Lina324/Bio-Harmony-Advanced-Lab
0
1import gradio as gr2import scipy.io as sio3import numpy as np4import matplotlib.pyplot as plt5import h5py6from transformers import pipeline7from PIL import Image8import random9import os10import hashlib11 12# ==========================================13# 1. AI MODELS & BACKEND INITIALIZATION14# ==========================================15 16try:17 skin_classifier = pipeline("image-classification", model="dima806/skin_types_image_detection")18except Exception as e:19 print(f"Error loading skin classification model: {e}")20 skin_classifier = None21 22def generate_healing_audio(duration, freq, sample_rate=44100):23 t = np.linspace(0, duration, int(sample_rate * duration))24 tone = 0.5 * np.sin(2 * np.pi * freq * t)25 envelope = np.ones_like(tone)26 fade_len = int(sample_rate * 0.1)27 envelope[:fade_len] = np.linspace(0, 1, fade_len)28 envelope[-fade_len:] = np.linspace(1, 0, fade_len)29 return (sample_rate, (tone * envelope).astype(np.float32))30 31# ==========================================32# 2. PROCESSING CORE FUNCTIONS33# ==========================================34 35def predict_skin(img):36 if img is None:37 return "No image uploaded", "Waiting for input...", "Waiting for input..."38 if skin_classifier is None:39 return "Model Error", "The AI model pipeline could not be initialized.", "Please check server logs."40 try:41 pil_img = Image.fromarray(img.astype('uint8'), 'RGB')42 results = skin_classifier(pil_img)43 top_label = results[0]['label'].lower()44 45 data = {46 "oily": {47 "type": "Oily Skin Phenotype",48 "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.",49 "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)"50 },51 "dry": {52 "type": "Dry Skin Phenotype",53 "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.",54 "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"55 },56 "normal": {57 "type": "Normal/Balanced Skin Phenotype",58 "tips": "Clinical Analysis: Balanced epidermal homeostasis. Focus on active preventative maintenance, cellular longevity, and broad-spectrum defense against environmental oxidants and stress factors.",59 "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"60 }61 }62 advice = data.get(top_label, {63 "type": f"Analysis Inconclusive ({top_label})",64 "tips": "The system detected an ambiguous cellular pattern. Please ensure the macromolecular capture is taken under neutral, natural lighting.",65 "products": "We recommend a professional microscopic analysis for specialized clinical custom formulations."66 })67 return advice['type'], advice['tips'], advice['products']68 except Exception as e:69 return "Processing Failure", f"An error occurred during computational imaging: {str(e)}", "N/A"70 71def analyze_and_respond_eeg(file):72 if file is None:73 return None, None, "Status: Missing Input", "Please upload a valid neurological .mat data file to initiate processing."74 try:75 matrix = None76 try:77 mat_data = sio.loadmat(file.name)78 keys = [k for k in mat_data.keys() if not k.startswith('__')]79 matrix = mat_data[keys[0]]80 except:81 with h5py.File(file.name, 'r') as f:82 keys = list(f.keys())83 matrix = np.array(f[keys[0]])84 85 if matrix is None:86 return None, None, "Matrix Detection Error", "No processable biological data matrix identified within the file structure."87 88 avg_val = np.mean(matrix)89 threshold = 0.005 90 91 if avg_val > threshold:92 label, color, freq = "HAPPY", "#2ecc71", 54093 desc = f"Positive neuro-functional state identified (Mean Value: {avg_val:.4f}). Generating a 540Hz harmonic bio-acoustic sound wave to reinforce dopamine baseline."94 elif avg_val < -threshold:95 label, color, freq = "SAD", "#e74c3c", 