diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..fe176cc39 --- /dev/null +++ b/.gitignore @@ -0,0 +1,58 @@ +# OS files +.DS_Store +Thumbs.db + +# Environments +venv/ +env/ +.env +.env.local +.venv/ +ENV/ + +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Node +node_modules/ +.next/ +out/ +build/ +dist/ + +# Scratch & Logs +dashboard/scratch/ +scratch/ +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# Models (optional, but often large) +# models/*.keras (keeping if user wants them in git) +# models/*.keras.bak +# models/*.pkl +*.bak + +# IDEs +.vscode/ +.idea/ diff --git a/README.md b/README.md index c5c886b3e..75140331b 100644 --- a/README.md +++ b/README.md @@ -1,86 +1,130 @@ -# HackToFuture 4.0 ā€” Template +
+ + + +

šŸŒ NeuroMesh

+

AI-Powered Disaster Response & Telemetry Validation Network

+ + [![HackToFuture 4](https://img.shields.io/badge/HackToFuture-4-blueviolet?style=for-the-badge)](#) + [![Status](https://img.shields.io/badge/Status-Active-success?style=for-the-badge)](#) +
-Welcome to your official HackToFuture 4 repository. +
-This repository template will be used for development, tracking progress, and final submission of your project. Ensure that all work is committed here within the allowed hackathon duration. +## šŸšØ Problem Statement / Idea ---- +**What is the problem?** +During critical disasters (earthquakes, structural collapses, gas leaks), first responders are blinded by chaotic data, broken communication lines, and false alarms. Misallocating rescue teams to a "false alarm" while a real crisis unfolds costs lives. -### Instructions for the teams: +**Why is it important?** +The "Golden Day" (first 24 hours) is crucial for survival. Emergency response systems need filtered, validated, and highly accurate situation reports (SitReps) to deploy Urban Search and Rescue (USAR) teams safely and dynamically. -- Fork the Repository and name the forked repo in this convention: hacktofuture4-team_id (for eg: hacktofuture4-A01) +**Who are the target users?** +- National Disaster Response Force (NDRF) / FEMA +- Emergency Command Centers +- First Responders & Hazmat (CBRN) Teams --- -## Rules - -- Work must be done ONLY in the forked repository -- Only Four Contributors are allowed. -- After 36 hours, Please make PR to the Main Repository. A Form will be sent to fill the required information. -- Do not copy code from other teams -- All commits must be from individual GitHub accounts -- Please provide meaningful commits for tracking. -- Do not share your repository with other teams -- Final submission must be pushed before the deadline -- Any violation may lead to disqualification - ---- +## šŸ’” Proposed Solution -# The Final README Template +**What are we building?** +**NeuroMesh** is a comprehensive hardware-software simulation platform representing a deployed mesh network of environmental sensors. It feeds raw telemetry (seismic vibrations, gas PPM, PIR survivor movement) into a multi-stage AI pipeline. -## Problem Statement / Idea +**How does it solve the problem?** +Raw data is processed by specialized ML models (CNNs for seismic, Random Forests for gas). Crucially, an **Isolation Forest** validator checks the aggregated outputs to flag hardware glitches or false alarms. If authenticated, an LLM (Mistral) generates an actionable, military-grade Situation Report (SitRep). -Clearly describe the problem you are solving. - -- What is the problem? -- Why is it important? -- Who are the target users? +**What makes your solution unique?** +- **False Alarm Filtration:** We don't just alert; we authenticate the crisis using anomaly detection, saving wasted rescue trips. +- **Multi-Modal AI:** Specialized models handle specific sensor outputs before fusing data into LangGraph. +- **Interactive UI:** A Next.js Turbopack dashboard visualizes node status with real-time Framer Motion animations. --- -## Proposed Solution - -Explain your approach: +## šŸš€ Features -- What are you building? -- How does it solve the problem? -- What makes your solution unique? +- šŸŸ¢ **Live Telemetry Dashboard:** An interactive Web GIS UI plotting mesh nodes and live status. +- šŸ§  **Multi-Model Inference:** + - Seismic Activity Classification (Keras/TensorFlow) + - Hazardous Gas Prediction (Scikit-Learn Random Forest) + - Survivor Count Estimation (Scikit-Learn) +- šŸ›”ļø **Crisis Validator (Isolation Forest):** Identifies erratic sensor behavior or false positives. +- āš” **Automated LLM SitReps:** LangGraph orchestration to output clear "Recommended Actions" and "Threat Levels". +- šŸ“” **Simulated LoRaWAN Network:** Fallback communication visualization for unconnected zones. --- -## Features +## šŸ› ļø Tech Stack -List the core features of your project: +
+Click to expand technical details +
-- Feature 1 -- Feature 2 -- Feature 3 +- **Frontend:** Next.js 16 (App Router, Turbopack), React, TailwindCSS, Framer Motion, Lucide Icons +- **Backend / AI Pipeline:** Python 3.13, LangGraph +- **Machine Learning:** Scikit-Learn, TensorFlow/Keras, Pandas, NumPy +- **Generative AI:** Mistral / LLM (SitRep Generation) +- **Data:** CSV-based robust simulated environments (`gas_data.csv`, `seismic_data.csv`, `survivor_data.csv`) ---- - -## Tech Stack - -Mention all technologies used: - -- Frontend: -- Backend: -- Database: -- APIs / Services: -- Tools / Libraries: +
--- -## Project Setup Instructions +## āš™ļø Project Setup Instructions Provide clear steps to run your project: ```bash -# Clone the repository -git clone +# 1. Clone the repository +git clone https://github.com/haashid/hacktofuture4-I03.git +cd hacktofuture4-I03 + +# --------------------------------------------- +# FRONTEND (Next.js Dashboard) +# --------------------------------------------- +cd dashboard +npm install + +# Run the Next.js development server +npm run dev + +# --------------------------------------------- +# AI PIPELINE (Python Environment) +# --------------------------------------------- +# Open a new terminal and navigate to the project root +cd .. + +# Create and activate a virtual environment +python3 -m venv venv +source venv/bin/activate # On Windows: venv\Scripts\activate + +# Install AI dependencies +pip install scikit-learn tensorflow pandas numpy langgraph + +# Run the AI Pipeline +python pipeline/langgraph_pipeline.py + +# --------------------------------------------- +# FIREBASE LIVE BRIDGE (ESP32 -> Firebase -> Models -> Dashboard) +# --------------------------------------------- +# Use your RTDB URL if different +export FIREBASE_DATABASE_URL="https://hack2future-79802-default-rtdb.firebaseio.com" + +# Run dashboard in another terminal +cd dashboard && npm run dev + +# Stream live node data from /node1 into the ML pipeline +cd .. +python -m pipeline.firebase_live_bridge --firebase-path node1 --dashboard-url http://localhost:3000 +``` -# Install dependencies -... +### Live Thresholds (matched to ESP32 sketch) -# Run the project -... -``` +- Acceleration trigger: `abs(acceleration - 1.0) > 0.25` +- Gas trigger: `gas > 2000` +- Earthquake persistence window: `10 seconds` + +
+
+ Built with ā¤ļø for HackToFuture 4 +
