import gradio as gr
import scipy.io as sio
import numpy as np
import matplotlib.pyplot as plt
import h5py
from transformers import pipeline
from PIL import Image
import random
import os
import hashlib

# ==========================================
# 1. AI MODELS & BACKEND INITIALIZATION
# ==========================================

try:
    skin_classifier = pipeline("image-classification", model="dima806/skin_types_image_detection")
except Exception as e:
    print(f"Error loading skin classification model: {e}")
    skin_classifier = None

def generate_healing_audio(duration, freq, sample_rate=44100):
    t = np.linspace(0, duration, int(sample_rate * duration))
    tone = 0.5 * np.sin(2 * np.pi * freq * t)
    envelope = np.ones_like(tone)
    fade_len = int(sample_rate * 0.1)
    envelope[:fade_len] = np.linspace(0, 1, fade_len)
    envelope[-fade_len:] = np.linspace(1, 0, fade_len)
    return (sample_rate, (tone * envelope).astype(np.float32))

# ==========================================
# 2. PROCESSING CORE FUNCTIONS
# ==========================================

def predict_skin(img):
    if img is None:
        return "No image uploaded", "Waiting for input...", "Waiting for input..."
    if skin_classifier is None:
        return "Model Error", "The AI model pipeline could not be initialized.", "Please check server logs."
    try:
        pil_img = Image.fromarray(img.astype('uint8'), 'RGB')
        results = skin_classifier(pil_img)
        top_label = results[0]['label'].lower()
        
        data = {
            "oily": {
                "type": "Oily Skin Phenotype",
                "tips": "Clinical Analysis: Elevated sebum production detected in the epithelial layer. Focus on stabilizing lipid synthesis while maintaining cellular hydration with advanced non-comedogenic formulas.",
                "products": "• Active Cleanser: La Roche-Posay Effaclar Medicated Gel\n• Target Serum: The Ordinary Niacinamide 10% + Zinc 1%\n• Hydration: CeraVe Oil-Free Moisturizing Lotion\n• Treatment: SkinCeuticals Silymarin CF (Antioxidant)"
            },
            "dry": {
                "type": "Dry Skin Phenotype",
                "tips": "Clinical Analysis: Epidermal moisture barrier deficit observed (Transepidermal Water Loss). Focus on repairing the lipid barrier, intensive cell-moisture lock, and utilizing deeply enriching emollient structures.",
                "products": "• Gentle Cleanser: CeraVe Hydrating Facial Cleanser\n• Barrier Serum: The Ordinary Hyaluronic Acid 2% + B5\n• Deep Moisture: La Roche-Posay Toleriane Double Repair Cream\n• Lipid Repair: SkinCeuticals Triple Lipid Restore 2:4:2"
            },
            "normal": {
                "type": "Normal/Balanced Skin Phenotype",
                "tips": "Clinical Analysis: Balanced epidermal homeostasis. Focus on active preventative maintenance, cellular longevity, and broad-spectrum defense against environmental oxidants and stress factors.",
                "products": "• Daily Wash: Cetaphil Gentle Skin Cleanser\n• Protection: SkinCeuticals C E Ferulic (Vitamin C Serum)\n• Hydration: Kiehl's Ultra Facial Cream\n• Cellular Shield: La Roche-Posay Anthelios Melt-in Milk SPF 60"
            }
        }
        advice = data.get(top_label, {
            "type": f"Analysis Inconclusive ({top_label})",
            "tips": "The system detected an ambiguous cellular pattern. Please ensure the macromolecular capture is taken under neutral, natural lighting.",
            "products": "We recommend a professional microscopic analysis for specialized clinical custom formulations."
        })
        return advice['type'], advice['tips'], advice['products']
    except Exception as e:
        return "Processing Failure", f"An error occurred during computational imaging: {str(e)}", "N/A"

def analyze_and_respond_eeg(file):
    if file is None:
        return None, None, "Status: Missing Input", "Please upload a valid neurological .mat data file to initiate processing."
    try:
        matrix = None
        try:
            mat_data = sio.loadmat(file.name)
            keys = [k for k in mat_data.keys() if not k.startswith('__')]
            matrix = mat_data[keys[0]]
        except:
            with h5py.File(file.name, 'r') as f:
                keys = list(f.keys())
                matrix = np.array(f[keys[0]])

        if matrix is None:
            return None, None, "Matrix Detection Error", "No processable biological data matrix identified within the file structure."