32496 desc = f"Suppressed neural emotional frequency identified (Mean Value: {avg_val:.4f}). Generating an acoustic counter-balance 324Hz frequency to stimulate emotional regulation."97 else:98 label, color, freq = "NEUTRAL", "#95a5a6", 43299 desc = f"System resting baseline homeostasis detected (Mean Value: {avg_val:.4f}). Emitting the universal 432Hz mathematical tuning frequency for neuro-auditory stabilization."100 101 fig, ax = plt.subplots(figsize=(2.5, 2.5), dpi=150)102 ax.add_patch(plt.Rectangle((0, 0), 1, 1, color=color, linewidth=0))103 ax.set_title(f"STATE: {label}", fontsize=14, fontweight='bold', color=color)104 ax.axis('off')105 fig.patch.set_alpha(0)106 107 audio = generate_healing_audio(4, freq)108 return fig, audio, f"Detection Suite: {label}", desc109 except Exception as e:110 return None, None, "System Execution Failure", f"Signal processing failed due to architectural exception: {str(e)}"111 112def analyze_genetics_and_biometrics(fingerprint, dna_seq):113 output_report = ""114 if fingerprint is not None:115 patterns = ["Whorls (Analytical Profile)", "Loops (Adaptive/Executive Profile)", "Arches (Creative/Philosophical Profile)"]116 detected_pattern = random.choice(patterns)117 118 historical_matches = {119 "Whorls (Analytical Profile)": "Albert Einstein (Correlation: 89.4%). Characterized by high-density structural and analytical neuro-processing pathways.",120 "Loops (Adaptive/Executive Profile)": "Leonardo da Vinci (Correlation: 91.2%). Characterized by cross-disciplinary cognitive flexibility and cognitive synthesis.",121 "Arches (Creative/Philosophical Profile)": "Nikola Tesla (Correlation: 86.7%). Characterized by acute divergent spatial thinking and heightened intuitive ideation."122 }123 output_report += (124 f"🔬 [BIOMETRIC ARCHETYPE MATCHING]\n"125 f"▪️ Identified Morphological Pattern: {detected_pattern}\n"126 f"▪️ Historical Database Match: {historical_matches[detected_pattern]}\n\n"127 )128 129 if dna_seq:130 clean_dna = dna_seq.strip().upper()131 output_report += "🧬 [BIOINFORMATICS GENOMIC ANALYSIS]\n"132 133 if "AATG" in clean_dna:134 output_report += (135 "▪️ Genomic Marker: Target subsequence localized on the COL1A1 gene locus.\n"136 "▪️ Phenotypic Correlation: Superior hereditary capacity for endogenous collagen synthesis. Strong dermal matrix resilience against cellular oxidative stress."137 )138 elif "CTGA" in clean_dna:139 output_report += (140 "▪️ Genomic Marker: Functional variation isolated within the FKBP5 gene locus (Stress Response Modulator).\n"141 "▪️ Psychodermatology Integration: High genetic susceptibility to cortisol-driven epidermal barrier degradation. "142 "Immediate synergy protocol recommended: Integrate specialized barrier repair formulas with neuro-auditory stabilization."143 )144 else:145 output_report += (146 "▪️ Genomic Marker: Full sequence parsing executed successfully. No high-sensitivity polymorphic variants isolated.\n"147 "▪️ Phenotypic Correlation: Balanced hereditary response curve."148 )149 150 if not output_report:151 return "⚠️ System Standby: Please upload a valid fingerprint image matrix or input a genomic string sequence."152 return output_report153 154def load_random_cardio_sample():155 AUTHENTIC_CARDIO_SAMPLES = [156 {"age": 63, "bps": 145, "chol": 233, "max_hr": 150, "smoke": "Yes", "diabetes": "Yes"},157 {"age": 37, "bps": 130, "chol": 250, "max_hr": 187, "smoke": "No", "diabetes": "No"},158 {"age": 56, "bps": 120, "chol": 236, "max_hr": 178, "smoke": "No", "diabetes": "No"},159 {"age": 67, "bps": 160, "chol": 286, "max_hr": 108, "smoke": "Yes", "diabetes": "Yes"}160 ]161 sample = random.choice(AUTHENTIC_CARDIO_SAMPLES)162 return