\ No newline at end of file diff --git a/agents/logistics_agent.py b/agents/logistics_agent.py new file mode 100644 index 000000000..1a9faf306 --- /dev/null +++ b/agents/logistics_agent.py @@ -0,0 +1,188 @@ +import numpy as np +import requests +from pipeline.state import NeuroMeshState +from datetime import datetime + +def get_road_network_simple(lat: float, lng: float, radius: int = 1000) -> dict: + """ + Get roads near collapse zone using Overpass API (free OSM). + Returns road segments as simple dict. + No osmnx needed ā€” raw Overpass query. + """ + overpass_url = "http://overpass-api.de/api/interpreter" + query = f""" + [out:json][timeout:10]; + ( + way["highway"~"primary|secondary|tertiary|residential|service"] + (around:{radius},{lat},{lng}); + ); + out body; + >; + out skel qt; + """ + + try: + response = requests.post(overpass_url, data=query, timeout=12) + if response.status_code == 200: + return response.json() + except: + pass + + return None + +def calculate_debris_probability(road_lat: float, road_lng: float, + collapse_lat: float, collapse_lng: float, + magnitude: float) -> float: + """ + ML-lite model: probability a road is blocked by debris. + Based on distance from collapse + magnitude. + Simple physics-based model, no training needed. + """ + # Haversine distance in meters + R = 6371000 + dlat = np.radians(road_lat - collapse_lat) + dlng = np.radians(road_lng - collapse_lng) + a = (np.sin(dlat/2)**2 + + np.cos(np.radians(collapse_lat)) * np.cos(np.radians(road_lat)) * + np.sin(dlng/2)**2) + distance_m = 2 * R * np.arcsin(np.sqrt(a)) + + # Debris throw radius scales with magnitude + # Magnitude 4 ā†’ ~50m debris zone + # Magnitude 6 ā†’ ~200m debris zone + debris_radius = magnitude * 35 + + if distance_m < debris_radius * 0.3: + return 0.95 # almost certainly blocked + elif distance_m < debris_radius * 0.6: + return 0.70 + elif distance_m < debris_radius: + return 0.40 + elif distance_m < debris_radius * 2: + return 0.15 + else: + return 0.05 # probably clear + +def find_best_routes(collapse_lat: float, collapse_lng: float, + magnitude: float) -> dict: + """ + Find primary and alternate routes to collapse site. + Marks debris-risk zones around collapse. + """ + # Debris exclusion zone radius (meters) + exclusion_radius = int(magnitude * 35) + + # Primary entry point: closest safe approach + # Offset from collapse center by exclusion radius in cardinal direction + offset_deg = exclusion_radius / 111000 # convert meters to degrees approx + + entry_primary = { + "lat": collapse_lat - offset_deg, + "lng": collapse_lng, + "label": "Primary entry point", + "note": f"Approach from south, {exclusion_radius}m from collapse center" + } + + entry_alternate = { + "lat": collapse_lat, + "lng": collapse_lng + offset_deg * 1.2, + "label": "Alternate entry point", + "note": f"Approach from east, {int(exclusion_radius * 1.2)}m from collapse center" + } + + assembly_point = { + "lat": collapse_lat - offset_deg * 3, + "lng": collapse_lng - offset_deg * 2, + "label": "Assembly point", + "note": "Safe staging area for teams and equipment" + } + + # Debris risk zones (circles around collapse) + debris_zones = [ + { + "type": "Feature", + "properties": { + "risk": "extreme", + "label": f"Extreme debris zone ({int(exclusion_radius * 0.3)}m radius)", + "entry_forbidden": True + }, + "geometry": { + "type": "Point", + "coordinates": [collapse_lng, collapse_lat], + "radius": exclusion_radius * 0.3 + } + }, + { + "type": "Feature", + "properties": { + "risk": "high", + "label": f"High debris zone ({exclusion_radius}m radius)", + "entry_forbidden": False + }, + "geometry": { + "type": "Point", + "coordinates": [collapse_lng, collapse_lat], + "radius": exclusion_radius + } + } + ] + + return { + "entry_primary": entry_primary, + "entry_alternate": entry_alternate, + "assembly_point": assembly_point, + "debris_zones": debris_zones, + "exclusion_radius_m": exclusion_radius, + "eta_estimate_minutes": 8 + (exclusion_radius / 1000) * 2 # rough ETA + } + +def logistics_agent(state: NeuroMeshState) -> NeuroMeshState: + """ + Agent 3: The Logistics Strategist + ZERO LLM CALLS. + Calculates routes, debris zones, entry points using OSM + physics model. + """ + log = state.get('processing_log', []) + + primary_node = max(state['raw_nodes'], + key=lambda n: n['seismic_magnitude']) + + lat = primary_node['lat'] + lng = primary_node['lng'] + mag = primary_node['seismic_magnitude'] + + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] LOGISTICS: Computing routes for collapse at {lat:.4f}, {lng:.4f}") + + routes = find_best_routes(lat, lng, mag) + + logistics_output = { + "primary_route": { + "type": "route", + "label": "Primary approach route", + "entry_point": routes['entry_primary'], + "via": "Main road, approach from south" + }, + "alternate_route": { + "type": "route", + "label": "Alternate approach route", + "entry_point": routes['entry_alternate'], + "via": "Service road, approach from east" + }, + "blocked_roads": [ + f"All roads within {routes['exclusion_radius_m']}m of collapse center", + "Check debris probability before entry" + ], + "estimated_eta_minutes": round(routes['eta_estimate_minutes'], 1), + "entry_point": routes['entry_primary'], + "assembly_point": routes['assembly_point'], + "debris_risk_zones": routes['debris_zones'], + "exclusion_radius_m": routes['exclusion_radius_m'] + } + + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] LOGISTICS: ETA {logistics_output['estimated_eta_minutes']} mins ā€” exclusion zone {routes['exclusion_radius_m']}m") + + return { + **state, + "logistics": logistics_output, + "processing_log": log + } diff --git a/agents/reporter_agent.py b/agents/reporter_agent.py new file mode 100644 index 000000000..dd6b773b7 --- /dev/null +++ b/agents/reporter_agent.py @@ -0,0 +1,154 @@ +import os +from pipeline.state import NeuroMeshState +from datetime import datetime + +try: + from mistralai import Mistral +except Exception: + Mistral = None + +MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY", "") +client = Mistral(api_key=MISTRAL_API_KEY) if (Mistral and MISTRAL_API_KEY) else None + +def build_compact_prompt(state: NeuroMeshState) -> str: + """ + Build the most token-efficient prompt possible. + Strip everything the LLM doesn't need. + The LLM only gets labels and scores ā€” never raw numbers. + """ + v = state['verifier'] + t = state['triage'] + l = state['logistics'] + node = max(state['raw_nodes'], key=lambda n: n['seismic_magnitude']) + + # This prompt is intentionally compact. + # Every field is a verified ML output ā€” not raw sensor data. + prompt = f"""NDRF Crisis AI. Generate SITREP from verified intel. + +EVENT: Structural collapse, Node {node['node_id']} +GPS: {node['lat']:.4f}, {node['lng']:.4f} +VERIFIED: {v['confidence']:.0%} confidence, {v['correlation_type']} trigger + +LIFE INTEL: +- Survivability: {t['survivability_score']}/100 +- Persons: ~{t['estimated_persons']} trapped +- Life signs: {t['life_sign_pattern']} +- Urgency: {t['urgency'].upper()} +- Golden hour remaining: {t['golden_hour_remaining']:.0f} min + +HAZARD: +- Gas: {t['gas_threat']} ({t['gas_note']}) +- Entry protocol: {t['entry_protocol'].upper()} + +LOGISTICS: +- ETA: {l['estimated_eta_minutes']} min +- Exclusion zone: {l['exclusion_radius_m']}m radius +- Team size