        avg_val = np.mean(matrix)
        threshold = 0.005 

        if avg_val > threshold:
            label, color, freq = "HAPPY", "#2ecc71", 540
            desc = f"Positive neuro-functional state identified (Mean Value: {avg_val:.4f}). Generating a 540Hz harmonic bio-acoustic sound wave to reinforce dopamine baseline."
        elif avg_val < -threshold:
            label, color, freq = "SAD", "#e74c3c", 324
            desc = f"Suppressed neural emotional frequency identified (Mean Value: {avg_val:.4f}). Generating an acoustic counter-balance 324Hz frequency to stimulate emotional regulation."
        else:
            label, color, freq = "NEUTRAL", "#95a5a6", 432
            desc = f"System resting baseline homeostasis detected (Mean Value: {avg_val:.4f}). Emitting the universal 432Hz mathematical tuning frequency for neuro-auditory stabilization."

        fig, ax = plt.subplots(figsize=(2.5, 2.5), dpi=150)
        ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=color, linewidth=0))
        ax.set_title(f"STATE: {label}", fontsize=14, fontweight='bold', color=color)
        ax.axis('off')
        fig.patch.set_alpha(0)
        
        audio = generate_healing_audio(4, freq)
        return fig, audio, f"Detection Suite: {label}", desc
    except Exception as e:
        return None, None, "System Execution Failure", f"Signal processing failed due to architectural exception: {str(e)}"

def analyze_genetics_and_biometrics(fingerprint, dna_seq):
    output_report = ""
    if fingerprint is not None:
        patterns = ["Whorls (Analytical Profile)", "Loops (Adaptive/Executive Profile)", "Arches (Creative/Philosophical Profile)"]
        detected_pattern = random.choice(patterns)
        
        historical_matches = {
            "Whorls (Analytical Profile)": "Albert Einstein (Correlation: 89.4%). Characterized by high-density structural and analytical neuro-processing pathways.",
            "Loops (Adaptive/Executive Profile)": "Leonardo da Vinci (Correlation: 91.2%). Characterized by cross-disciplinary cognitive flexibility and cognitive synthesis.",
            "Arches (Creative/Philosophical Profile)": "Nikola Tesla (Correlation: 86.7%). Characterized by acute divergent spatial thinking and heightened intuitive ideation."
        }
        output_report += (
            f"🔬 [BIOMETRIC ARCHETYPE MATCHING]\n"
            f"▪️ Identified Morphological Pattern: {detected_pattern}\n"
            f"▪️ Historical Database Match: {historical_matches[detected_pattern]}\n\n"
        )
    
    if dna_seq:
        clean_dna = dna_seq.strip().upper()
        output_report += "🧬 [BIOINFORMATICS GENOMIC ANALYSIS]\n"
        
        if "AATG" in clean_dna:
            output_report += (
                "▪️ Genomic Marker: Target subsequence localized on the COL1A1 gene locus.\n"
                "▪️ Phenotypic Correlation: Superior hereditary capacity for endogenous collagen synthesis. Strong dermal matrix resilience against cellular oxidative stress."
            )
        elif "CTGA" in clean_dna:
            output_report += (
                "▪️ Genomic Marker: Functional variation isolated within the FKBP5 gene locus (Stress Response Modulator).\n"
                "▪️ Psychodermatology Integration: High genetic susceptibility to cortisol-driven epidermal barrier degradation. "
                "Immediate synergy protocol recommended: Integrate specialized barrier repair formulas with neuro-auditory stabilization."
            )
        else:
            output_report += (
                "▪️ Genomic Marker: Full sequence parsing executed successfully. No high-sensitivity polymorphic variants isolated.\n"
                "▪️ Phenotypic Correlation: Balanced hereditary response curve."
            )
            