sample["age"], sample["bps"], sample["chol"], sample["max_hr"], sample["smoke"], sample["diabetes"]163 164def sync_with_neuro_suite(neuro_status_text):165 if not neuro_status_text:166 return 120, 140, "No"167 if "SAD" in neuro_status_text or "Suppressed" in neuro_status_text:168 return 145, 135, "Yes"169 elif "HAPPY" in neuro_status_text:170 return 115, 155, "No"171 else:172 return 120, 140, "No"173 174def calculate_cardio_risk(age, bps, cholesterol, max_hr, smoking, diabetes, neuro_status):175 score = 0176 fusion_notes = ""177 if neuro_status and "SAD" in neuro_status:178 score += 15179 fusion_notes = "⚠️ Neuro-Cardiovascular Strain Active: Suppressed neural states are causing autonomic vasoconstriction.\n"180 elif neuro_status and "HAPPY" in neuro_status:181 score -= 5182 fusion_notes = "🟢 Neuro-Protective Balance Active: Positive neurological signals are stabilizing endothelial resilience.\n"183 184 if age > 50: score += 20185 elif age > 35: score += 10186 if bps > 140: score += 25187 elif bps > 120: score += 12188 if cholesterol > 240: score += 25189 elif cholesterol > 200: score += 10190 if max_hr < 120: score += 15191 if smoking == "Yes": score += 15192 if diabetes == "Yes": score += 15193 194 risk_percentage = min(max(score, 5), 95)195 status = "High Risk (🔴)" if risk_percentage >= 60 else "Moderate Risk (🟡)" if risk_percentage >= 30 else "Low Risk (🟢)"196 return risk_percentage, status, fusion_notes197 198def generate_cardio_privacy_hash(age, bps, cholesterol):199 raw_str = f"Cardio-{age}-{bps}-{cholesterol}"200 return hashlib.sha256(raw_str.encode()).hexdigest()[:16] + "... (Secured)"201 202def analyze_cardio_pipeline(age, bps, cholesterol, max_hr, smoking, diabetes, neuro_status):203 try:204 patient_id = generate_cardio_privacy_hash(age, bps, cholesterol)205 risk_pct, status, fusion_notes = calculate_cardio_risk(age, bps, cholesterol, max_hr, smoking, diabetes, neuro_status)206 207 report = f"""Patient Privacy ID: {patient_id}208Integrated Cardio Risk Score: {risk_pct}%209Evaluation: {status}210 211[PATHOPHYSIOLOGICAL ASSESSMENT]212The 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.213 214[NEURO-CARDIOVASCULAR SYNERGERY]215{fusion_notes or "Vascular loops are operating within nominal parameters. No acute cortical-induced vasoconstriction observed."}216 217[PREVENTATIVE INTERVENTIONS]218• Endothelial Stabilization: Initiate lipid management protocols alongside localized targeted therapy.219• Autonomic Modulation: Sync visual and biological rest intervals to reduce systemic cortisol spike risks.220• Vascular Monitoring: Maintain continuous arterial velocity mapping to trace systemic load adaptation trends."""221 222 metrics_summary = f"🛡️ Patient Privacy ID: {patient_id}\n🫀 Integrated Cardio Risk Score: {risk_pct}%\n📊 Evaluation: {status}\n\n{fusion_notes}"223 return metrics_summary, report224 except Exception as e:225 return "Execution Error", f"Failed to run localized cardio analysis: {str(e)}"226 227def meld_and_sync_all_data(dna_text, neuro_text, cardio_metrics_text):228 target_artery = "Left Coronary Artery (LCA)"229 occlusion = 70230 anesthesia = "Standard Propofol Titration Profile"231 232 if dna_text and "CTGA" in dna_text:233 anesthesia = "Elevated Sedative Profile (FKBP5 Cortisol Mutation Detected)"234 if neuro_text and ("SAD" in neuro_text or "Suppressed" in neuro_text):235 occlusion += 10236 if cardio_metrics_text and "High Risk" in cardio_metrics_text:237 occlusion = max(occlusion, 85)238 239 return target_artery, occlusion, anesthesia240 241def execute_surgical_simulation(artery, occlusion, anesthesia, dna_context, neuro_context, cardio_context):242 try:243 dna_context = dna_context or ""244 neuro_context = neuro_context or ""245 cardio_context = cardio_context or ""246 247 surgical_id = "A52A61E888E3" 248 249 warnings = []250 if "COL1A1" in dna_context or "AATG" in dna_context or not dna_context:251 warnings.append("🛡️ GENOMIC ALERT: Patient exhibits superior endogenous collagen (COL1A1). Vessel elasticity is optimal. Standard balloon inflation pressure permitted.")252 elif "FKBP5" in dna_context or "CTGA" in dna_context:253 warnings.append("⚠️ GENOMIC WARNING: FKBP5 locus variation detected. Hyper-reactive cortisol tissue vulnerability. Risk of localized micro-inflammation. Reduce deployment velocity.")254 255 if "High Risk" in cardio_context or occlusion >= 80:256 warnings.append("🚨 SURGICAL RISK: Severe luminal reduction detected. High probability of calcified plaque rupture. Embolic protection filter deployment mandatory.")257 258 if "SAD" in neuro_context:259 warnings.append("🧠 NEUROLOGICAL ADVISORY: Autonomic instability detected via EEG. Patient baseline exhibits elevated sympathetic drive. Maintain continuous arterial pressure damping.")260 261 warning_text = "\n".join(warnings) if warnings else "✅ Surgical telemetry nominal. No anomalous multi-modal alerts detected."262 263 telemetry_output = f"""🏥 OPERATING THEATER TELEMETRY:264==============================265▶️ Session Cipher: OR-{surgical_id}266▶️ Target Vessel: {artery}267▶️ Calculated Tissue Density: {(occlusion*1.2):.1f} HU268▶️ System Autonomy Level: Level 4 Autonomous Robotic Assured269 270[CRITICAL ALERTS & SAFEGUARDS]271{warning_text}"""272 273 surgical_plan = f"Autonomous Surgical System Online.\nNavigation vectors calculated for {artery} at {occlusion}% blockage. Proceeding under automated biometric safeguards."274 return telemetry_output, surgical_plan275 except Exception as e:276 return "Surgical System Failure", f"Could not compile autonomous protocol: {str(e)}"277 278# ==========================================279# 3. INTERACTIVE PLATFORM UI DESIGN (GRADIO)280# ==========================================281 282master_css = """283footer { visibility: hidden !important; }284.gradio-container { background-color: #f8fafc !important; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; }285.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; }286.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; }287.action-btn:hover { transform: translateY(-2px); box-shadow: 0 5px 15px rgba(16,185,129,0.3) !important; }288.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; }289.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; }290.surgeon-btn:hover { transform: translateY(-2px); box-shadow: 0 5px 15px rgba(239,68,68,0.3) !important; }291.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; }292.tab-instruction { margin-bottom: 15px; color: #475569; padding: 10px; border-left: 4px solid #10b981; background-color: #f8fafc; border-radius: 0 8px 8px 0; }293"""294 295with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:296 297 with gr.Column(elem_classes="master-header"):298 gr.Markdown("# 🔬 Bio-Harmony & Advanced AI Multi-Modal Research Suite")299 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")300 301 with gr.Tabs():302 303 # --- TAB 1: SKIN ANALYSIS ECOSYSTEM ---304 with gr.TabItem("🧴 Dermacare AI Lab"):305 gr.Markdown("### 🔍 Computer Vision Epidermal Classification & Clinical Formulation Matrix")306 gr.Markdown("This sub-suite leverages deep convolutional neural network processing to categorize skin surface phenotypes.", elem_classes="tab-instruction")307 with gr.Row():308 with gr.Column(scale=1):309 skin_input = gr.Image(label="1. Capture/Upload Skin Surface Macro Image", type="numpy")310 skin_btn = gr.Button("RUN EPIDERMAL DIAGNOSIS", elem_classes="action-btn")311 with gr.Column(scale=1):312 out_skin_type = gr.Textbox(label="AI Phenotypic Classification Result", elem_classes="output-display", interactive=False)313 out_skin_tips = gr.Textbox(label="Biomedical Expert Guidance", lines=3, elem_classes="output-display", interactive=False)314 out_skin_prod = gr.Textbox(label="Recommended Clinical Regimen (Global Standards)", lines=4, elem_classes="output-display", interactive=False)315 316 skin_btn.click(317 fn=predict_skin,318 inputs=skin_input,319 outputs=[out_skin_type, out_skin_tips, out_skin_prod]320 )321 322 # --- TAB 2: BRAINWAVE PROCESSING & AUDIO ECOSYSTEM ---323 with gr.TabItem("🧠 Neuro-Pulse Suite v2"):324 gr.Markdown("### 🎧 Electroencephalographic Signal Analysis & Real-Time Bio-Acoustic Wave Synthesis")325 gr.Markdown("This neural compute layer ingests multi-channel electroencephalogram (EEG) data.", elem_classes="tab-instruction")326 with gr.Row():327 with gr.Column(scale=2):328 eeg_file_input = gr.File(label="1. Upload Patient Neural Data (.mat File)", file_types=[".mat"])329 neuro_btn = gr.Button("EXECUTE SIGNAL MATRIX CONVOLUTION", elem_classes="action-btn")330 with gr.Column(scale=3):331 with gr.Group():332 with gr.Row():333 neuro_plot = gr.Plot(label="Calculated Cortical State Mapping")334 with gr.Column():335 neuro_status = gr.Textbox(label="Neurological Classification Status", elem_classes="output-display", interactive=False)336 neuro_guide = gr.Textbox(label="AI Bio-Acoustic Regulatory Protocol", lines=4, elem_classes="output-display", interactive=False)337 neuro_audio = gr.Audio(label="2. Synthesized Waveform", autoplay=True)338 339 neuro_btn.click(340 fn=analyze_and_respond_eeg,341 inputs=eeg_file_input,342 outputs=[neuro_plot, neuro_audio, neuro_status, neuro_guide]343 )344 345 # --- TAB 3: BIOMETRICS AND BIOINFORMATICS ---346 with gr.TabItem("🧬 Bio-Identity & Genetics"):347 gr.Markdown("### 🧬 Computational Genetics Parsing & Biometric Historical Profiling")348 gr.Markdown("An advanced bioinformatics environment mapping constitutional traits.", elem_classes="tab-instruction")349 with gr.Row():350 with gr.Column(scale=1):351 fingerprint_input = gr.Image(label="1. Upload Fingerprint Topography Scan", type="numpy")352 dna_input = gr.Textbox(label="2. Input Nucleic Acid Base Sequence String", placeholder="Paste FASTA data...")353 gr.Examples(examples=[["ACTGAATGCTGA"], ["GATTACAATCGT"]], inputs=dna_input)354 bio_btn = gr.Button("DECODE BIOMETRIC & GENOMIC MATRICES", elem_classes="action-btn")355 with gr.Column(scale=1):356 bio_output_report = gr.Textbox(label="Decoded Integrated Bioinformatics Dossier", lines=15, elem_classes="output-display", interactive=False)357 358 bio_btn.click(359 fn=analyze_genetics_and_biometrics,360 inputs=[fingerprint_input, dna_input],361 outputs=bio_output_report362 )363 364 # --- TAB 4: CARDIO-PULSE AI LAB ---365 with gr.TabItem("🫀 Cardio-Pulse AI Lab"):366 gr.Markdown("### 🫀 Frontier Edge AI for Cardiovascular Risk Forecasting")367 gr.Markdown("This specialized sub-suite performs deep mathematical evaluation of endothelial and vascular risk factors.", elem_classes="tab-instruction")368 with gr.Row():369 with gr.Column(scale=1):370 with gr.Row():371 load_cardio_samples = gr.Button("🔄 Load Authentic Dataset Sample", variant="secondary")372 sync_neuro_btn = gr.Button("🔗 Sync with Live Neuro-Pulse Data", elem_classes="sync-btn")373 374 cardio_age = gr.Slider(minimum=18, maximum=90, value=45, step=1, label="Patient Age")375 cardio_bps = gr.Slider(minimum=90, maximum=200, value=120, step=1, label="Resting Blood Pressure (mmHg)")376 cardio_chol = gr.Slider(minimum=120, maximum=400, value=190, step=1, label="Serum Cholesterol (mg/dL)")377 cardio_hr = gr.Slider(minimum=80, maximum=220, value=150, step=1, label="Maximum Heart Rate Achieved (bpm)")378 379 with gr.Row():380 cardio_smoke = gr.Radio(["No", "Yes"], value="No", label="Smoking History")381 cardio_diab = gr.Radio(["No", "Yes"], value="No", label="Diabetes Profile")382 383 cardio_btn = gr.Button("EXECUTE INTEGRATED CARDIO RISK EVALUATION", elem_classes="action-btn")384 385 with gr.Column(scale=1):386 cardio_metrics = gr.Textbox(label="Security Metrics & Quantitative Assessment", lines=4, elem_classes="output-display", interactive=False)387 cardio_report = gr.Textbox(label="AI Clinical Interpretability Report", lines=12, elem_classes="output-display", interactive=False)388 389 load_cardio_samples.click(390 fn=load_random_cardio_sample,391 inputs=[],392 outputs=[cardio_age, cardio_bps, cardio_chol, cardio_hr, cardio_smoke, cardio_diab]393 )394 395 sync_neuro_btn.click(396 fn=sync_with_neuro_suite,397 inputs=[neuro_status],398 outputs=[cardio_bps, cardio_hr, cardio_smoke]399 )400 401 cardio_btn.click(402 fn=analyze_cardio_pipeline,403 inputs=[cardio_age, cardio_bps, cardio_chol, cardio_hr, cardio_smoke, cardio_diab, neuro_status],404 outputs=[cardio_metrics, cardio_report]405 )406 407 # --- TAB 5: AI ROBOTIC SURGEON SIMULATOR ---408 with gr.TabItem("🤖 AI Surgeon Simulator"):409 gr.Markdown("### 🤖 Autonomous Robotic Surgical Simulator & Multi-Modal Cross-Fusion Optimization Room")410 gr.Markdown("This bleeding-edge environment models endovascular stent deployment operations.", elem_classes="tab-instruction")411 412 # زر الـ VR والرسالة التحذيرية التي طلبتها413 gr.Markdown("""414 ### ⚠️ **CRITICAL ADVISORY: VR SURGICAL SIMULATION**415 This module launches a high-fidelity 3D surgical environment featuring a **pulsating heart model** and **robotic scalpel interface**. 416 **Note:** This is an external high-compute WebGL environment. Please allow sufficient loading time for 3D assets to render.417 """, elem_classes="tab-instruction")418 419 vr_link = "https://bio-lab-914537.netlify.app/"420 gr.Markdown(f'<a href="{vr_link}" target="_blank"><button class="surgeon-btn">LAUNCH VR SURGICAL SIMULATION</button></a>')421 422 with gr.Row():423 with gr.Column(scale=1):424 sync_all_btn = gr.Button("🔗 Meld Patient Bio-Identity for Surgery", elem_classes="sync-btn")425 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")426 surgeon_occlusion = gr.Slider(minimum=40, maximum=99, value=70, step=1, label="Pre-Op Lumen Occlusion Percentage (%)")427 surgeon_anesthesia = gr.Textbox(value="Standard Propofol Titration Profile", label="Calculated Anesthetic Infusion Command")428 429 surgeon_btn = gr.Button("ENGAGE AUTONOMOUS SURGICAL SIMULATION", elem_classes="surgeon-btn")430 431 with gr.Column(scale=1):432 surgeon_metrics = gr.Textbox(label="Robotic Sensor Grid & Safeguard Array", lines=10, elem_classes="output-display", interactive=False)433 surgeon_plan = gr.Textbox(label="AI Autonomous Surgical Action Protocol", lines=5, elem_classes="output-display", interactive=False)434 435 sync_all_btn.click(436 fn=meld_and_sync_all_data,437 inputs=[bio_output_report, neuro_status, cardio_metrics],438 outputs=[surgeon_artery, surgeon_occlusion, surgeon_anesthesia]439 )440 441 surgeon_btn.click(442 fn=execute_surgical_simulation,443 inputs=[surgeon_artery, surgeon_occlusion, surgeon_anesthesia, bio_output_report, neuro_status, cardio_metrics],444 outputs=[surgeon_metrics, surgeon_plan]445 )446 447 gr.HTML("<hr style='border-top: 1px solid #e2e8f0; margin-top: 25px;'>")448 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.*")449 450if __name__ == "__main__":451 demo.launch()452