Home.py

import base64
from pathlib import Path

import streamlit as st

from src.ui_style import apply_global_style

st.set_page_config(page_title="Home", layout="wide")
apply_global_style()

logo_path = Path(__file__).resolve().parent / "icons" / "logo.png"
logo_data_uri = ""
if logo_path.exists():
    logo_data_uri = "data:image/png;base64," + base64.b64encode(logo_path.read_bytes()).decode("ascii")

st.markdown(
    """
<div style="margin: 0.15rem 0 0.4rem 0;">
  <span class="pp-badge">Home</span>
</div>
""",
    unsafe_allow_html=True,
)

st.markdown(
    f"""
<section class="pp-main-card">
  <div class="pp-main-grid">
    <div>
      <h1 class="pp-main-title">Polymer Discovery Platform</h1>
      <p class="pp-main-copy">
        A unified platform for polymer research that combines property prediction, molecular visualization, and objective-driven candidate discovery to support faster, data-backed screening and selection decisions.
      </p>
    </div>
    <div class="pp-main-logo">
      {"<img src='" + logo_data_uri + "' alt='Platform logo' />" if logo_data_uri else ""}
    </div>
  </div>
</section>
""",
    unsafe_allow_html=True,
)

stats = [
    ("25+", "Properties"),
    ("13K+", "Real Polymers"),
    ("1M", "Virtual Polymers"),
]
stats_html = "".join(
    [
        f"""
<div class="pp-kpi-item">
  <p class="pp-kpi-value">{value}</p>
  <p class="pp-kpi-label">{label}</p>
</div>
"""
        for value, label in stats
    ]
)
st.markdown(f'<section class="pp-kpi-strip">{stats_html}</section>', unsafe_allow_html=True)

st.divider()
st.markdown("### Platform Modules")
st.caption(
    "Use the modules below to probe, predict, visualize, discover, and ground polymer decisions with literature evidence."
)

cards = [
    (
        "Property Probe",
        "Input a single SMILES or polymer name and retrieve predicted or available values for one target property. "
        "Best for quick validation before larger screening.",
        "pages/1_Property_Probe.py",
    ),
    (
        "Batch Prediction",
        "Upload or paste many SMILES and run bulk property prediction in one job. "
        "Useful when you want ranked outputs and exportable tables for downstream analysis.",
        "pages/2_Batch_Prediction.py",
    ),
    (
        "Molecular View",
        "Render 2D and 3D molecular structures, inspect composition, and download visual assets "
        "or MOL files for documentation and simulation setup.",
        "pages/3_Molecular_View.py",
    ),
    (
        "Discovery (Manual)",
        "Set hard constraints, objectives, trust/selection weights, and diversity settings directly. "
        "Designed for controlled multi-objective exploration with transparent parameter tuning.",
        "pages/4_Discovery_(Manual).py",
    ),
    (
        "Discovery (AI)",
        "Describe target behavior in natural language and let the LLM build discovery settings. "
        "You can run directly or inspect/edit the generated JSON in advanced mode.",
        "pages/5_Discovery_(AI).py",
    ),
    (
        "Novel SMILES Generation",
        "Sample new polymer SMILES with the pretrained RNN and filter out molecules already present "
        "in local datasets (EXP/MD/DFT/GC/POLYINFO/PI1M).",
        "pages/6_Novel_SMILES_Generation.py",
    ),
    (
        "Literature Search",
        "Search polymer papers, stage evidence records, inspect OA/PDF availability, and review structured material-property evidence before promotion.",
        "pages/7_Literature_Search.py",
    ),
    (
        "Feedback",
        "Send bug reports, feature requests, and usage feedback.",
        "pages/8_Feedback.py",
    ),
]

for i, (title, desc, page_path) in enumerate(cards, start=1):
    c1, c2 = st.columns([5, 1.1], vertical_alignment="center")
    page_exists = (Path(__file__).resolve().parent / page_path).exists()
    with c1:
        st.markdown(
            f"""
<div class="pp-module-card">
  <p class="pp-module-title">{title}</p>
  <p class="pp-module-copy">{desc}</p>
</div>
""",
            unsafe_allow_html=True,
        )
    with c2:
        if st.button("Open", type="primary", key=f"home_go_{i}", disabled=not page_exists):
            st.switch_page(page_path)

st.divider()
st.markdown(
    """
<section class="pp-lab-card">
  <div class="pp-lab-head">
    <span class="pp-lab-kicker">Research Partner</span>
    <h3 class="pp-lab-title">Developed by MONSTER Lab</h3>
    <p class="pp-lab-subtitle">
      Molecular/Nano-Scale Transport &amp; Energy Research Laboratory | College of Engineering, University of Notre Dame
    </p>
  </div>
  <p class="pp-lab-copy">
    The MONSTER Lab studies the
    physics of energy and mass transport across molecular and nano-scales using theory, simulation,
    data-driven methods, and experiments. The team translates these insights into materials and
    systems for thermal management, energy efficiency, water desalination, high-sensitivity biosensing,
    and additive manufacturing.
  </p>
  <a class="pp-lab-link" href="https://monsterlab.nd.edu/" target="_blank" rel="noopener noreferrer">
    Visit MONSTER Lab Website
  </a>
</section>
""",
    unsafe_allow_html=True,
)