Update app.py

app.py

@@ -7,7 +7,6 @@ from transformers import pipeline
 from PIL import Image
 import random
 import os
-import requests
 import hashlib
 
 # ==========================================
@@ -143,18 +142,16 @@ def analyze_genetics_and_biometrics(fingerprint, dna_seq):
             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. "
-                "Hereditary pathways indicate that localized skin cell inflammation can be actively triggered by the neural distress states "
-                "monitored in the Neuro-Pulse suite. Immediate synergy protocol recommended: Integrate specialized barrier repair formulas "
-                "(containing Ceramides and Centella Asiatica) with the system's generated 324Hz/432Hz bio-acoustic sound waves to suppress adrenal stress cues."
+                "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. Baseline gene-environment adaptation parameters are nominal."
+                "▪️ 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 to initialize the bio-identity sequence."
+        return "⚠️ System Standby: Please upload a valid fingerprint image matrix or input a genomic string sequence."
     return output_report
 
 # --- Tab 4: Cardio-Pulse AI Lab Logic ---
@@ -182,10 +179,10 @@ def calculate_cardio_risk(age, bps, cholesterol, max_hr, smoking, diabetes, neur
     fusion_notes = ""
     if "SAD" in neuro_status:
         score += 15
-        fusion_notes = "⚠️ Neuro-Cardiovascular Strain Active: Suppressed neural states are causing autonomic vasoconstriction, compounding vascular vulnerability indices.\n"
+        fusion_notes = "⚠️ Neuro-Cardiovascular Strain Active: Suppressed neural states are causing autonomic vasoconstriction.\n"
     elif "HAPPY" in neuro_status:
         score -= 5
-        fusion_notes = "🟢 Neuro-Protective Balance Active: High vagal tone and positive neurological signals are actively stabilizing endothelial resilience.\n"
+        fusion_notes = "🟢 Neuro-Protective Balance Active: Positive neurological signals are stabilizing endothelial resilience.\n"
 
     if age > 50: score += 20
     elif age > 35: score += 10
@@ -206,34 +203,29 @@ def generate_cardio_privacy_hash(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):
-    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)
-    
-    API_URL = "https://api-inference.huggingface.co/models/google/gemma-1.1-7b-it"
-    headers = {"Authorization": f"Bearer {os.getenv('HF_TOKEN', '')}"}
-    
-    prompt = f"""
-    [⚡ System: Advanced AI Cardiovascular Specialist. Neuro-Cardio Fusion Active.]
-    Secure ID: {patient_id} | Neurological Environmental State: {neuro_status}
-    Biomarkers: Age {age}, BP {bps} mmHg, Chol {cholesterol} mg/dL, MaxHR {max_hr} bpm, Smoker: {smoking}, Diabetes: {diabetes}.
-    Risk Score: {risk_pct}% ({status}).
-    
-    Provide a professional, concise clinical interpretability report in English. Detail how the intersection of these physical biomarkers and the patient's current neurological stress levels drive this risk score. Outline 3 structured preventative recommendations. Keep it sharp and high-level.
-    """
-    
-    payload = {"inputs": prompt, "parameters": {"max_new_tokens": 250, "temperature": 0.2}}
     try:
-        response = requests.post(API_URL, headers=headers, json=payload)
-        output = response.json()
-        if isinstance(output, list) and "generated_text" in output[0]:
-            report = output[0]["generated_text"].replace(prompt, "").strip()
-        else:
-            report = f"Analysis complete for Patient {patient_id}. System metrics indicate a {status} posture. Maintain optimized vascular control loops."
-    except:
-        report = f"Clinical Engine Online. Neural Stress Context Integrated. Raw Risk Factor: {risk_pct}%. Optimize biomarkers to scale down endothelial pressure."
+        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)
         
-    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
+        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)}"
 
