Upload Home.py

Home.py

@@ -0,0 +1,158 @@
+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,
+)