGE 3 — RESULTS | |
| # ═══════════════════════════════════════════════════════════════════════════ | |
| elif st.session_state.stage == "results": | |
| results = st.session_state.results | |
| if not results: | |
| st.session_state.stage = "upload" | |
| st.rerun() | |
| meta = results.get("metadata", {}) | |
| reviewers = results.get("reviewers", []) | |
| topics = results.get("extracted_topics", {}) | |
| _step_indicator(3) | |
| if st.button("← New Search"): | |
| st.session_state.stage = "upload" | |
| st.session_state.parsed = {} | |
| st.session_state.results = None | |
| st.session_state.invite_open = {} | |
| st.session_state.invite_sent = {} | |
| st.rerun() | |
| st.markdown(f""" | |
| <div class="fade-up"> | |
| <h2 style="font-size:28px;font-weight:900;margin:4px 0 2px;letter-spacing:-0.03em;"> | |
| Reviewer Recommendations | |
| </h2> | |
| <p style="color:#64748b;font-size:14px;margin-bottom:22px;"> | |
| <span class="gradient-text" style="font-weight:700;">{len(reviewers)}</span> top matches ranked by AI relevance scoring | |
| </p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Stats | |
| st.markdown(f""" | |
| <div class="stat-grid fade-up-d1"> | |
| <div class="stat-card"> | |
| <div class="stat-value">{meta.get('vector_candidates',0)}</div> | |
| <div class="stat-label">Candidates Searched</div> | |
| </div> | |
| <div class="stat-card"> | |
| <div class="stat-value">{meta.get('reranked_candidates',0)}</div> | |
| <div class="stat-label">Scored & Ranked</div> | |
| </div> | |
| <div class="stat-card"> | |
| <div class="stat-value">{len(reviewers)}</div> | |
| <div class="stat-label">Recommended</div> | |
| </div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Research profile | |
| if topics: | |
| tags_html = "" | |
| for d in topics.get("primary_domains", []): | |
| tags_html += f'<span class="tag tag-purple">{esc(d)}</span>' | |
| for m in topics.get("methodologies", []): | |
| tags_html += f'<span class="tag tag-blue">{esc(m)}</span>' | |
| for s in topics.get("sub_topics", []): | |
| tags_html += f'<span class="tag tag-green">{esc(s)}</span>' | |
| st.markdown(f""" | |
| <div class="glass-card fade-up-d2"> | |
| <div class="section-label">Research Profile</div> | |
| <div style="display:flex;flex-wrap:wrap;gap:4px;">{tags_html}</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # ── Reviewer Cards ──────────────────────────────────────────────────── | |
| if not reviewers: | |
| st.warning("No matching reviewers found. Try broadening your keywords or increasing search depth.") | |
| else: | |
| for idx, r in enumerate(reviewers): | |
| score = r.get("overall_score", 0) | |
| sc_bg = "rgba(16,185,129,0.12)" if score >= 7 else "rgba(245,158,11,0.12)" if score >= 5 else "rgba(239,68,68,0.12)" | |
| sc_color = "#34d399" if score >= 7 else "#fbbf24" if score >= 5 else "#f87171" | |
| sc_border = "rgba(16,185,129,0.25)" if score >= 7 else "rgba(245,158,11,0.25)" if score >= 5 else "rgba(239,68,68,0.25)" | |
| name = esc(r.get("name", "Unknown")) | |
| inst = esc(r.get("institution", "")) | |
| rank = r.get("rank", "?") | |
| reason = esc(r.get("reasoning", "")) | |
| contact = r.get("contact", {}) | |
| email = contact.get("email", "") | |
| email_is_inferred = contact.get("email_is_inferred", False) | |
| # Gradient divider | |
| if idx > 0: | |
| st.markdown('<div class="reviewer-divider"></div>', unsafe_allow_html=True) | |
| # Layout: rank | info | score bars | |
| col_rank, col_info, col_scores = st.columns([0.5, 4, 1.8]) | |
| with col_rank: | |
| st.markdown(f"""<div style="text-align:center;padding-top:8px;"> | |
| <div style="font-size:11px;font-weight:700;color:#64748b;text-transform:uppercase;letter-spacing:.06em;">Rank</div> | |