 # --- Tab 5: AI Robotic Surgeon Simulator Logic ---
 def meld_and_sync_all_data(dna_text, neuro_text, cardio_metrics_text):
@@ -251,50 +243,39 @@ def meld_and_sync_all_data(dna_text, neuro_text, cardio_metrics_text):
     return target_artery, occlusion, anesthesia
 
 def execute_surgical_simulation(artery, occlusion, anesthesia, dna_context, neuro_context, cardio_context):
-    surgical_id = hashlib.sha256(f"Surgeon-{artery}-{occlusion}".encode()).hexdigest()[:12].upper()
-    
-    warnings = []
-    if "COL1A1" in dna_context or "AATG" in 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.")
+    try:
+        surgical_id = hashlib.sha256(f"Surgeon-{artery}-{occlusion}".encode()).hexdigest()[:12].upper()
+        
+        warnings = []
+        if "COL1A1" in dna_context or "AATG" in 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 "SAD" in neuro_context:
-        warnings.append("🧠 NEUROLOGICAL ADVISORY: Autonomic instability detected via EEG. Patient baseline exhibits elevated sympathetic drive. Maintain continuous arterial pressure damping.")
+        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."
+        warning_text = "\n".join(warnings) if warnings else "✅ Surgical telemetry nominal. No anomalous multi-modal alerts detected."
 
-    API_URL = "https://api-inference.huggingface.co/models/google/gemma-1.1-7b-it"
-    headers = {"Authorization": f"Bearer {os.getenv('HF_TOKEN', '')}"}
-    
-    prompt = f"""
-    [⚡ System: Autonomous AI Robotic Surgeon Directive. Operating Theater Matrix Active.]
-    Surgical ID: {surgical_id} | Target Site: {artery} | Pre-Op Occlusion: {occlusion}%
-    Anesthetic Control: {anesthesia}
-    Multi-Modal Intelligence Context:
-    - Genomics: {dna_context[:150]}
-    - Neuro/EEG: {neuro_context[:100]}
-    - Cardio Metrics: {cardio_context[:150]}
-    
-    Generate a highly advanced, structured 4-step Surgical Procedure Protocol in English for a Percutaneous Coronary Intervention (PCI / Stenting). Include catheter entry, balloon expansion parameters adjusted for the patient's specific genetic/neural vulnerabilities, and post-stent endothelial optimization steps. Keep it professional, strict, and dense.
-    """
-    
-    payload = {"inputs": prompt, "parameters": {"max_new_tokens": 300, "temperature": 0.15}}
-    try:
-        response = requests.post(API_URL, headers=headers, json=payload)
-        output = response.json()
-        if isinstance(output, list) and "generated_text" in output[0]:
-            surgical_plan = output[0]["generated_text"].replace(prompt, "").strip()
-        else:
-            surgical_plan = f"Robotic Surgical System calibrated successfully for ID {surgical_id}. Deployment loops verified. Ready for micro-catheter intervention."
-    except:
-        surgical_plan = f"Autonomous Surgical System Online. Navigation vectors calculated for {artery} at {occlusion}% blockage. Proceeding under automated biometric safeguards."
+        # مخرجات مخصصة لإصلاح الـ Telemetry (الصندوق العلوي في واجهتكِ القديمة)
+        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
 
-    telemetry_output = f"🏥 OPERATING THEATER TELEMETRY:\n==============================\n▶️ Session Cipher: OR-{surgical_id}\n▶️ Target Vessel: {artery}\n▶️ Calculated Tissue Density: {(occlusion*1.2):.1f} HU\n▶️ System Autonomy Level: Level 4 Autonomous Robotic Assured\n\n[CRITICAL ALERTS & SAFEGUARDS]\n{warning_text}"
-    return telemetry_output, surgical_plan
+[CRITICAL ALERTS & SAFEGUARDS]
+{warning_text}"""
+
+        # مخرجات مخصصة لإصلاح الـ Action Protocol (الصندوق السفلي في واجهتكِ القديمة)
+        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)
@@ -309,7 +290,7 @@ footer { visibility: hidden !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); }
+.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; }
 """
 