needed: {t['recommended_team_size']} + +Generate SITREP (under 120 words): +1. CODE [GREEN/YELLOW/ORANGE/RED/BLACK] +2. SITUATION: (1 sentence) +3. SURVIVORS: (1 sentence) +4. HAZARDS: (1 sentence) +5. ACTION: (2-3 bullet points, numbered) +6. TIME: (deadline + consequence)""" + + return prompt + +def determine_threat_level(triage, verifier) -> str: + """Rule-based threat level. NO LLM.""" + score = triage['survivability_score'] + gas = triage['gas_threat'] + conf = verifier['confidence'] + + if conf < 0.5: + return "GREEN" + elif score >= 80 and gas == "lethal": + return "BLACK" # mass casualty + toxic + elif score >= 70 or gas == "lethal": + return "RED" + elif score >= 50 or gas == "warning": + return "ORANGE" + elif score >= 30: + return "YELLOW" + else: + return "GREEN" + + +def build_fallback_sitrep(state: NeuroMeshState, threat_level: str) -> str: + """Generate a deterministic SITREP when LLM is unavailable.""" + node = max(state['raw_nodes'], key=lambda n: n['seismic_magnitude']) + triage = state['triage'] + logistics = state['logistics'] + return ( + f"1. CODE {threat_level}\n" + f"2. SITUATION: Structural collapse detected at node {node['node_id']} near " + f"{node['lat']:.4f}, {node['lng']:.4f}.\n" + f"3. SURVIVORS: Estimated {triage['estimated_persons']} survivors with " + f"{triage['life_sign_pattern']} life signs.\n" + f"4. HAZARDS: Gas threat is {triage['gas_threat']} and entry protocol is " + f"{triage['entry_protocol']}.\n" + f"5. ACTION: 1) Dispatch {triage['recommended_team_size']} responders. " + f"2) Stage at assembly point and use primary route. " + f"3) Respect {logistics['exclusion_radius_m']}m exclusion zone.\n" + f"6. TIME: Initiate rescue within {triage['time_sensitivity_minutes']} minutes " + f"or survivability will decline." + ) + +def reporter_agent(state: NeuroMeshState) -> NeuroMeshState: + """ + Agent 4: The Reporter + ONE LLM CALL with compact verified input. + Generates human-readable SITREP for NDRF commanders. + """ + log = state.get('processing_log', []) + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] REPORTER: Generating SITREP...") + + # Threat level from rules (NOT LLM) + threat_level = determine_threat_level(state['triage'], state['verifier']) + + # Severity score from rules (NOT LLM) + severity_score = state['triage']['survivability_score'] + + # Only one LLM call ā€” with compact, token-efficient prompt + prompt = build_compact_prompt(state) + + if client is None: + sitrep_text = build_fallback_sitrep(state, threat_level) + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] REPORTER: No MISTRAL_API_KEY found, used fallback SITREP") + else: + try: + response = client.chat.complete( + model="mistral-small-latest", + messages=[{"role": "user", "content": prompt}], + max_tokens=250 # Hard cap on tokens + ) + sitrep_text = response.choices[0].message.content + except Exception as err: + sitrep_text = build_fallback_sitrep(state, threat_level) + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] REPORTER: LLM call failed ({type(err).__name__}), used fallback SITREP") + + # Headline from rules (NOT LLM) + node = max(state['raw_nodes'], key=lambda n: n['seismic_magnitude']) + headline = (f"CODE {threat_level}: Structural collapse at Node {node['node_id']} ā€” " + f"{state['triage']['estimated_persons']} survivors ā€” " + f"{state['triage']['urgency'].upper()} response") + + sitrep_output = { + "threat_level": threat_level, + "severity_score": severity_score, + "headline": headline, + "full_sitrep": sitrep_text, + "recommended_team_size": state['triage']['recommended_team_size'], + "equipment_checklist": state['triage']['equipment_checklist'], + "time_sensitivity_minutes": state['triage']['time_sensitivity_minutes'] + } + + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] REPORTER: SITREP generated ā€” {threat_level} ā€” {severity_score}/100") + + return { + **state, + "sitrep": sitrep_output, + "pipeline_status": "complete", + "processing_log": log + } diff --git a/agents/triage_agent.py b/agents/triage_agent.py new file mode 100644 index 000000000..35737b80b --- /dev/null +++ b/agents/triage_agent.py @@ -0,0 +1,240 @@ +import numpy as np +import joblib +from pipeline.state import NeuroMeshState +from datetime import datetime + +try: + survivor_model = joblib.load('models/survivor_model.pkl') + survivor_scaler = joblib.load('models/survivor_scaler.pkl') + gas_model = joblib.load('models/gas_model.pkl') +except: + survivor_model = None + gas_model = None + +GAS_LABELS = ['safe', 'LPG_leak', 'smoke_fire'] + +def classify_life_signs(pir_count: int, gas_ppm: float, + time_since_mins: float, magnitude: float) -> dict: + """ + Pattern recognition for life signs. + Maps sensor combos to biological states. + NO LLM ā€” pure ML + lookup table. + """ + + # Survivor probability from ML model + if survivor_model: + features = np.array([[pir_count, time_since_mins, magnitude]]) + features_scaled = survivor_scaler.transform(features) + prob = float(survivor_model.predict_proba(features_scaled)[0][1]) + else: + # Fallback heuristic + prob = min(0.95, (pir_count * 0.08) + max(0, 0.8 - time_since_mins * 0.01)) + + # Pattern matching logic + # Each combo maps to a specific biological state + if pir_count >= 8 and gas_ppm < 200: + pattern = "active" # survivors moving, air breathable + estimated = f"{max(2, pir_count // 3)}-{max(3, pir_count // 2)}" + elif pir_count >= 4 and gas_ppm < 400: + pattern = "active" + estimated = "2-4" + elif pir_count >= 2 and gas_ppm >= 400: + pattern = "weakening" # survivors present but toxic air + estimated = "1-3" + elif pir_count == 1: + pattern = "critical" # faint movement, single survivor + estimated = "1" + elif pir_count == 0 and magnitude > 4.0: + pattern = "none" # either no survivors or deeply buried + estimated = "0 (possible deeply buried)" + else: + pattern = "weakening" + estimated = "1-2" + + # Estimate count from probability + if prob > 0.85: + score = int(70 + prob * 30) + elif prob > 0.6: + score = int(40 + prob * 40) + else: + score = int(prob * 40) + + return { + "survivor_probability": round(prob, 2), + "survivability_score": score, + "life_sign_pattern": pattern, + "estimated_persons": estimated + } + +def classify_gas_threat(gas_ppm: float, seismic_flag: int) -> dict: + """ + Gas threat classification using ML model. + Maps to entry protocol. + """ + if gas_model: + # Create a 30-reading array (simulate steady state at current PPM) + ppm_readings = np.linspace(max(50, gas_ppm * 0.3), gas_ppm, 30) + features = list(ppm_readings) + [seismic_flag] + features = np.array(features).reshape(1, -1) + probs = gas_model.predict_proba(features)[0] + gas_class = GAS_LABELS[np.argmax(probs)] + else: + if gas_ppm > 500: + gas_class = 'LPG_leak' + elif gas_ppm > 300: + gas_class = 'smoke_fire' + else: + gas_class = 'safe' + + # Map gas class + PPM to threat level and entry protocol + if gas_class == 'LPG_leak' or gas_ppm > 600: + return { + "gas_threat": "lethal", + "gas_type": gas_class, + "entry_protocol": "hazmat", + "note": "NO ENTRY without full SCBA. Explosion risk. Isolate utilities." + } + elif gas_class == 'smoke_fire' or gas_ppm > 250: + return { + "gas_threat": "warning", + "gas_type": gas_class, + "entry_protocol": "breathing_apparatus", + "note": "Breathing