| <div style="font-size:24px;font-weight:900;letter-spacing:-0.03em;margin:4px 0 10px;" class="gradient-text">#{rank}</div> | |
| <div class="score-badge" style="background:{sc_bg};color:{sc_color};border:2px solid {sc_border};">{score:.1f}</div> | |
| </div>""", unsafe_allow_html=True) | |
| with col_info: | |
| # Name | |
| st.markdown(f'<div style="font-size:18px;font-weight:800;color:#f1f5f9;letter-spacing:-0.02em;">{name}</div>', unsafe_allow_html=True) | |
| st.markdown(f'<div style="font-size:13px;color:#64748b;margin-bottom:10px;">📍 {inst or "Institution not listed"}</div>', unsafe_allow_html=True) | |
| # Topic tags | |
| rtags = "".join( | |
| f'<span class="tag tag-purple tag-sm">{esc(t)}</span>' | |
| for t in r.get("topics", [])[:5] | |
| ) | |
| if rtags: | |
| st.markdown(rtags, unsafe_allow_html=True) | |
| # Metrics | |
| h = r.get('h_index', 'N/A') | |
| cit = r.get('citation_count', 'N/A') | |
| wrk = r.get('works_count', 'N/A') | |
| st.markdown(f"""<div style="margin-top:10px;font-size:13px;color:#64748b;display:flex;gap:20px;flex-wrap:wrap;"> | |
| <span>H-index <b style="color:#cbd5e1;">{h}</b></span> | |
| <span>Citations <b style="color:#cbd5e1;">{cit}</b></span> | |
| <span>Works <b style="color:#cbd5e1;">{wrk}</b></span> | |
| </div>""", unsafe_allow_html=True) | |
| # AI Reasoning | |
| if reason: | |
| st.markdown(f"""<div style="padding:10px 14px;background:rgba(99,102,241,0.06); | |
| border-left:3px solid #818cf8;border-radius:0 12px 12px 0;font-size:13px;color:#94a3b8; | |
| font-style:italic;margin-top:12px;line-height:1.7;">{reason}</div>""", unsafe_allow_html=True) | |
| # COI | |
| for flag in r.get("coi_flags", []): | |
| sev = flag.get("severity", "medium") | |
| coi_bg = "rgba(239,68,68,0.1)" if sev in ("high","critical") else "rgba(245,158,11,0.1)" | |
| coi_color = "#f87171" if sev in ("high","critical") else "#fbbf24" | |
| coi_border = "rgba(239,68,68,0.2)" if sev in ("high","critical") else "rgba(245,158,11,0.2)" | |
| st.markdown(f'<div class="coi-flag" style="background:{coi_bg};color:{coi_color};border:1px solid {coi_border};">⚠️ {esc(flag.get("detail",""))}</div>', unsafe_allow_html=True) | |
| # Contact — email row | |
| if email: | |
| email_html = ( | |
| '<div style="display:flex;align-items:center;gap:10px;margin-top:14px;flex-wrap:wrap;">' | |
| '<span style="font-size:13px;">\u2709\uFE0F</span>' | |
| f'<a href="mailto:{esc(email)}" style="font-size:13px;font-weight:700;color:#a5b4fc;text-decoration:none;border-bottom:1px dashed rgba(99,102,241,0.3);">{esc(email)}</a>' | |
| '</div>' | |
| ) | |
| st.markdown(email_html, unsafe_allow_html=True) | |
| # Contact — profile links (separate st.markdown to avoid sanitizer issues) | |
| link_items = [] | |
| for lbl, key in [("ORCID","orcid_url"),("OpenAlex","openalex_url"),("Homepage","homepage")]: | |
| url = contact.get(key,"") | |
| if url: | |
| link_items.append(f'<a href="{esc(url)}" target="_blank" class="profile-link">{lbl}</a>') | |
| if link_items: | |
| links_html = " ".join(link_items) | |
| st.markdown(f'<div style="display:flex;gap:6px;margin-top:8px;flex-wrap:wrap;">{links_html}</div>', unsafe_allow_html=True) | |
| with col_scores: | |
| st.markdown("<div style='height:4px'></div>", unsafe_allow_html=True) | |
| _score_bars(r) | |
| # ── Invite ──────────────────────────────────────────────────── | |
| invite_key = f"invite_{rank}" | |
| already_sent = st.session_state.invite_sent.get(invite_key, False) | |
| if email and not already_sent: | |
| inv_c1, inv_c2 = st.columns([5, 1]) | |
| with inv_c2: | |
| if st.button("📨 Invite", key=f"btn_{invite_key}"): | |
| st.session_state.invite_open[invite_key] = not st.session_state.invite_open.get(invite_key, False) | |
| st.rerun() | |
| elif already_sent: | |
| st.markdown('<div style="text-align:right;padding:2px 0;"><span class="sent-badge">✓ Invite Sent</span></div>', unsafe_allow_html=True) | |
| if st.session_state.invite_open.get(invite_key, False) and email: | |
| paper_title = st.session_state.parsed.get("title", "our manuscript") | |
| default_subject = f"Invitation to Review: {paper_title[:80]}" | |
| reviewer_topics = r.get('topics', ['this field'])[:3] | |
| default_body = ( | |
| f"Dear {r.get('name', 'Professor')},\n\n" | |
| f"We would like to invite you to serve as a peer reviewer for our manuscript titled " | |
| f"\"{paper_title}\".\n\n" | |
| f"Your expertise in {', '.join(reviewer_topics)} makes you an ideal " | |
| f"candidate for reviewing this work. We believe your insights would greatly strengthen " | |
| f"the quality of our research.\n\n" | |
| f"Please let us know if you would be available and willing to review this manuscript. " | |
| f"We would be happy to provide additional details about the submission.\n\n" | |
| f"Thank you for considering this request.\n\n" | |
| f"Best regards" | |
| ) | |
| invite_header_html = ( | |
| '<div class="invite-header">' | |
| '<div style="display:flex;align-items:center;gap:8px;">' | |
| '<span style="font-size:14px;">\U0001F4E8</span>' | |
| '<span style="font-size:14px;font-weight:700;color:#a5b4fc;">Compose Invite</span>' | |
| '<span style="color:#475569;">\u2192</span>' | |
| f'<span style="font-size:13px;font-weight:600;color:#e2e8f0;">{esc(email)}</span>' | |
| '</div>' | |
| '</div>' | |
| ) | |
| st.markdown(invite_header_html, unsafe_allow_html=True) | |
| subj = st.text_input("Subject", value=default_subject, key=f"subj_{invite_key}") | |
| body = st.text_area("Message", value=default_body, height=220, key=f"body_{invite_key}") | |
| send_cols = st.columns([3, 1, 1]) | |
| with send_cols[1]: | |
| if st.button("Send ✉️", key=f"send_{invite_key}", type="primary"): | |
| st.session_state.invite_sent[invite_key] = True | |
| st.session_state.invite_open[invite_key] = False | |
| st.rerun() | |
| with send_cols[2]: | |
| if st.button("Cancel", key=f"cancel_{invite_key}"): | |
| st.session_state.invite_open[invite_key] = False | |
| st.rerun() | |
| # ── Export ──────────────────────────────────────────────────────────── | |
| if reviewers: | |
| st.markdown("<div style='height:20px'></div>", unsafe_allow_html=True) | |
| export = [] | |
| for r in reviewers: | |
| c = r.get("contact", {}) | |
| email_val = c.get("email", "") | |
| email_note = " (AI inferred)" if c.get("email_is_inferred") else "" | |
| export.append({ | |
| "Rank": r.get("rank"), "Name": r.get("name"), | |
| "Institution": r.get("institution", ""), | |
| "Email": f"{email_val}{email_note}" if email_val else "", | |
| "Score": round(r.get("overall_score", 0), 1), | |
| "H-Index": r.get("h_index", ""), "Citations": r.get("citation_count", ""), | |
| "ORCID": c.get("orcid_url", ""), "OpenAlex": c.get("openalex_url", ""), | |
| }) | |
| df = pd.DataFrame(export) | |
| st.markdown('<div class="section-label">Export Results</div>', unsafe_allow_html=True) | |
| ec1, ec2, ec3 = st.columns([1, 1, 3]) | |
| with ec1: | |
| st.download_button("📥 Download CSV", df.to_csv(index=False), | |
| file_name=f"reviewers_{datetime.now().strftime('%Y%m%d')}.csv", mime="text/csv") | |
| with ec2: | |
| st.download_button("📊 Download Excel", _to_excel(df), | |
| file_name=f"reviewers_{datetime.now().strftime('%Y%m%d')}.xlsx", | |
| mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet") | |