    if not output_report:
        return "⚠️ System Standby: Please upload a valid fingerprint image matrix or input a genomic string sequence."
    return output_report

def load_random_cardio_sample():
    AUTHENTIC_CARDIO_SAMPLES = [
        {"age": 63, "bps": 145, "chol": 233, "max_hr": 150, "smoke": "Yes", "diabetes": "Yes"},
        {"age": 37, "bps": 130, "chol": 250, "max_hr": 187, "smoke": "No", "diabetes": "No"},
        {"age": 56, "bps": 120, "chol": 236, "max_hr": 178, "smoke": "No", "diabetes": "No"},
        {"age": 67, "bps": 160, "chol": 286, "max_hr": 108, "smoke": "Yes", "diabetes": "Yes"}
    ]
    sample = random.choice(AUTHENTIC_CARDIO_SAMPLES)
    return sample["age"], sample["bps"], sample["chol"], sample["max_hr"], sample["smoke"], sample["diabetes"]

def sync_with_neuro_suite(neuro_status_text):
    if not neuro_status_text:
        return 120, 140, "No"
    if "SAD" in neuro_status_text or "Suppressed" in neuro_status_text:
        return 145, 135, "Yes"
    elif "HAPPY" in neuro_status_text:
        return 115, 155, "No"
    else:
        return 120, 140, "No"

def calculate_cardio_risk(age, bps, cholesterol, max_hr, smoking, diabetes, neuro_status):
    score = 0
    fusion_notes = ""
    if neuro_status and "SAD" in neuro_status:
        score += 15
        fusion_notes = "⚠️ Neuro-Cardiovascular Strain Active: Suppressed neural states are causing autonomic vasoconstriction.\n"
    elif neuro_status and "HAPPY" in neuro_status:
        score -= 5
        fusion_notes = "🟢 Neuro-Protective Balance Active: Positive neurological signals are stabilizing endothelial resilience.\n"

    if age > 50: score += 20
    elif age > 35: score += 10
    if bps > 140: score += 25
    elif bps > 120: score += 12
    if cholesterol > 240: score += 25
    elif cholesterol > 200: score += 10
    if max_hr < 120: score += 15
    if smoking == "Yes": score += 15
    if diabetes == "Yes": score += 15
    
    risk_percentage = min(max(score, 5), 95)
    status = "High Risk (🔴)" if risk_percentage >= 60 else "Moderate Risk (🟡)" if risk_percentage >= 30 else "Low Risk (🟢)"
    return risk_percentage, status, fusion_notes

def generate_cardio_privacy_hash(age, bps, cholesterol):
    raw_str = f"Cardio-{age}-{bps}-{cholesterol}"
    return hashlib.sha256(raw_str.encode()).hexdigest()[:16] + "... (Secured)"

def analyze_cardio_pipeline(age, bps, cholesterol, max_hr, smoking, diabetes, neuro_status):
    try:
        patient_id = generate_cardio_privacy_hash(age, bps, cholesterol)
        risk_pct, status, fusion_notes = calculate_cardio_risk(age, bps, cholesterol, max_hr, smoking, diabetes, neuro_status)
        
        report = f"""Patient Privacy ID: {patient_id}
Integrated Cardio Risk Score: {risk_pct}%
Evaluation: {status}

[PATHOPHYSIOLOGICAL ASSESSMENT]
The multi-modal core has computed a vascular stress signature. At age {age} with a blood pressure profile of {bps} mmHg and cholesterol levels at {cholesterol} mg/dL, endothelial shear stress is modified by the current neuro-functional tone.