apparatus mandatory. Fire suppression on standby." + } + else: + return { + "gas_threat": "clear", + "gas_type": "safe", + "entry_protocol": "standard", + "note": "Air quality acceptable. Standard entry procedure." + } + +def determine_urgency(survivability_score: float, + gas_threat: str, + time_since_mins: float) -> dict: + """ + Urgency scoring. Maps to time window and team size. + Golden Hour logic built in. + """ + # Base urgency from survivability + if survivability_score >= 80 and gas_threat == "lethal": + urgency = "extreme" + time_window = 15 # minutes before situation degrades + team_size = 12 + elif survivability_score >= 70: + urgency = "immediate" + time_window = 30 + team_size = 8 + elif survivability_score >= 50: + urgency = "high" + time_window = 60 + team_size = 6 + elif survivability_score >= 30: + urgency = "moderate" + time_window = 120 + team_size = 4 + else: + urgency = "low" + time_window = 240 + team_size = 2 + + # Degrade urgency if too much time has passed (Golden Hour) + golden_hour_factor = max(0, 1 - (time_since_mins / 60)) + adjusted_score = survivability_score * golden_hour_factor + + return { + "urgency": urgency, + "time_sensitivity_minutes": time_window, + "recommended_team_size": team_size, + "golden_hour_remaining": max(0, 60 - time_since_mins), + "adjusted_survivability": round(adjusted_score, 1) + } + +def build_equipment_checklist(entry_protocol: str, + life_pattern: str, + estimated_persons: str) -> list: + """Rule-based equipment checklist. No LLM needed.""" + checklist = ["Radio comms (LoRa backup)", "GPS tracker", "First aid kit"] + + if entry_protocol == "hazmat": + checklist += ["Full SCBA breathing apparatus", "Gas detector (multi-sensor)", + "Blast shields", "Hazmat suits", "Fire suppression foam"] + elif entry_protocol == "breathing_apparatus": + checklist += ["Breathing apparatus (each member)", "Smoke hood backup", + "Fire extinguisher"] + + if life_pattern in ["active", "weakening"]: + checklist += ["Rescue stretchers", "Cervical collars", + "Thermal blankets", "IV fluid kits"] + + if "5+" in estimated_persons or "3-5" in estimated_persons: + checklist += ["Mass casualty triage tags", "Additional stretchers x4", + "Oxygen cylinders x6"] + + checklist += ["Hydraulic spreader (Jaws of Life)", "Search torch", + "Victim voice amplifier", "Thermal imaging camera"] + + return checklist + +def triage_agent(state: NeuroMeshState) -> NeuroMeshState: + """ + Agent 2: The Triage Medic + ZERO LLM CALLS. Pure ML + pattern matching. + Outputs survivability score, life signs, gas threat, protocol. + """ + log = state.get('processing_log', []) + + # Use worst-case node (highest magnitude + most activity) + nodes = state['raw_nodes'] + primary = max(nodes, key=lambda n: n['seismic_magnitude']) + + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] TRIAGE: Analysing primary node {primary['node_id']}") + + time_since = 5.0 # assume 5 minutes since event for demo; in prod use timestamp diff + + # Run ML sub-models + life_data = classify_life_signs( + pir_count=primary['pir_count'], + gas_ppm=primary['gas_ppm'], + time_since_mins=time_since, + magnitude=primary['seismic_magnitude'] + ) + + gas_data = classify_gas_threat( + gas_ppm=primary['gas_ppm'], + seismic_flag=1 + ) + + urgency_data = determine_urgency( + survivability_score=life_data['survivability_score'], + gas_threat=gas_data['gas_threat'], + time_since_mins=time_since + ) + + equipment = build_equipment_checklist( + entry_protocol=gas_data['entry_protocol'], + life_pattern=life_data['life_sign_pattern'], + estimated_persons=life_data['estimated_persons'] + ) + + triage_output = { + "survivability_score": life_data['survivability_score'], + "estimated_persons": life_data['estimated_persons'], + "life_sign_pattern": life_data['life_sign_pattern'], + "gas_threat": gas_data['gas_threat'], + "entry_protocol": gas_data['entry_protocol'], + "urgency": urgency_data['urgency'], + "time_sensitivity_minutes": urgency_data['time_sensitivity_minutes'], + "recommended_team_size": urgency_data['recommended_team_size'], + "golden_hour_remaining": urgency_data['golden_hour_remaining'], + "equipment_checklist": equipment, + "gas_note": gas_data['note'] + } + + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] TRIAGE: Survivability {life_data['survivability_score']}/100 ā€” {urgency_data['urgency'].upper()} ā€” {gas_data['gas_threat']} gas") + + return { + **state, + "triage": triage_output, + "processing_log": log + } \ No newline at end of file diff --git a/agents/verifier_agent.py b/agents/verifier_agent.py new file mode 100644 index 000000000..f07fd0524 --- /dev/null +++ b/agents/verifier_agent.py @@ -0,0 +1,183 @@ +import numpy as np +import joblib +from pipeline.state import NeuroMeshState +from datetime import datetime + +# Load your pre-trained Isolation Forest +try: + validator_model = joblib.load('models/validator_model.pkl') +except: + validator_model = None + +def run_isolation_forest(node: dict) -> dict: + """Run anomaly detection on a single node's readings""" + if validator_model is None: + # Fallback: simple threshold logic if model not loaded + is_real = (node['seismic_magnitude'] > 2.0 and + node['event_duration_ms'] > 250) + return {"is_genuine": is_real, "score": -0.5 if is_real else 0.2} + + features = np.array([[ + node['seismic_magnitude'], + node['gas_ppm'], + node['pir_count'], + node['event_duration_ms'] + ]]) + + score = float(validator_model.score_samples(features)[0]) + prediction = validator_model.predict(features)[0] + + model_genuine = prediction == 1 + + # Live IoT fallback: allow deterministic escalation for strong single-node evidence. + # This prevents real strong events from being rejected only because of model drift. + rule_escalation = ( + (node['seismic_magnitude'] >= 4.5 and node['event_duration_ms'] >= 250) or + (node['gas_ppm'] >= 2000 and node['event_duration_ms'] >= 250) or + (node['pir_count'] >= 2 and node['seismic_magnitude'] >= 3.5) + ) + + return { + "is_genuine": bool(model_genuine or rule_escalation), + "score": score + } + +def compute_spatial_correlation(nodes: list) -> dict: + """ + Check if multiple nodes are triggering. + Real disasters affect multiple nodes in a cluster. + A single node triggering alone is suspicious. + """ + triggering_nodes = [ + n for n in nodes + if n['seismic_magnitude'] > 2.0 + ] + + count = len(triggering_nodes) + total = len(nodes) + + if count == 0: + return { + "correlation_type": "none", + "confidence": 0.0, + "triggered_count": 0 + } + elif count == 1: + return { + "correlation_type": "single_node", + "confidence": 0.45, # suspicious ā€” could be false alarm + "triggered_count": 1 + } + elif count == 2: + return { + "correlation_type": "pair", + "confidence": 0.78, + "triggered_count": 2 + } + else: + return { + "correlation_type": "cluster", + "confidence": 0.97, # strong ā€” real event + "triggered_count": count + } + +def magnitude_cross_check(nodes: list) -> bool: + """ + Real earthquakes/collapses show correlated magnitude + across nearby nodes. Random false alarms don't. + """ + magnitudes = [n['seismic_magnitude'] for n in nodes if n['seismic_magnitude'] > 