@@ -324,7 +305,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
         # --- 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. It maps diagnostic results with leading global dermatological compounds and established clinical routines.", elem_classes="tab-instruction")
+            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")
@@ -343,12 +324,11 @@ with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
         # --- 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 natively preserved in MATLAB matrix protocols (`.mat` v7/v7.3+). The system runs localized statistical evaluation to interpret immediate emotional vectors and synthetically builds an acoustic wave to regulate homeostasis.", elem_classes="tab-instruction")
+            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")
-                    gr.Markdown("<br><small><i>Computational Note: Sensitivity margins are locked at Δ ±0.005 for high-fidelity micro-fluctuation harvesting.</i></small>")
                 with gr.Column(scale=3):
                     with gr.Group():
                         with gr.Row():
@@ -356,7 +336,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
                             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 (Real-Time Audio Balance Output)", autoplay=True)
+                        neuro_audio = gr.Audio(label="2. Synthesized Waveform", autoplay=True)
             
             neuro_btn.click(
                 fn=analyze_and_respond_eeg,
@@ -367,12 +347,11 @@ with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
         # --- 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. It correlates phenotypic patterns (minutiae-based fingerprint profiling) with historical archetypes, and evaluates raw nucleobase text sequences to establish Psychodermatological feedback loops.", elem_classes="tab-instruction")
+            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 (A, T, C, G Syntax)", placeholder="Paste FASTA data fragment or raw sequence...")
-                    gr.Markdown("**📌 Select Standard Genomic Control Models to Pre-populate Sequence Box:**")
+                    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):
@@ -386,11 +365,10 @@ with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
 
         # --- TAB 4: CARDIO-PULSE AI LAB ---
         with gr.TabItem("🫀 Cardio-Pulse AI Lab"):
-            gr.Markdown("### 🫀 Frontier Edge AI for Cardiovascular Risk Forecasting & Neuro-Cardio Fusion Analytics")
-            gr.Markdown("This specialized sub-suite performs deep mathematical and generative evaluation of endothelial and vascular risk factors. It functions in Dual Mode: standard biomarker parsing or live fusion sync with the Neuro-Pulse suite to cross-analyze cortisol-driven strain.", elem_classes="tab-instruction")
+            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):
-                    gr.Markdown("### 📊 Patient Biomarkers & Interactivity Controls")
                     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")
@@ -407,9 +385,8 @@ with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
                     cardio_btn = gr.Button("EXECUTE INTEGRATED CARDIO RISK EVALUATION", elem_classes="action-btn")
                 
                 with gr.Column(scale=1):
-                    gr.Markdown("### ⚡ AI Analytics & Privacy Shield Output")
                     cardio_metrics = gr.Textbox(label="Security Metrics & Quantitative Assessment", lines=4, elem_classes="output-display", interactive=False)
-                    cardio_report = gr.Markdown("### 📋 AI Clinical Interpretability Report\n*Your multidimensional, anonymized neuro-cardio report will appear here.*")
+                    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,
@@ -432,12 +409,10 @@ with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
         # --- 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 (Angioplasty). It executes Triple-Fusion: pulling genetic vulnerability bounds, neuro-cortical stress telemetry, and cardio profiles to build an absolute protective procedure template.", elem_classes="tab-instruction")
+            gr.Markdown("This bleeding-edge environment models endovascular stent deployment operations.", elem_classes="tab-instruction")
             with gr.Row():
                 with gr.Column(scale=1):
-                    gr.Markdown("### 🛠️ Surgical Telemetry & Cross-Tab Interactivity Engine")
                     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")
@@ -445,9 +420,9 @@ with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
                     surgeon_btn = gr.Button("ENGAGE AUTONOMOUS SURGICAL SIMULATION", elem_classes="surgeon-btn")
                 
                 with gr.Column(scale=1):
-                    gr.Markdown("### 🖥️ Robotic Operating Theater Telemetry")
-                    surgeon_metrics = gr.Textbox(label="Robotic Sensor Grid & Safeguard Array", lines=6, elem_classes="output-display", interactive=False)
-                    surgeon_plan = gr.Markdown("### 📋 AI Autonomous Surgical Action Protocol\n*The generative robotic operative roadmap will be compiled here.*")
+                    # تمت موازنة صناديق الـ Textboxes لتعود كروت حقيقية ومرتبة كما في لقطات الشاشة السابقة تماماً
+                    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,
@@ -461,9 +436,8 @@ with gr.Blocks(theme=gr.themes.Soft(), css=master_css) as demo:
                 outputs=[surgeon_metrics, surgeon_plan]
             )
             
-    # Universal Regulatory Compliance Footer
     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 including computer vision images, binary .mat neuro-signals, biometric dactyloscopy matrices, and genetic sequence strings are parsed in-memory instantly and remain contained entirely within the current sandboxed user session. No remote database storage occurs.*")
+    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()