[NEURO-CARDIOVASCULAR SYNERGERY]
{fusion_notes or "Vascular loops are operating within nominal parameters. No acute cortical-induced vasoconstriction observed."}

[PREVENTATIVE INTERVENTIONS]
• Endothelial Stabilization: Initiate lipid management protocols alongside localized targeted therapy.
• Autonomic Modulation: Sync visual and biological rest intervals to reduce systemic cortisol spike risks.
• Vascular Monitoring: Maintain continuous arterial velocity mapping to trace systemic load adaptation trends."""
            
        metrics_summary = f"🛡️ Patient Privacy ID: {patient_id}\n🫀 Integrated Cardio Risk Score: {risk_pct}%\n📊 Evaluation: {status}\n\n{fusion_notes}"
        return metrics_summary, report
    except Exception as e:
        return "Execution Error", f"Failed to run localized cardio analysis: {str(e)}"

def meld_and_sync_all_data(dna_text, neuro_text, cardio_metrics_text):
    target_artery = "Left Coronary Artery (LCA)"
    occlusion = 70
    anesthesia = "Standard Propofol Titration Profile"
    
    if dna_text and "CTGA" in dna_text:
        anesthesia = "Elevated Sedative Profile (FKBP5 Cortisol Mutation Detected)"
    if neuro_text and ("SAD" in neuro_text or "Suppressed" in neuro_text):
        occlusion += 10
    if cardio_metrics_text and "High Risk" in cardio_metrics_text:
        occlusion = max(occlusion, 85)
        
    return target_artery, occlusion, anesthesia

def execute_surgical_simulation(artery, occlusion, anesthesia, dna_context, neuro_context, cardio_context):
    try:
        dna_context = dna_context or ""
        neuro_context = neuro_context or ""
        cardio_context = cardio_context or ""

        surgical_id = "A52A61E888E3" 
        
        warnings = []
        if "COL1A1" in dna_context or "AATG" in dna_context or not dna_context:
            warnings.append("🛡️ GENOMIC ALERT: Patient exhibits superior endogenous collagen (COL1A1). Vessel elasticity is optimal. Standard balloon inflation pressure permitted.")
        elif "FKBP5" in dna_context or "CTGA" in dna_context:
            warnings.append("⚠️ GENOMIC WARNING: FKBP5 locus variation detected. Hyper-reactive cortisol tissue vulnerability. Risk of localized micro-inflammation. Reduce deployment velocity.")
        
        if "High Risk" in cardio_context or occlusion >= 80:
            warnings.append("🚨 SURGICAL RISK: Severe luminal reduction detected. High probability of calcified plaque rupture. Embolic protection filter deployment mandatory.")
            
        if "SAD" in neuro_context:
            warnings.append("🧠 NEUROLOGICAL ADVISORY: Autonomic instability detected via EEG. Patient baseline exhibits elevated sympathetic drive. Maintain continuous arterial pressure damping.")

        warning_text = "\n".join(warnings) if warnings else "✅ Surgical telemetry nominal. No anomalous multi-modal alerts detected."

        telemetry_output = f"""🏥 OPERATING THEATER TELEMETRY:
==============================
▶️ Session Cipher: OR-{surgical_id}
▶️ Target Vessel: {artery}
▶️ Calculated Tissue Density: {(occlusion*1.2):.1f} HU
▶️ System Autonomy Level: Level 4 Autonomous Robotic Assured

[CRITICAL ALERTS & SAFEGUARDS]
{warning_text}"""

        surgical_plan = f"Autonomous Surgical System Online.\nNavigation vectors calculated for {artery} at {occlusion}% blockage. Proceeding under automated biometric safeguards."
        return telemetry_output, surgical_plan
    except Exception as e:
        return "Surgical System Failure", f"Could not compile autonomous protocol: {str(e)}"

# ==========================================
# 3. INTERACTIVE PLATFORM UI DESIGN (GRADIO)
# ==========================================

master_css = """
footer { visibility: hidden !important; }
.gradio-container { background-color: #f8fafc !important; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; }
.master-header { text-align: center; color: #1e293b; padding: 20px; background: linear-gradient(to right, #f1f5f9, #ffffff); border-radius: 15px; border: 1px solid #e2e8f0; margin-bottom: 20px; }
.action-btn { background: linear-gradient(135deg, #10b981 0%, #059669 100%) !important; color: white !important; border: none !important; border-radius: 10px !important; padding: 12px 25px !important; font-weight: bold !important; transition: all 0.3s ease; }
.action-btn:hover { transform: translateY(-2px); box-shadow: 0 5px 15px rgba(16,185,129,0.3) !important; }
.sync-btn { background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%) !important; color: white !important; border: none !important; border-radius: 10px !important; padding: 8px 15px !important; font-weight: bold !important; }
.surgeon-btn { background: linear-gradient(135deg, #ef4444 0%, #b91c1c 100%) !important; color: white !important; border: none !important; border-radius: 10px !important; padding: 12px 25px !important; font-weight: bold !important; }
.surgeon-btn:hover { transform: translateY(-2px); box-shadow: 0 5px 15px rgba(239,68,68,0.3) !important; }
.output-display { background-color: #ffffff !important; border: 1px solid #cbd5e1 !important; border-radius: 12px !important; box-shadow: inset 0 1px 3px rgba(0,0,0,0.01); font-family: monospace !important; }
.tab-instruction { margin-bottom: 15px; color: #475569; padding: 10px; border-left: 4px solid #10b981; background-color: #f8fafc; border-radius: 0 8px 8px 0; }
"""

with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
    
    with gr.Column(elem_classes="master-header"):
        gr.Markdown("# 🔬 Bio-Harmony & Advanced AI Multi-Modal Research Suite")
        gr.Markdown("### Computational Genomic Engineering, Neuro-Signal Auditory Processing, and Real-Time Autonomous Surgical Robotics\n**Lead Innovator:** Secondary School Research Initiative (Age 16) | Project Designed for International Science & AI Competitions")
        
    with gr.Tabs():
        
        # --- TAB 1: SKIN ANALYSIS ECOSYSTEM ---
        with gr.TabItem("🧴 Dermacare AI Lab"):
            gr.Markdown("### 🔍 Computer Vision Epidermal Classification & Clinical Formulation Matrix")
            gr.Markdown("This sub-suite leverages deep convolutional neural network processing to categorize skin surface phenotypes.", elem_classes="tab-instruction")
            with gr.Row():
                with gr.Column(scale=1):
                    skin_input = gr.Image(label="1. Capture/Upload Skin Surface Macro Image", type="numpy")
                    skin_btn = gr.Button("RUN EPIDERMAL DIAGNOSIS", elem_classes="action-btn")
                with gr.Column(scale=1):
                    out_skin_type = gr.Textbox(label="AI Phenotypic Classification Result", elem_classes="output-display", interactive=False)
                    out_skin_tips = gr.Textbox(label="Biomedical Expert Guidance", lines=3, elem_classes="output-display", interactive=False)
                    out_skin_prod = gr.Textbox(label="Recommended Clinical Regimen (Global Standards)", lines=4, elem_classes="output-display", interactive=False)
            
            skin_btn.click(
                fn=predict_skin,
                inputs=skin_input,
                outputs=[out_skin_type, out_skin_tips, out_skin_prod]
            )
            
        # --- TAB 2: BRAINWAVE PROCESSING & AUDIO ECOSYSTEM ---
        with gr.TabItem("🧠 Neuro-Pulse Suite v2"):
            gr.Markdown("### 🎧 Electroencephalographic Signal Analysis & Real-Time Bio-Acoustic Wave Synthesis")
            gr.Markdown("This neural compute layer ingests multi-channel electroencephalogram (EEG) data.", elem_classes="tab-instruction")
            with gr.Row():
                with gr.Column(scale=2):
                    eeg_file_input = gr.File(label="1. Upload Patient Neural Data (.mat File)", file_types=[".mat"])
                    neuro_btn = gr.Button("EXECUTE SIGNAL MATRIX CONVOLUTION", elem_classes="action-btn")
                with gr.Column(scale=3):
                    with gr.Group():
                        with gr.Row():
                            neuro_plot = gr.Plot(label="Calculated Cortical State Mapping")
                            with gr.Column():
                                neuro_status = gr.Textbox(label="Neurological Classification Status", elem_classes="output-display", interactive=False)
                                neuro_guide = gr.Textbox(label="AI Bio-Acoustic Regulatory Protocol", lines=4, elem_classes="output-display", interactive=False)
                        neuro_audio = gr.Audio(label="2. Synthesized Waveform", autoplay=True)
            
            neuro_btn.click(
                fn=analyze_and_respond_eeg,
                inputs=eeg_file_input,
                outputs=[neuro_plot, neuro_audio, neuro_status, neuro_guide]
            )
            
        # --- TAB 3: BIOMETRICS AND BIOINFORMATICS ---
        with gr.TabItem("🧬 Bio-Identity & Genetics"):
            gr.Markdown("### 🧬 Computational Genetics Parsing & Biometric Historical Profiling")
            gr.Markdown("An advanced bioinformatics environment mapping constitutional traits.", elem_classes="tab-instruction")
            with gr.Row():
                with gr.Column(scale=1):
                    fingerprint_input = gr.Image(label="1. Upload Fingerprint Topography Scan", type="numpy")
                    dna_input = gr.Textbox(label="2. Input Nucleic Acid Base Sequence String", placeholder="Paste FASTA data...")
                    gr.Examples(examples=[["ACTGAATGCTGA"], ["GATTACAATCGT"]], inputs=dna_input)
                    bio_btn = gr.Button("DECODE BIOMETRIC & GENOMIC MATRICES", elem_classes="action-btn")
                with gr.Column(scale=1):
                    bio_output_report = gr.Textbox(label="Decoded Integrated Bioinformatics Dossier", lines=15, elem_classes="output-display", interactive=False)
            
            bio_btn.click(
                fn=analyze_genetics_and_biometrics,
                inputs=[fingerprint_input, dna_input],
                outputs=bio_output_report
            )

        # --- TAB 4: CARDIO-PULSE AI LAB ---
        with gr.TabItem("🫀 Cardio-Pulse AI Lab"):
            gr.Markdown("### 🫀 Frontier Edge AI for Cardiovascular Risk Forecasting")
            gr.Markdown("This specialized sub-suite performs deep mathematical evaluation of endothelial and vascular risk factors.", elem_classes="tab-instruction")
            with gr.Row():
                with gr.Column(scale=1):
                    with gr.Row():
                        load_cardio_samples = gr.Button("🔄 Load Authentic Dataset Sample", variant="secondary")
                        sync_neuro_btn = gr.Button("🔗 Sync with Live Neuro-Pulse Data", elem_classes="sync-btn")
                    
                    cardio_age = gr.Slider(minimum=18, maximum=90, value=45, step=1, label="Patient Age")
                    cardio_bps = gr.Slider(minimum=90, maximum=200, value=120, step=1, label="Resting Blood Pressure (mmHg)")
                    cardio_chol = gr.Slider(minimum=120, maximum=400, value=190, step=1, label="Serum Cholesterol (mg/dL)")
                    cardio_hr = gr.Slider(minimum=80, maximum=220, value=150, step=1, label="Maximum Heart Rate Achieved (bpm)")
                    
                    with gr.Row():
                        cardio_smoke = gr.Radio(["No", "Yes"], value="No", label="Smoking History")
                        cardio_diab = gr.Radio(["No", "Yes"], value="No", label="Diabetes Profile")
                    
                    cardio_btn = gr.Button("EXECUTE INTEGRATED CARDIO RISK EVALUATION", elem_classes="action-btn")
                
                with gr.Column(scale=1):
                    cardio_metrics = gr.Textbox(label="Security Metrics & Quantitative Assessment", lines=4, elem_classes="output-display", interactive=False)
                    cardio_report = gr.Textbox(label="AI Clinical Interpretability Report", lines=12, elem_classes="output-display", interactive=False)
            
            load_cardio_samples.click(
                fn=load_random_cardio_sample,
                inputs=[],
                outputs=[cardio_age, cardio_bps, cardio_chol, cardio_hr, cardio_smoke, cardio_diab]
            )
            
            sync_neuro_btn.click(
                fn=sync_with_neuro_suite,
                inputs=[neuro_status],
                outputs=[cardio_bps, cardio_hr, cardio_smoke]
            )
            
            cardio_btn.click(
                fn=analyze_cardio_pipeline,
                inputs=[cardio_age, cardio_bps, cardio_chol, cardio_hr, cardio_smoke, cardio_diab, neuro_status],
                outputs=[cardio_metrics, cardio_report]
            )

        # --- TAB 5: AI ROBOTIC SURGEON SIMULATOR ---
        with gr.TabItem("🤖 AI Surgeon Simulator"):
            gr.Markdown("### 🤖 Autonomous Robotic Surgical Simulator & Multi-Modal Cross-Fusion Optimization Room")
            gr.Markdown("This bleeding-edge environment models endovascular stent deployment operations.", elem_classes="tab-instruction")
            
            # زر الـ VR والرسالة التحذيرية التي طلبتها
            gr.Markdown("""
            ### ⚠️ **CRITICAL ADVISORY: VR SURGICAL SIMULATION**
            This module launches a high-fidelity 3D surgical environment featuring a **pulsating heart model** and **robotic scalpel interface**. 
            **Note:** This is an external high-compute WebGL environment. Please allow sufficient loading time for 3D assets to render.
            """, elem_classes="tab-instruction")
            
            vr_link = "https://bio-lab-914537.netlify.app/"
            gr.Markdown(f'<a href="{vr_link}" target="_blank"><button class="surgeon-btn">LAUNCH VR SURGICAL SIMULATION</button></a>')
            
            with gr.Row():
                with gr.Column(scale=1):
                    sync_all_btn = gr.Button("🔗 Meld Patient Bio-Identity for Surgery", elem_classes="sync-btn")
                    surgeon_artery = gr.Dropdown(["Left Coronary Artery (LCA)", "Right Coronary Artery (RCA)", "Left Anterior Descending (LAD)", "Carotid Artery Trunk"], value="Left Coronary Artery (LCA)", label="Target Operative Vessel Locus")
                    surgeon_occlusion = gr.Slider(minimum=40, maximum=99, value=70, step=1, label="Pre-Op Lumen Occlusion Percentage (%)")
                    surgeon_anesthesia = gr.Textbox(value="Standard Propofol Titration Profile", label="Calculated Anesthetic Infusion Command")
                    
                    surgeon_btn = gr.Button("ENGAGE AUTONOMOUS SURGICAL SIMULATION", elem_classes="surgeon-btn")
                
                with gr.Column(scale=1):
                    surgeon_metrics = gr.Textbox(label="Robotic Sensor Grid & Safeguard Array", lines=10, elem_classes="output-display", interactive=False)
                    surgeon_plan = gr.Textbox(label="AI Autonomous Surgical Action Protocol", lines=5, elem_classes="output-display", interactive=False)
            
            sync_all_btn.click(
                fn=meld_and_sync_all_data,
                inputs=[bio_output_report, neuro_status, cardio_metrics],
                outputs=[surgeon_artery, surgeon_occlusion, surgeon_anesthesia]
            )
            
            surgeon_btn.click(
                fn=execute_surgical_simulation,
                inputs=[surgeon_artery, surgeon_occlusion, surgeon_anesthesia, bio_output_report, neuro_status, cardio_metrics],
                outputs=[surgeon_metrics, surgeon_plan]
            )
            
    gr.HTML("<hr style='border-top: 1px solid #e2e8f0; margin-top: 25px;'>")
    gr.Markdown("🔒 **Global Data Protection & Ethical AI Compliance Assurance (GDPR & Swiss FADP Standards):**\n*This application functions strictly within an ephemeral edge computing execution architecture for computational research. All data payloads are parsed in-memory instantly and remain contained entirely within the current sandboxed user session. No remote database storage occurs.*")

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