0.5] + if len(magnitudes) < 2: + return True # can't cross-check with one node + + # Standard deviation of magnitudes across nodes + # Real events: low std (all nodes feel similar shaking) + # False alarms: high std (one node spikes, others don't) + std = np.std(magnitudes) + return std < 2.5 # correlated if std < 2.5G + +def verifier_agent(state: NeuroMeshState) -> NeuroMeshState: + """ + Agent 1: The Verifier + NO LLM CALLS. Pure ML + statistical logic. + Determines if the event is genuine before anything else runs. + """ + log = state.get('processing_log', []) + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] VERIFIER: Starting analysis on {len(state['raw_nodes'])} nodes") + + nodes = state['raw_nodes'] + + # Step 1: Run Isolation Forest on each node + node_results = [] + for node in nodes: + result = run_isolation_forest(node) + node_results.append({ + "node_id": node['node_id'], + "is_genuine": result['is_genuine'], + "score": result['score'], + "magnitude": node['seismic_magnitude'] + }) + + genuine_nodes = [n for n in node_results if n['is_genuine']] + + # Step 2: Spatial correlation + spatial = compute_spatial_correlation(nodes) + + # Step 3: Magnitude cross-check + correlated = magnitude_cross_check(nodes) + + # Step 4: Final decision logic (NO LLM ā€” pure rules) + triggered_node_ids = [n['node_id'] for n in genuine_nodes] + + # Decision matrix + if len(genuine_nodes) == 0: + is_genuine = False + confidence = 0.05 + rejection_reason = "All nodes failed Isolation Forest check ā€” false alarm" + elif len(genuine_nodes) >= 2 and correlated: + is_genuine = True + confidence = min(0.99, spatial['confidence'] + 0.1) + rejection_reason = None + elif len(genuine_nodes) == 1 and ( + nodes[0]['seismic_magnitude'] > 4.5 or + nodes[0]['gas_ppm'] >= 2000 or + nodes[0]['pir_count'] >= 2 + ): + # Single node but very high magnitude ā€” escalate anyway + is_genuine = True + confidence = 0.72 + rejection_reason = None + elif len(genuine_nodes) == 1: + is_genuine = False + confidence = 0.35 + rejection_reason = "Single node trigger with moderate magnitude ā€” possible false alarm. Monitor." + else: + is_genuine = True + confidence = spatial['confidence'] + rejection_reason = None + + verifier_output = { + "is_genuine": is_genuine, + "confidence": round(confidence, 2), + "triggered_nodes": triggered_node_ids, + "correlation_type": spatial['correlation_type'], + "rejection_reason": rejection_reason + } + + log.append(f"[{datetime.now().strftime('%H:%M:%S')}] VERIFIER: {'GENUINE EVENT' if is_genuine else 'FALSE ALARM'} ā€” confidence {confidence:.0%} ā€” {spatial['correlation_type']}") + + if not is_genuine: + return { + **state, + "verifier": verifier_output, + "pipeline_status": "aborted", + "abort_reason": rejection_reason, + "processing_log": log + } + + return { + **state, + "verifier": verifier_output, + "pipeline_status": "running", + "processing_log": log + } \ No newline at end of file diff --git a/dashboard/.gitignore b/dashboard/.gitignore new file mode 100644 index 000000000..5ef6a5207 --- /dev/null +++ b/dashboard/.gitignore @@ -0,0 +1,41 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/dashboard/AGENTS.md b/dashboard/AGENTS.md new file mode 100644 index 000000000..8bd0e3908 --- /dev/null +++ b/dashboard/AGENTS.md @@ -0,0 +1,5 @@ + +# This is NOT the Next.js you know + +This version has breaking changes ā€” APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in `node_modules/next/dist/docs/` before writing any code. Heed deprecation notices. + diff --git a/dashboard/CLAUDE.md b/dashboard/CLAUDE.md new file mode 100644 index 000000000..43c994c2d --- /dev/null +++ b/dashboard/CLAUDE.md @@ -0,0 +1 @@ +@AGENTS.md diff --git a/dashboard/README.md b/dashboard/README.md new file mode 100644 index 000000000..e215bc4cc --- /dev/null +++ b/dashboard/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/dashboard/REALTIME_IOT_SETUP.md b/dashboard/REALTIME_IOT_SETUP.md new file mode 100644 index 000000000..7754db90c --- /dev/null +++ b/dashboard/REALTIME_IOT_SETUP.md @@ -0,0 +1,102 @@ +# Realtime IoT Feed Setup (ESP32 -> NDRF Dashboard) + +## What was added + +- `GET /api/telemetry`: returns current live alert snapshot for dashboard polling. +- `POST /api/telemetry`: accepts sensor frames from ESP32 and updates alert state. +- Dashboard now auto-polls this API every 1 second and renders live alerts. + +## API Payload + +Send JSON with these required fields: + +```json +{ + "node_id": "NM-01", + "lat": 12.9141, + "lng": 74.8560, + "acceleration_g": 1.23, + "gas_raw": 2890, + "motion": true, + "temp_c": 30.4, + "humidity": 62.0, + "address": "Hampankatta Zone A" +} +``` + +## Alert Logic + +- Earthquake intensity is derived from `acceleration_g`. +- Gas risk is derived from `gas_raw` (0-4095) mapped to percentage. +- Threat level: + - `RED`: quake active + high gas + motion + - `ORANGE`: quake active + (high gas or motion) + - `YELLOW`: quake only or gas only + - `GREEN`: otherwise +- Dashboard feed shows only non-GREEN alerts. + +## ESP32 Code Patch + +Add these includes and constants: + +```cpp +#include + +const char* ingestUrl = "http://:3000/api/telemetry"; +const char* nodeId = "NM-01"; +const float nodeLat = 12.9141; +const float nodeLng = 74.8560; +``` + +Add this helper function: + +```cpp +void postTelemetry(float acc, int gasValue, int motion, float temp, float hum) { + if (WiFi.status() != WL_CONNECTED) return; + + HTTPClient http; + http.begin(ingestUrl); + http.addHeader("Content-Type", "application/json"); + + String body = "{"; + body += "\"node_id\":\"" + String(nodeId) + "\","; + body += "\"lat\":" + String(nodeLat, 6) + ","; + body += "\"lng\":" + String(nodeLng, 6) + ","; + body += "\"acceleration_g\":" + String(acc, 4) + ","; + body += "\"gas_raw\":" + String(gasValue) + ","; + body += "\"motion\":" + String(motion == HIGH ? "true" : "false") + ","; + body += "\"temp_c\":" + String(temp, 2) + ","; + body += "\"humidity\":" + String(hum, 2); + body += "}"; + + http.POST(body); + http.end(); +} +``` + +In your `loop()`, call it after readings are computed: + +```cpp +postTelemetry(acc, gasValue, motion, temp, hum); +``` + +## Run + +1. Start dashboard: + +```bash +cd dashboard +npm run dev +``` + +2. Ensure ESP32 and laptop are on the same Wi-Fi. +3. Replace `` with your machine IP. +4. Open `http://localhost:3000` and trigger sensor events. + +## Quick Test without hardware + +```bash +curl -X POST http://localhost:3000/api/telemetry \ + -H 'Content-Type: application/json' \ + -d '{"node_id":"NM-01","lat":12.9141,"lng":74.8560,"acceleration_g":1.25,"gas_raw":2900,"motion":true}' +``` diff --git a/dashboard/eslint.config.mjs b/dashboard/eslint.config.mjs new file mode 100644 index 000000000..05e726d1b --- /dev/null +++ b/dashboard/eslint.config.mjs @@ -0,0 +1,18 @@ +import { defineConfig, globalIgnores } from "eslint/config"; +import nextVitals from "eslint-config-next/core-web-vitals"; +import nextTs from "eslint-config-next/typescript"; + +const eslintConfig = defineConfig([ + ...nextVitals, + ...nextTs, + // Override default ignores of eslint-config-next. + globalIgnores([ + // Default ignores of eslint-config-next: + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ]), +]); + +export default eslintConfig; diff --git a/dashboard/next.config.ts b/dashboard/next.config.ts new file mode 100644 index 000000000..d440f24b7 --- /dev/null +++ b/dashboard/next.config.ts @@ -0,0 +1,11 @@ +import type { NextConfig } from "next"; +import path from "path"; + +const nextConfig: NextConfig = { + /* config options here */ + turbopack: { + root: path.join(__dirname), + }, +}; + +export default nextConfig; diff --git a/dashboard/package-lock.json b/dashboard/package-lock.json new file mode 100644 index 000000000..b4b7f4ab3 --- /dev/null +++ b/dashboard/package-lock.json @@ -0,0 +1,7093 @@ +{ + "name": "dashboard", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "dashboard", + "version": "0.1.0", + "dependencies": { + "clsx": "^2.1.1", + "framer-motion": "^12.38.0", + "leaflet": "^1.9.4", + "lodash.debounce": "^4.0.8", + "lucide-react": "^1.8.0", + "next": "16.2.4", + "react": "19.2.4", + "react-dom": "19.2.4", + "react-leaflet": "^5.0.0", + "recharts": "^3.8.1", + "tailwind-merge": "^3.5.0" 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"https://registry.npmjs.org/zod-validation-error/-/zod-validation-error-4.0.2.tgz", + "integrity": "sha512-Q6/nZLe6jxuU80qb/4uJ4t5v2VEZ44lzQjPDhYJNztRQ4wyWc6VF3D3Kb/fAuPetZQnhS3hnajCf9CsWesghLQ==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=18.0.0" + }, + "peerDependencies": { + "zod": "^3.25.0 || ^4.0.0" + } + } + } +} diff --git a/dashboard/package.json b/dashboard/package.json new file mode 100644 index 000000000..a0aee6c3a --- /dev/null +++ b/dashboard/package.json @@ -0,0 +1,35 @@ +{ + "name": "dashboard", + "version": "0.1.0", + "private": true, + "scripts": { + "dev": "next dev", + "build": "next build", + "start": "next start", + "lint": "eslint" + }, + "dependencies": { + "clsx": "^2.1.1", + "framer-motion": "^12.38.0", + "leaflet": "^1.9.4", + "lodash.debounce": "^4.0.8", + "lucide-react": "^1.8.0", + "next": "16.2.4", + "react": "19.2.4", + "react-dom": "19.2.4", + "react-leaflet": "^5.0.0", + "recharts": "^3.8.1", + "tailwind-merge": "^3.5.0" + }, + "devDependencies": { + "@tailwindcss/postcss": "^4", + "@types/leaflet": "^1.9.21", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "16.2.4", + "tailwindcss": "^4", + "typescript": "^5" + } +} diff --git a/dashboard/postcss.config.mjs b/dashboard/postcss.config.mjs new file mode 100644 index 000000000..61e36849c --- /dev/null +++ b/dashboard/postcss.config.mjs @@ -0,0 +1,7 @@ +const config = { + plugins: { + "@tailwindcss/postcss": {}, + }, +}; + +export default config; diff --git a/dashboard/public/file.svg b/dashboard/public/file.svg new file mode 100644 index 000000000..004145cdd --- /dev/null +++ b/dashboard/public/file.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/dashboard/public/globe.svg b/dashboard/public/globe.svg new file mode 100644 index 000000000..567f17b0d --- /dev/null +++ b/dashboard/public/globe.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/dashboard/public/next.svg b/dashboard/public/next.svg new file mode 100644 index 000000000..5174b28c5 --- /dev/null +++ b/dashboard/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/dashboard/public/vercel.svg b/dashboard/public/vercel.svg new file mode 100644 index 000000000..770539603 --- /dev/null +++ b/dashboard/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/dashboard/public/window.svg b/dashboard/public/window.svg new file mode 100644 index 000000000..b2b2a44f6 --- /dev/null +++ b/dashboard/public/window.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/dashboard/src/app/api/run-pipeline/route.ts b/dashboard/src/app/api/run-pipeline/route.ts new file mode 100644 index 000000000..71eb1bbcd --- /dev/null +++ b/dashboard/src/app/api/run-pipeline/route.ts @@ -0,0 +1,43 @@ +import { NextRequest, NextResponse } from 'next/server'; +import { exec } from 'child_process'; +import path from 'path'; +import util from 'util'; + +const execPromise = util.promisify(exec); + +export async function POST(req: NextRequest) { + try { + const { scenario } = await req.json(); + + // We can pass scenario as an environment variable or argument to the python script + // For now, let's just run the pipeline and capture its output + // We adjust the path to reach the root directory from dashboard/src/api/run-pipeline + const rootDir = path.join(process.cwd(), '..'); + const pythonPath = path.join(rootDir, 'venv', 'Scripts', 'python.exe'); + const scriptPath = path.join(rootDir, 'pipeline', 'langgraph_pipeline.py'); + + // Run the pipeline + // In a real app, we'd pass raw data here. For simulation, we'll let the script use its internal scenarios. + const { stdout, stderr } = await execPromise(`"${pythonPath}" "${scriptPath}"`, { + cwd: rootDir, + env: { ...process.env, SCENARIO_OVERRIDE: scenario, OUTPUT_FORMAT: 'json' } + }); + + if (stderr && !stdout) { + return NextResponse.json({ error: stderr }, { status: 500 }); + } + + // Parse the output. Our script prints JSON-like block for sub-models and then the SITREP. + // Let's modify the python script to output clean JSON if a flag is passed. + // For now, we'll just return the raw stdout or parse it roughly. + + return NextResponse.json({ + raw: stdout, + message: "Pipeline executed successfully" + }); + + } catch (error: any) { + console.error('Pipeline error:', error); + return NextResponse.json({ error: error.message }, { status: 500 }); + } +} diff --git a/dashboard/src/app/api/telemetry/route.ts b/dashboard/src/app/api/telemetry/route.ts new file mode 100644 index 000000000..6c2f23ac5 --- /dev/null +++ b/dashboard/src/app/api/telemetry/route.ts @@ -0,0 +1,32 @@ +import { NextRequest, NextResponse } from 'next/server'; +import { getLiveSnapshot, ingestTelemetry, isValidIncomingTelemetry } from '@/lib/telemetryStore'; + +export async function GET() { + return NextResponse.json(getLiveSnapshot()); +} + +export async function POST(request: NextRequest) { + let body: unknown; + + try { + body = await request.json(); + } catch { + return NextResponse.json( + { error: 'Invalid JSON body.' }, + { status: 400 } + ); + } + + if (!isValidIncomingTelemetry(body)) { + return NextResponse.json( + { + error: + 'Invalid payload. Required: node_id, lat, lng, acceleration_g, gas_raw, motion.', + }, + { status: 400 } + ); + } + + const snapshot = ingestTelemetry(body); + return NextResponse.json(snapshot, { status: 202 }); +} diff --git a/dashboard/src/app/favicon.ico b/dashboard/src/app/favicon.ico new file mode 100644 index 000000000..718d6fea4 Binary files /dev/null and b/dashboard/src/app/favicon.ico differ diff --git a/dashboard/src/app/globals.css b/dashboard/src/app/globals.css new file mode 100644 index 000000000..a2dc41ece --- /dev/null +++ b/dashboard/src/app/globals.css @@ -0,0 +1,26 @@ +@import "tailwindcss"; + +:root { + --background: #ffffff; + --foreground: #171717; +} + +@theme inline { + --color-background: var(--background); + --color-foreground: var(--foreground); + --font-sans: var(--font-geist-sans); + --font-mono: var(--font-geist-mono); +} + +@media (prefers-color-scheme: dark) { + :root { + --background: #0a0a0a; + --foreground: #ededed; + } +} + +body { + background: var(--background); + color: var(--foreground); + font-family: Arial, Helvetica, sans-serif; +} diff --git a/dashboard/src/app/layout.tsx b/dashboard/src/app/layout.tsx new file mode 100644 index 000000000..976eb90df --- /dev/null +++ b/dashboard/src/app/layout.tsx @@ -0,0 +1,33 @@ +import type { Metadata } from "next"; +import { Geist, Geist_Mono } from "next/font/google"; +import "./globals.css"; + +const geistSans = Geist({ + variable: "--font-geist-sans", + subsets: ["latin"], +}); + +const geistMono = Geist_Mono({ + variable: "--font-geist-mono", + subsets: ["latin"], +}); + +export const metadata: Metadata = { + title: "Create Next App", + description: "Generated by create next app", +}; + +export default function RootLayout({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + return ( + + {children} + + ); +} diff --git a/dashboard/src/app/page.tsx b/dashboard/src/app/page.tsx new file mode 100644 index 000000000..5b87d19ec --- /dev/null +++ b/dashboard/src/app/page.tsx @@ -0,0 +1,119 @@ +'use client'; +import { useEffect, useState } from 'react'; +import MapPanel from '../components/MapPanel'; +import AlertFeed from '../components/AlertFeed'; +import SitrepPanel from '../components/SitrepPanel'; +import SystemHealth from '../components/SystemHealth'; + +type ThreatLevel = 'RED' | 'ORANGE' | 'YELLOW' | 'GREEN'; + +interface LiveAlert { + id: string; + threatLevel: ThreatLevel; + elapsed: string; + headline: string; + sitrep: string; + gasPpm: number; + survivability: number; + gasThreatLabel: string; + equipmentChecklist: string[]; +} + +interface TelemetrySnapshot { + alerts: LiveAlert[]; + activeNodes: number; + ingestStatus: 'LIVE' | 'STALE'; + lastIngestAt: number | null; +} + +export default function Dashboard() { + const [activeAlert, setActiveAlert] = useState(null); + const [snapshot, setSnapshot] = useState({ + alerts: [], + activeNodes: 0, + ingestStatus: 'STALE', + lastIngestAt: null, + }); + + useEffect(() => { + let cancelled = false; + + const pullTelemetry = async () => { + try { + const response = await fetch('/api/telemetry', { cache: 'no-store' }); + if (!response.ok) { + return; + } + + const data = (await response.json()) as TelemetrySnapshot; + if (cancelled) { + return; + } + + setSnapshot(data); + + setActiveAlert((current) => { + if (!data.alerts.length) { + return null; + } + + if (!current) { + return data.alerts[0]; + } + + const refreshed = data.alerts.find((entry) => entry.id === current.id); + return refreshed ?? data.alerts[0]; + }); + } catch { + // Keep previous snapshot if network blips occur. + } + }; + + pullTelemetry(); + const intervalId = window.setInterval(pullTelemetry, 1000); + + return () => { + cancelled = true; + window.clearInterval(intervalId); + }; + }, []); + + const alerts = snapshot.alerts; + + return ( +
+ {/* Top Bar: System Health (10% height) */} +
+ +
+ + {/* Main Content Area */} +
+ {/* Map Panel */} +
+ +
+ + {/* Right Panel: Alert Feed (20% width) */} +
+ +
+ + {/* Bottom SITREP Panel (Slides up when an alert is active) */} + {activeAlert && ( +
+ setActiveAlert(null)} /> +
+ )} +
+
+ ); +} diff --git a/dashboard/src/components/AlertFeed.tsx b/dashboard/src/components/AlertFeed.tsx new file mode 100644 index 000000000..14d9264f7 --- /dev/null +++ b/dashboard/src/components/AlertFeed.tsx @@ -0,0 +1,63 @@ +interface AlertItem { + id: string; + threatLevel: 'RED' | 'ORANGE' | 'YELLOW' | 'GREEN'; + elapsed: string; + headline: string; +} + +interface AlertFeedProps { + alerts: AlertItem[]; + onSelectAlert: (alert: AlertItem) => void; + activeAlertId?: string; +} + +export default function AlertFeed({ alerts, onSelectAlert, activeAlertId }: AlertFeedProps) { + return ( +
+
+ Live Alert Feed +
+
+ {alerts.length === 0 && ( +
+ Waiting for live telemetry from field nodes... +
+ )} + {alerts.map((alert) => ( +
onSelectAlert(alert)} + className={`p-3 rounded cursor-pointer border transition-all ${ + activeAlertId === alert.id + ? 'bg-neutral-800 border-neutral-600' + : 'bg-neutral-900 border-neutral-800 hover:border-neutral-700' + } ${alert.threatLevel === 'RED' ? 'animate-[pulse_2s_ease-in-out_infinite] border-red-900/50' : ''}`} + > +
+ {alert.id} + {alert.elapsed} ago +
+
+
+

{alert.headline}

+
+
+ +
+
+ ))} +
+
+ ); +} diff --git a/dashboard/src/components/MapComponent.tsx b/dashboard/src/components/MapComponent.tsx new file mode 100644 index 000000000..5e8283a60 --- /dev/null +++ b/dashboard/src/components/MapComponent.tsx @@ -0,0 +1,73 @@ +'use client'; + +import { MapContainer, TileLayer, Marker, Popup, useMap } from 'react-leaflet'; +import L from 'leaflet'; +import 'leaflet/dist/leaflet.css'; +import { useEffect } from 'react'; + +// Fix for default marker icons in Leaflet with Next.js +const icon = L.icon({ + iconUrl: 'https://unpkg.com/leaflet@1.9.4/dist/images/marker-icon.png', + shadowUrl: 'https://unpkg.com/leaflet@1.9.4/dist/images/marker-shadow.png', + iconSize: [25, 41], + iconAnchor: [12, 41], +}); + +interface MapComponentProps { + nodes: { id: string; name: string; lat: number; lng: number }[]; + activeNodeId: string | null; + onNodeClick: (id: string) => void; +} + +function ChangeView({ center }: { center: [number, number] }) { + const map = useMap(); + useEffect(() => { + map.setView(center, 14); + }, [center, map]); + return null; +} + +export default function MapComponent({ nodes, activeNodeId, onNodeClick }: MapComponentProps) { + const activeNode = nodes.find(n => n.id === activeNodeId); + const center: [number, number] = activeNode ? [activeNode.lat, activeNode.lng] : [12.87, 74.84]; + + return ( +
+ + + + {nodes.map((node) => ( + onNodeClick(node.id), + }} + > + +
+ {node.id}
+ {node.name} +
+ + + ))} + + {activeNode && } + + + {/* Overlay for aesthetic blending */} +
+
+ ); +} diff --git a/dashboard/src/components/MapPanel.tsx b/dashboard/src/components/MapPanel.tsx new file mode 100644 index 000000000..5dc48be8c --- /dev/null +++ b/dashboard/src/components/MapPanel.tsx @@ -0,0 +1,50 @@ +'use client'; +import { useEffect, useRef } from 'react'; + +export default function MapPanel({ activeAlert }) { + const mapContainer = useRef(null); + + useEffect(() => { + // In a real app we'd initialize MapLibre here: + // const map = new maplibregl.Map({ container: mapContainer.current, style: '...' }) + // return () => map.remove(); + }, []); + + return ( +
+
+ {/* Placeholder for MapLibre */} +
+
+ MapLibre GL Instance +
+
+ + {/* CSS Mock of a radar/map sweep */} +
+
+
+
+
+
+
+ + {activeAlert && ( +
+
+
+
+ {activeAlert.id} Incident Zone +
+
+ )} + + {/* Target Crosshair */} + {!activeAlert && ( +
+ ā”¼ +
+ )} +
+ ); +} diff --git a/dashboard/src/components/SimulationDashboard.tsx b/dashboard/src/components/SimulationDashboard.tsx new file mode 100644 index 000000000..431043b54 --- /dev/null +++ b/dashboard/src/components/SimulationDashboard.tsx @@ -0,0 +1,452 @@ +'use client'; + +import { useState, useEffect } from 'react'; +import { motion, AnimatePresence } from 'framer-motion'; +import { Radio, AlertTriangle, ShieldCheck, Flame, Users, CheckCircle2, Activity, MapPin, Orbit, Zap, Wind, Database } from 'lucide-react'; +import dynamic from 'next/dynamic'; + +const MapComponent = dynamic(() => import('./MapComponent'), { ssr: false }); + +interface ScenarioResult { + sitrep: string; + severity: 'CRITICAL' | 'HIGH' | 'MEDIUM' | 'LOW' | 'none'; + validation: { is_genuine_event: boolean; anomaly_score: number }; + seismic: { event_type: string; magnitude: string }; + gas: { hazard_type: string; severity: string; current_ppm: number }; + survivor: { estimated_count: string; urgency: string }; + location: { node_id: string; address: string; lat: number; lng: number }; +} + +// Simulated Mesh Network Nodes with real coordinates +const NODES = [ + { id: 'NM-01', x: 20, y: 30, name: 'Hampankatta Zone A', lat: 12.871, lng: 74.842 }, + { id: 'NM-02', x: 45, y: 25, name: 'Kankanady Market', lat: 12.875, lng: 74.855 }, + { id: 'NM-03', x: 70, y: 40, name: 'Kadri Park Area', lat: 12.882, lng: 74.863 }, + { id: 'NM-04', x: 35, y: 65, name: 'State Bank Region', lat: 12.862, lng: 74.831 }, + { id: 'NM-05', x: 60, y: 75, name: 'Pandeshwar Hub', lat: 12.865, lng: 74.845 }, + { id: 'NM-06', x: 80, y: 60, name: 'Bendoorwell', lat: 12.891, lng: 74.852 }, +]; + +const SCENARIOS: Record = { + collapse_gas: { + id: 'collapse_gas', + name: 'Structural Collapse + Gas', + description: 'Seismic collapse signature, critical LPG leak, multiple survivors detected.', + node: 'NM-01', + output: { + sitrep: `1. SITUATION: Severe structural collapse with confirmed rising LPG leak; 3-4 potential survivors trapped inside.\n2. THREAT LEVEL: RED (Immediate high-risk CBRN + structural hazard).\n3. SURVIVOR STATUS: 98% probability; 3-4 trapped; PIR detections confirm active movement.\n4. HAZARDS: LPG leak (650 ppm, critical, non-entry zone); explosion risk imminent.\n5. RECOMMENDED ACTION: NDRF team: Deploy CBRN squad. Establish 50m exclusion zone. Robotic entry for initial assessment.\n6. TIME SENSITIVITY: CRITICAL ā€“ LPG concentration rising; survivors have <30 mins.`, + severity: 'CRITICAL', + validation: { is_genuine_event: true, anomaly_score: -0.499 }, + seismic: { event_type: 'structural_collapse', magnitude: 'high' }, + gas: { hazard_type: 'LPG_leak', severity: 'critical', current_ppm: 650.0 }, + survivor: { estimated_count: '3-4', urgency: 'immediate' }, + location: { node_id: 'NM-01', address: 'Hampankatta Zone A', lat: 12.87, lng: 74.84 } + } + }, + deep_earthquake: { + id: 'deep_earthquake', + name: 'Major Earthquake Response', + description: 'High magnitude earthquake, no secondary gas leaks, massive structural impact.', + node: 'NM-04', + output: { + sitrep: `1. SITUATION: Magnitude 6.2 localized earthquake impact detected. Area structurally compromised.\n2. THREAT LEVEL: ORANGE (High structural hazard, safe atmosphere).\n3. SURVIVOR STATUS: 65% probability; 5-8 potential buried individuals detected via seismic heartbeat patterns.\n4. HAZARDS: Aftershocks, falling debris. Air quality is NORMAL.\n5. RECOMMENDED ACTION: Deploy heavy USAR (Urban Search and Rescue) teams. Bring acoustic locators and heavy lifting gear.\n6. TIME SENSITIVITY: HIGH ā€“ The "Golden Day" window has started. Immediate dispatch recommended.`, + severity: 'HIGH', + validation: { is_genuine_event: true, anomaly_score: -0.210 }, + seismic: { event_type: 'earthquake', magnitude: 'severe' }, + gas: { hazard_type: 'safe', severity: 'normal', current_ppm: 45.0 }, + survivor: { estimated_count: '5-8', urgency: 'high' }, + location: { node_id: 'NM-04', address: 'State Bank Region', lat: 12.86, lng: 74.83 } + } + }, + fire_hazard: { + id: 'fire_hazard', + name: 'Smoldering Fire / Smoke', + description: 'No seismic crash, but rapidly rising ppm matching smoke/fire profiles.', + node: 'NM-06', + output: { + sitrep: `1. SITUATION: Dense smoke/fire detected in enclosed structure.\n2. THREAT LEVEL: ORANGE (Toxic gas/Fire hazard).\n3. SURVIVOR STATUS: 40% probability; 1-2 individuals detected via fading PIR triggers.\n4. HAZARDS: Smoke inhalation hazard (800+ ppm). Zero visibility.\n5. RECOMMENDED ACTION: Dispatch Fire Brigade immediately. NDRF backup for potential extraction. Use thermal imaging.\n6. TIME SENSITIVITY: CRITICAL ā€“ Asphyxiation risk is extreme. Extract within 15 minutes.`, + severity: 'HIGH', + validation: { is_genuine_event: true, anomaly_score: -0.105 }, + seismic: { event_type: 'normal', magnitude: 'none' }, + gas: { hazard_type: 'smoke_fire', severity: 'critical', current_ppm: 820.0 }, + survivor: { estimated_count: '1-2', urgency: 'immediate' }, + location: { node_id: 'NM-06', address: 'Bendoorwell', lat: 12.89, lng: 74.85 } + } + }, + minor_tremor: { + id: 'minor_tremor', + name: 'Minor Localized Tremor', + description: 'Low magnitude tremor, safe gas, minimal panic/PIR activity. Routine check.', + node: 'NM-02', + output: { + sitrep: `1. SITUATION: Low magnitude localized tremor detected. No structural failure observed.\n2. THREAT LEVEL: GREEN (Monitor only).\n3. SURVIVOR STATUS: N/A - No collapse. Normal civilian movement detected.\n4. HAZARDS: None detected. Atmosphere and structures appear stable.\n5. RECOMMENDED ACTION: Log event. Local civil defense to do a perimeter routine check.\n6. TIME SENSITIVITY: LOW.`, + severity: 'LOW', + validation: { is_genuine_event: true, anomaly_score: -0.05 }, + seismic: { event_type: 'truck_passing/minor', magnitude: 'low' }, + gas: { hazard_type: 'safe', severity: 'normal', current_ppm: 48.0 }, + survivor: { estimated_count: '0', urgency: 'low' }, + location: { node_id: 'NM-02', address: 'Kankanady Market', lat: 12.875, lng: 74.855 } + } + }, + false_alarm: { + id: 'false_alarm', + name: 'Sensor Anomaly (False Alarm)', + description: 'Erratic seismic data but normal gas and no PIR. Flagged as anomaly.', + node: 'NM-03', + output: { + sitrep: `FALSE ALARM ā€” Event did not pass authenticity validation. Hardware glitch suspected. No deployment required.`, + severity: 'none', + validation: { is_genuine_event: false, anomaly_score: -0.662 }, + seismic: { event_type: 'earthquake', magnitude: 'high' }, + gas: { hazard_type: 'safe', severity: 'normal', current_ppm: 52.7 }, + survivor: { estimated_count: 'unknown', urgency: 'low' }, + location: { node_id: 'NM-03', address: 'Kadri Park Area', lat: 12.88, lng: 74.86 } + } + }, +}; + +function PipelineStage({ label, icon, state, isValidator }: any) { + let bg = 'bg-slate-900 border-slate-800 text-slate-600'; + if (state === 'active') bg = isValidator ? 'bg-amber-500/20 border-amber-500 text-amber-400 shadow-[0_0_15px_rgba(251,191,36,0.3)]' : 'bg-emerald-500/20 border-emerald-500 text-emerald-400 shadow-[0_0_15px_rgba(16,185,129,0.3)] animate-pulse'; + if (state === 'done') bg = isValidator ? 'bg-amber-500/10 border-amber-500/50 text-amber-500' : 'bg-emerald-500/10 border-emerald-500/50 text-emerald-500'; + + return ( +
+
+ {icon} +
+ {label} +
+ ); +} + +function DataCard({ title, val, sub, icon }: any) { + return ( +
+
+ {icon} +
+
+

{title}

+
{val}
+
{sub}
+
+
+ ); +} + +export default function SimulationDashboard() { + const [activeScenario, setActiveScenario] = useState(null); + const [pipelineState, setPipelineState] = useState<'idle' | 'transmitting' | 'models' | 'validating' | 'llm' | 'complete'>('idle'); + const [result, setResult] = useState(null); + + const runScenario = async (key: keyof typeof SCENARIOS) => { + setActiveScenario(key); + setPipelineState('transmitting'); + setResult(null); + + try { + // