app.py

"""

BioPrime Molecular Docking Demo

================================

Interactive demo showcasing AI-powered molecular docking for drug discovery.



Features:

- 3D protein structure visualization

- Compound library selection

- Docking simulation with binding energy results

- BSV blockchain verification display



Powered by: Origin Neural AI Docking Engine

Sponsored by: Smartledger Solutions, Origin Neural AI, Bryan Daugherty

Website: https://bioprime.one

"""

import gradio as gr
import requests
import json
import hashlib
import random
from datetime import datetime
from typing import Optional, Dict, List, Tuple
import time

# =============================================================================
# Configuration
# =============================================================================

BIOPRIME_API = "https://bioprime.one/api/v1"
RCSB_PDB_URL = "https://files.rcsb.org/download"

# Demo targets with sponsored research campaigns
DEMO_TARGETS = [
    {
        "id": "melanoma",
        "name": "BRAF V600E - Melanoma",
        "pdb": "4MNE",
        "sponsor": "Bryan Daugherty",
        "disease": "Melanoma",
        "description": "Mutated BRAF kinase found in ~50% of melanomas, target for vemurafenib-like inhibitors.",
        "binding_site": {"x": 25.0, "y": 5.0, "z": 15.0},
        "color": "#FF6B6B"
    },
    {
        "id": "diabetes",
        "name": "DPP-4 - Type 2 Diabetes",
        "pdb": "2ONC",
        "sponsor": "Bryan Daugherty",
        "disease": "Type 2 Diabetes",
        "description": "Dipeptidyl peptidase-4, target for incretin-based diabetes medications like sitagliptin.",
        "binding_site": {"x": 35.0, "y": 40.0, "z": 45.0},
        "color": "#4ECDC4"
    },
    {
        "id": "covid",
        "name": "COVID-19 Main Protease",
        "pdb": "6LU7",
        "sponsor": "BioPrime Community",
        "disease": "COVID-19",
        "description": "SARS-CoV-2 main protease (Mpro), essential for viral replication. Target for Paxlovid.",
        "binding_site": {"x": -10.8, "y": 35.2, "z": 63.4},
        "color": "#9B59B6"
    },
    {
        "id": "hiv",
        "name": "HIV-1 Protease",
        "pdb": "1HVR",
        "sponsor": "Origin Neural AI",
        "disease": "HIV/AIDS",
        "description": "Critical enzyme for HIV replication, target for protease inhibitor antiretroviral drugs.",
        "binding_site": {"x": -6.2, "y": 20.1, "z": 41.8},
        "color": "#E74C3C"
    },
    {
        "id": "lung",
        "name": "EGFR Kinase - Lung Cancer",
        "pdb": "1M17",
        "sponsor": "Smartledger & Origin Neural AI",
        "disease": "Non-small Cell Lung Cancer",
        "description": "Epidermal growth factor receptor, key target in NSCLC therapy. Target for erlotinib.",
        "binding_site": {"x": 40.5, "y": 0.6, "z": 56.0},
        "color": "#3498DB"
    },
    {
        "id": "breast",
        "name": "CDK4/6 - Breast Cancer",
        "pdb": "5L2I",
        "sponsor": "Smartledger",
        "disease": "Breast Cancer",
        "description": "Cyclin-dependent kinase 4/6 inhibitor target for hormone-receptor positive breast cancer.",
        "binding_site": {"x": 15.0, "y": 25.0, "z": 35.0},
        "color": "#E91E63"
    },
]

# Demo compound library
COMPOUND_LIBRARY = [
    {"id": "aspirin", "name": "Aspirin", "smiles": "CC(=O)OC1=CC=CC=C1C(=O)O", "mw": 180.16, "category": "Anti-inflammatory"},
    {"id": "ibuprofen", "name": "Ibuprofen", "smiles": "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O", "mw": 206.29, "category": "Anti-inflammatory"},
    {"id": "caffeine", "name": "Caffeine", "smiles": "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "mw": 194.19, "category": "Stimulant"},
    {"id": "paracetamol", "name": "Acetaminophen", "smiles": "CC(=O)NC1=CC=C(O)C=C1", "mw": 151.16, "category": "Analgesic"},
    {"id": "metformin", "name": "Metformin", "smiles": "CN(C)C(=N)NC(=N)N", "mw": 129.17, "category": "Antidiabetic"},
    {"id": "atorvastatin", "name": "Atorvastatin", "smiles": "CC(C)C1=C(C(=C(N1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4", "mw": 558.64, "category": "Statin"},
    {"id": "nirmatrelvir", "name": "Nirmatrelvir", "smiles": "CC1(CC1)C(=O)NC(CC2CCNC2=O)C(=O)NC(CC(F)(F)F)C#N", "mw": 499.53, "category": "Antiviral (COVID-19)"},
    {"id": "oseltamivir", "name": "Oseltamivir", "smiles": "CCOC(=O)C1=CC(OC(CC)CC)C(NC(C)=O)C(N)C1", "mw": 312.41, "category": "Antiviral (Flu)"},
    {"id": "remdesivir", "name": "Remdesivir", "smiles": "CCC(CC)COC(=O)C(C)NP(=O)(OCC1C(C(C(O1)N2C=CC(=O)NC2=O)O)O)OC3=CC=CC=C3", "mw": 602.58, "category": "Antiviral"},
    {"id": "sitagliptin", "name": "Sitagliptin", "smiles": "NC(CC(=O)N1CCN2C(C1)=NN=C2C(F)(F)F)CC1=C(F)C=C(F)C(F)=C1F", "mw": 407.31, "category": "Antidiabetic (DPP-4)"},
    {"id": "vemurafenib", "name": "Vemurafenib", "smiles": "CCCS(=O)(=O)NC1=CC=C(C=C1)C2=NC(=C(S2)C3=CC(=NC=C3)NC4=CC=C(C=C4)Cl)C#N", "mw": 489.93, "category": "Kinase Inhibitor (Melanoma)"},
    {"id": "erlotinib", "name": "Erlotinib", "smiles": "COCCOC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)OCCOC", "mw": 393.44, "category": "EGFR Inhibitor"},
]

# Pre-computed docking results (simulated but realistic)
PRECOMPUTED_RESULTS = {
    "melanoma": {
        "vemurafenib": -9.8,
        "erlotinib": -7.2,
        "caffeine": -4.1,
        "aspirin": -5.3,
        "ibuprofen": -5.8,
    },
    "diabetes": {
        "sitagliptin": -10.2,
        "metformin": -6.8,
        "caffeine": -4.5,
        "aspirin": -4.9,
        "ibuprofen": -5.1,
    },
    "covid": {
        "nirmatrelvir": -8.9,
        "remdesivir": -7.6,
        "caffeine": -5.2,
        "aspirin": -4.8,
        "oseltamivir": -6.4,
    },
    "hiv": {
        "remdesivir": -7.8,
        "oseltamivir": -6.2,
        "caffeine": -4.3,
        "aspirin": -4.5,
        "atorvastatin": -6.9,
    },
    "lung": {
        "erlotinib": -9.4,
        "vemurafenib": -7.1,
        "caffeine": -4.0,
        "aspirin": -4.7,
        "atorvastatin": -6.5,
    },
    "breast": {
        "atorvastatin": -7.3,
        "erlotinib": -6.8,
        "caffeine": -4.2,
        "aspirin": -4.4,
        "metformin": -5.1,
    },
}

# =============================================================================
# Helper Functions
# =============================================================================

def fetch_pdb_structure(pdb_id: str) -> Optional[str]:
    """Fetch PDB structure from RCSB."""
    try:
        # Try BioPrime API first
        response = requests.get(f"{BIOPRIME_API}/docking/demo/pdb/{pdb_id}", timeout=10)
        if response.status_code == 200:
            data = response.json()
            return data.get("pdb_content", data.get("pdb_data", None))
    except:
        pass

    # Fallback to RCSB
    try:
        response = requests.get(f"{RCSB_PDB_URL}/{pdb_id}.pdb", timeout=10)
        if response.status_code == 200:
            return response.text
    except:
        pass

    return None


def create_3d_viewer(pdb_content: str, binding_site: dict = None, style: str = "cartoon") -> str:
    """Create 3D molecular viewer HTML using iframe with srcdoc for JS execution."""
    import html
    import base64

    # Escape PDB content for JavaScript (double escape for iframe)
    pdb_escaped = pdb_content.replace('\\', '\\\\').replace('\n', '\\n').replace('\r', '').replace("'", "\\'").replace('"', '\\"')

    # Build style configuration
    if style == "cartoon":
        style_js = "viewer.setStyle({}, {cartoon: {color: 'spectrum'}});"
    elif style == "surface":
        style_js = "viewer.setStyle({}, {cartoon: {color: 'spectrum'}}); viewer.addSurface($3Dmol.SAS, {opacity: 0.7, color: 'white'});"
    elif style == "stick":
        style_js = "viewer.setStyle({}, {stick: {colorscheme: 'Jmol'}});"
    elif style == "sphere":
        style_js = "viewer.setStyle({}, {sphere: {colorscheme: 'Jmol', scale: 0.3}});"
    else:
        style_js = "viewer.setStyle({}, {cartoon: {color: 'spectrum'}});"

    # Binding site sphere
    binding_site_js = ""
    if binding_site:
        binding_site_js = f"viewer.addSphere({{center: {{x: {binding_site['x']}, y: {binding_site['y']}, z: {binding_site['z']}}}, radius: 8, color: 'red', opacity: 0.3}});"

    # Create complete HTML document for iframe
    iframe_html = f'''<!DOCTYPE html>

<html>

<head>

    <script src="https://3dmol.org/build/3Dmol-min.js"></script>

    <style>

        body {{ margin: 0; padding: 0; overflow: hidden; background: #1a1a2e; }}

        #viewer {{ width: 100%; height: 100%; }}

    </style>

</head>

<body>

    <div id="viewer"></div>

    <script>

        document.addEventListener('DOMContentLoaded', function() {{

            var pdbData = "{pdb_escaped}";

            var element = document.getElementById('viewer');

            var config = {{ backgroundColor: '0x1a1a2e' }};

            var viewer = $3Dmol.createViewer(element, config);

            viewer.addModel(pdbData, "pdb");

            {style_js}

            {binding_site_js}

            viewer.zoomTo();

            viewer.render();

        }});

    </script>

</body>

</html>'''

    # Escape for srcdoc attribute
    iframe_srcdoc = html.escape(iframe_html)

    html_content = f'''

    <div style="width: 100%; display: flex; justify-content: center;">

        <iframe srcdoc="{iframe_srcdoc}"

                style="width: 700px; height: 500px; border: none; border-radius: 12px; background: #1a1a2e;"

                sandbox="allow-scripts allow-same-origin">

        </iframe>

    </div>

    '''

    return html_content


def generate_docking_result(target_id: str, compound_ids: List[str]) -> Tuple[str, str, str]:
    """

    Simulate docking and return results.

    Returns: (results_text, binding_chart_data, receipt)

    """
    target = next((t for t in DEMO_TARGETS if t["id"] == target_id), None)
    if not target:
        return "Target not found", "", ""

    results = []
    precomputed = PRECOMPUTED_RESULTS.get(target_id, {})

    for comp_id in compound_ids:
        compound = next((c for c in COMPOUND_LIBRARY if c["id"] == comp_id), None)
        if not compound:
            continue

        # Use precomputed or generate realistic random
        if comp_id in precomputed:
            energy = precomputed[comp_id]
        else:
            # Generate realistic binding energy based on molecular weight
            base_energy = -4.0 - (compound["mw"] / 100)
            energy = round(base_energy + random.uniform(-1.5, 1.5), 2)

        results.append({
            "compound": compound["name"],
            "smiles": compound["smiles"],
            "energy": energy,
            "mw": compound["mw"],
            "category": compound["category"]
        })

    # Sort by binding energy (more negative = better)
    results.sort(key=lambda x: x["energy"])

    # Generate results text
    results_text = f"""

## Docking Results for {target['name']}



**Target Disease:** {target['disease']}

**Sponsor:** {target['sponsor']}

**PDB ID:** {target['pdb']}



---



### Top Binding Compounds



| Rank | Compound | Binding Energy | Category |

|------|----------|----------------|----------|

"""

    for i, r in enumerate(results[:10], 1):
        emoji = "🏆" if i == 1 else "🥈" if i == 2 else "🥉" if i == 3 else "  "
        results_text += f"| {emoji} {i} | **{r['compound']}** | {r['energy']:.2f} kcal/mol | {r['category']} |\n"

    results_text += f"""

---



### Interpretation



- **Best Hit:** {results[0]['compound']} with {results[0]['energy']:.2f} kcal/mol

- **Binding energies < -7 kcal/mol** indicate strong binding potential

- **Binding energies < -9 kcal/mol** suggest drug-like affinity



---



*Powered by Origin Neural AI Docking Engine*

*Results simulated for demonstration - actual BioPrime uses GPU-accelerated physics*

"""

    # Generate chart data
    chart_labels = [r["compound"][:12] for r in results[:8]]
    chart_values = [abs(r["energy"]) for r in results[:8]]

    chart_html = f"""

<div style="background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%); padding: 20px; border-radius: 12px; margin-top: 10px;">

    <h3 style="color: #4ECDC4; margin-bottom: 15px; text-align: center;">Binding Affinity Comparison</h3>

    <div style="display: flex; align-items: flex-end; justify-content: space-around; height: 200px; padding: 10px;">

"""

    max_val = max(chart_values) if chart_values else 1
    colors = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4", "#FFEAA7", "#DDA0DD", "#98D8C8", "#F7DC6F"]

    for i, (label, val) in enumerate(zip(chart_labels, chart_values)):
        height = int((val / max_val) * 150)
        color = colors[i % len(colors)]
        chart_html += f"""

        <div style="display: flex; flex-direction: column; align-items: center; width: 60px;">

            <span style="color: white; font-size: 11px; margin-bottom: 5px;">-{val:.1f}</span>

            <div style="width: 40px; height: {height}px; background: {color}; border-radius: 4px 4px 0 0;"></div>

            <span style="color: #888; font-size: 9px; margin-top: 5px; text-align: center; word-wrap: break-word; width: 55px;">{label}</span>

        </div>

"""

    chart_html += """

    </div>

    <p style="color: #666; font-size: 11px; text-align: center; margin-top: 10px;">Binding Energy (kcal/mol) - Higher bars = stronger binding</p>

</div>

"""

    # Add social sharing buttons
    share_text = f"I just screened compounds against {target['name']} using BioPrime! Best hit: {results[0]['compound']} at {results[0]['energy']:.2f} kcal/mol. Try AI-powered drug discovery:"
    share_url = "https://bioprime.one"

    import urllib.parse
    encoded_text = urllib.parse.quote(share_text)
    encoded_url = urllib.parse.quote(share_url)

    chart_html += f"""

<div style="background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%); padding: 15px; border-radius: 12px; margin-top: 10px;">

    <p style="color: #4ECDC4; font-size: 13px; text-align: center; margin-bottom: 12px; font-weight: 600;">Share Your Discovery</p>

    <div style="display: flex; justify-content: center; gap: 12px; flex-wrap: wrap;">

        <a href="https://twitter.com/intent/tweet?text={encoded_text}&url={encoded_url}" target="_blank"

           style="display: inline-flex; align-items: center; gap: 6px; padding: 8px 16px; background: #1DA1F2; color: white; text-decoration: none; border-radius: 6px; font-size: 13px; font-weight: 500;">

            <svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor"><path d="M18.244 2.25h3.308l-7.227 8.26 8.502 11.24H16.17l-5.214-6.817L4.99 21.75H1.68l7.73-8.835L1.254 2.25H8.08l4.713 6.231zm-1.161 17.52h1.833L7.084 4.126H5.117z"/></svg>

            Post on X

        </a>

        <a href="https://www.linkedin.com/sharing/share-offsite/?url={encoded_url}" target="_blank"

           style="display: inline-flex; align-items: center; gap: 6px; padding: 8px 16px; background: #0A66C2; color: white; text-decoration: none; border-radius: 6px; font-size: 13px; font-weight: 500;">

            <svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor"><path d="M20.447 20.452h-3.554v-5.569c0-1.328-.027-3.037-1.852-3.037-1.853 0-2.136 1.445-2.136 2.939v5.667H9.351V9h3.414v1.561h.046c.477-.9 1.637-1.85 3.37-1.85 3.601 0 4.267 2.37 4.267 5.455v6.286zM5.337 7.433c-1.144 0-2.063-.926-2.063-2.065 0-1.138.92-2.063 2.063-2.063 1.14 0 2.064.925 2.064 2.063 0 1.139-.925 2.065-2.064 2.065zm1.782 13.019H3.555V9h3.564v11.452zM22.225 0H1.771C.792 0 0 .774 0 1.729v20.542C0 23.227.792 24 1.771 24h20.451C23.2 24 24 23.227 24 22.271V1.729C24 .774 23.2 0 22.222 0h.003z"/></svg>

            LinkedIn

        </a>

        <a href="https://www.facebook.com/sharer/sharer.php?u={encoded_url}" target="_blank"

           style="display: inline-flex; align-items: center; gap: 6px; padding: 8px 16px; background: #1877F2; color: white; text-decoration: none; border-radius: 6px; font-size: 13px; font-weight: 500;">

            <svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor"><path d="M24 12.073c0-6.627-5.373-12-12-12s-12 5.373-12 12c0 5.99 4.388 10.954 10.125 11.854v-8.385H7.078v-3.47h3.047V9.43c0-3.007 1.792-4.669 4.533-4.669 1.312 0 2.686.235 2.686.235v2.953H15.83c-1.491 0-1.956.925-1.956 1.874v2.25h3.328l-.532 3.47h-2.796v8.385C19.612 23.027 24 18.062 24 12.073z"/></svg>

            Facebook

        </a>

    </div>

    <p style="color: #666; font-size: 11px; text-align: center; margin-top: 12px;">

        Want real blockchain-verified results? <a href="https://bioprime.one" target="_blank" style="color: #4ECDC4;">Sign up at bioprime.one</a>

    </p>

</div>

"""

    # Generate blockchain receipt
    job_id = f"DEMO-{target_id.upper()}-{hashlib.md5(str(compound_ids).encode()).hexdigest()[:8]}"
    data_hash = hashlib.sha256(json.dumps(results, sort_keys=True).encode()).hexdigest()

    receipt = generate_demo_receipt(
        job_id=job_id,
        target_name=target['name'],
        compounds_screened=len(compound_ids),
        top_hits=len(results),
        best_energy=results[0]['energy'] if results else 0,
        data_hash=data_hash
    )

    return results_text, chart_html, receipt


def generate_demo_receipt(job_id: str, target_name: str, compounds_screened: int,

                          top_hits: int, best_energy: float, data_hash: str) -> str:
    """Generate a demo blockchain receipt."""
    timestamp = datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC")
    demo_txid = f"demo_{hashlib.md5(data_hash.encode()).hexdigest()[:48]}"

    receipt = f"""

╔══════════════════════════════════════════════════════════════════════════╗

║                    BIOPRIME DISCOVERY CERTIFICATE                        ║

║                         [DEMO - NOT ON CHAIN]                            ║

╠══════════════════════════════════════════════════════════════════════════╣

║                                                                          ║

║  JOB ID:      {job_id:<57} ║

║  TARGET:      {target_name[:47]:<57} ║

║                                                                          ║

╠══════════════════════════════════════════════════════════════════════════╣

║                          SCREENING RESULTS                               ║

╠══════════════════════════════════════════════════════════════════════════╣

║                                                                          ║

║  Compounds Screened:     {compounds_screened:<45} ║

║  Top Poses Generated:    {top_hits:<45} ║

║  Best Binding Energy:    {best_energy:.2f} kcal/mol{' ':<36} ║

║                                                                          ║

╠══════════════════════════════════════════════════════════════════════════╣

║                        VERIFICATION DETAILS                              ║

╠══════════════════════════════════════════════════════════════════════════╣

║                                                                          ║

║  Timestamp:      {timestamp:<53} ║

║  Data Hash:      sha256:{data_hash[:49]:<46} ║

║                                                                          ║

╠══════════════════════════════════════════════════════════════════════════╣

║                       BLOCKCHAIN VERIFICATION                            ║

╠══════════════════════════════════════════════════════════════════════════╣

║                                                                          ║

║  Network:        BSV (Bitcoin SV) - Demo Mode                            ║

║  Status:         Demo certificate - not anchored to blockchain           ║

║                                                                          ║

║  For real blockchain-verified results, visit:                            ║

║  https://bioprime.one                                                    ║

║                                                                          ║

╠══════════════════════════════════════════════════════════════════════════╣

║                                                                          ║

║  BioPrime anchors real docking results to the BSV blockchain for         ║

║  immutable proof of discovery. Sign up to run verified experiments.      ║

║                                                                          ║

║                         ━━━ bioprime.one ━━━                              ║

║                                                                          ║

╚══════════════════════════════════════════════════════════════════════════╝

"""
    return receipt.strip()


# =============================================================================
# Gradio Interface
# =============================================================================

def view_protein(target_name: str, view_style: str) -> Tuple[str, str]:
    """View selected protein structure."""
    target = next((t for t in DEMO_TARGETS if t["name"] == target_name), None)
    if not target:
        return "<p>Please select a target</p>", ""

    pdb_content = fetch_pdb_structure(target["pdb"])
    if not pdb_content:
        return f"<p style='color: red;'>Failed to fetch PDB structure for {target['pdb']}</p>", ""

    viewer_html = create_3d_viewer(pdb_content, target.get("binding_site"), view_style.lower())

    info_html = f"""

<div style="background: linear-gradient(135deg, #0f0c29 0%, #302b63 50%, #24243e 100%); padding: 20px; border-radius: 12px; color: white;">

    <h3 style="color: {target['color']}; margin-bottom: 10px;">{target['name']}</h3>

    <p><strong>PDB ID:</strong> <a href="https://www.rcsb.org/structure/{target['pdb']}" target="_blank" style="color: #4ECDC4;">{target['pdb']}</a></p>

    <p><strong>Disease:</strong> {target['disease']}</p>

    <p><strong>Sponsor:</strong> {target['sponsor']}</p>

    <p style="margin-top: 10px; color: #aaa;">{target['description']}</p>

    <div style="margin-top: 15px; padding: 10px; background: rgba(78, 205, 196, 0.1); border-radius: 8px; border-left: 3px solid #4ECDC4;">

        <p style="font-size: 12px; color: #4ECDC4; margin: 0;">

            💡 The red sphere indicates the active binding site where drug candidates interact with the protein.

        </p>

    </div>

</div>

"""

    return viewer_html, info_html


def run_docking(target_name: str, compounds: List[str]) -> Tuple[str, str, str]:
    """Run docking simulation."""
    if not target_name:
        return "Please select a target protein", "", ""
    if not compounds:
        return "Please select at least one compound", "", ""

    target = next((t for t in DEMO_TARGETS if t["name"] == target_name), None)
    if not target:
        return "Invalid target", "", ""

    # Get compound IDs from names
    compound_ids = []
    for comp_name in compounds:
        comp = next((c for c in COMPOUND_LIBRARY if c["name"] == comp_name), None)
        if comp:
            compound_ids.append(comp["id"])

    # Simulate docking (small delay for effect)
    time.sleep(1.5)

    results_text, chart_html, receipt = generate_docking_result(target["id"], compound_ids)

    return results_text, chart_html, receipt


# Create the Gradio interface
with gr.Blocks(
    title="BioPrime Molecular Docking Demo",
    theme=gr.themes.Base(
        primary_hue="teal",
        secondary_hue="purple",
        neutral_hue="slate",
        font=gr.themes.GoogleFont("Inter")
    ),
    css="""

    .gradio-container {

        max-width: 1400px !important;

        background: linear-gradient(135deg, #0f0c29 0%, #302b63 50%, #24243e 100%) !important;

    }

    .gr-button-primary {

        background: linear-gradient(135deg, #4ECDC4 0%, #44A08D 100%) !important;

    }

    .header-text {

        text-align: center;

        color: white;

    }

    footer {display: none !important;}

    """
) as demo:

    # Header
    gr.HTML("""

    <div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 12px; margin-bottom: 20px;">

        <h1 style="color: white; margin: 0; font-size: 2.5em;">🧬 BioPrime</h1>

        <p style="color: rgba(255,255,255,0.9); margin: 10px 0 0 0; font-size: 1.2em;">

            AI-Powered Molecular Docking for Drug Discovery

        </p>

        <p style="color: rgba(255,255,255,0.7); margin: 5px 0 0 0;">

            10,000x Faster • $5 per Million Compounds • Blockchain-Verified Results

        </p>

    </div>

    """)

    with gr.Tabs():
        # Tab 1: Interactive Docking Demo
        with gr.TabItem("🔬 Try Docking", id="docking"):
            gr.Markdown("""

            ### Dock Drug Candidates Against Disease Targets

            Select a protein target and compounds to simulate molecular docking. See binding energies and get a blockchain-ready certificate.

            """)

            with gr.Row():
                with gr.Column(scale=1):
                    target_dropdown = gr.Dropdown(
                        choices=[t["name"] for t in DEMO_TARGETS],
                        label="🎯 Select Disease Target",
                        info="Choose from sponsored research targets"
                    )

                    compound_select = gr.CheckboxGroup(
                        choices=[c["name"] for c in COMPOUND_LIBRARY],
                        label="💊 Select Compounds to Test",
                        info="Choose multiple compounds for screening"
                    )

                    dock_btn = gr.Button("🚀 Run Docking Simulation", variant="primary", size="lg")

                    gr.HTML("""

                    <div style="margin-top: 15px; padding: 15px; background: rgba(78, 205, 196, 0.1); border-radius: 8px; border: 1px solid rgba(78, 205, 196, 0.3);">

                        <h4 style="color: #4ECDC4; margin: 0 0 10px 0;">💡 Quick Start</h4>

                        <ol style="color: #aaa; margin: 0; padding-left: 20px; font-size: 13px;">

                            <li>Select <strong>BRAF V600E - Melanoma</strong></li>

                            <li>Check <strong>Vemurafenib</strong> (the actual drug!)</li>

                            <li>Add a few other compounds to compare</li>

                            <li>Click Run Docking</li>

                        </ol>

                    </div>

                    """)

                with gr.Column(scale=2):
                    results_md = gr.Markdown("*Results will appear here after docking...*")
                    chart_html = gr.HTML()

            with gr.Accordion("📜 Blockchain Certificate (Demo)", open=False):
                receipt_text = gr.Code(label="Discovery Certificate", language=None, lines=30)

            dock_btn.click(
                fn=run_docking,
                inputs=[target_dropdown, compound_select],
                outputs=[results_md, chart_html, receipt_text]
            )

        # Tab 2: 3D Protein Viewer
        with gr.TabItem("🔮 3D Protein Viewer", id="viewer"):
            gr.Markdown("""

            ### Explore Protein Structures in 3D

            Visualize the molecular targets for drug discovery. The red sphere indicates the binding site.

            """)

            with gr.Row():
                with gr.Column(scale=1):
                    viewer_target = gr.Dropdown(
                        choices=[t["name"] for t in DEMO_TARGETS],
                        label="🎯 Select Protein",
                        value=DEMO_TARGETS[0]["name"]
                    )

                    view_style = gr.Radio(
                        choices=["Cartoon", "Surface", "Stick", "Sphere"],
                        value="Cartoon",
                        label="🎨 Visualization Style"
                    )

                    view_btn = gr.Button("👁️ View Structure", variant="primary")

                    target_info = gr.HTML()

                with gr.Column(scale=2):
                    viewer_output = gr.HTML(
                        value="<div style='height: 500px; display: flex; align-items: center; justify-content: center; color: #666; background: #1a1a2e; border-radius: 12px;'><p>Select a protein and click 'View Structure'</p></div>"
                    )

            view_btn.click(
                fn=view_protein,
                inputs=[viewer_target, view_style],
                outputs=[viewer_output, target_info]
            )

        # Tab 3: About BioPrime
        with gr.TabItem("ℹ️ About", id="about"):
            gr.Markdown("""

            ## About BioPrime Network



            BioPrime is a **decentralized molecular docking platform** that makes drug discovery accessible to everyone.



            ### Key Features



            | Feature | Traditional | BioPrime |

            |---------|-------------|----------|

            | Speed | Days-Weeks | Minutes |

            | Cost | $10,000+ | $5/million |

            | Verification | Manual | Blockchain |

            | Access | Limited | Open |



            ### How It Works



            1. **Submit** - Upload your protein target or select from our library

            2. **Screen** - Our Origin Neural AI engine docks millions of compounds

            3. **Discover** - Get ranked binding poses with energy scores

            4. **Verify** - Results anchored to BSV blockchain for immutable proof



            ### Sponsored Research Campaigns



            BioPrime features sponsored research targets where community members can contribute to drug discovery:



            - **Bryan Daugherty** - Melanoma (BRAF V600E), Type 2 Diabetes (DPP-4)

            - **Smartledger** - Breast Cancer (CDK4/6)

            - **Origin Neural AI** - HIV-1 Protease

            - **Greg Ward** - Tuberculosis, Dengue Fever

            - **Shawn Ryan** - Alzheimer's, Parkinson's



            ### Technology Stack



            - **Docking Engine**: Origin Neural AI (GPU-accelerated)

            - **Blockchain**: BSV (Bitcoin SV) for immutable verification

            - **Backend**: FastAPI, Python

            - **Frontend**: React, TypeScript



            ---



            ### Get Started



            **🌐 Visit [bioprime.one](https://bioprime.one) to run real docking experiments!**



            - Sign up for free (10 free credits)

            - Screen up to 1 million compounds per job

            - Get blockchain-verified discovery certificates

            - Participate in sponsored research campaigns



            ---



            <div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 12px;">

                <p style="color: white; font-size: 1.2em; margin: 0;">

                    <strong>Ready to discover the next breakthrough drug?</strong>

                </p>

                <p style="color: rgba(255,255,255,0.8); margin: 10px 0 0 0;">

                    <a href="https://bioprime.one" target="_blank" style="color: #4ECDC4; text-decoration: none; font-size: 1.3em;">

                        🚀 Launch BioPrime →

                    </a>

                </p>

            </div>

            """)

    # Footer
    gr.HTML("""

    <div style="text-align: center; padding: 20px; margin-top: 20px; border-top: 1px solid rgba(255,255,255,0.1);">

        <p style="color: #666; margin: 0;">

            <a href="https://bioprime.one" target="_blank" style="color: #4ECDC4;">bioprime.one</a>

        </p>

        <p style="color: #444; margin: 5px 0 0 0; font-size: 12px;">

            Powered by Origin Neural AI • Blockchain verification on BSV

        </p>

    </div>

    """)


if __name__ == "__main__":
    demo.launch()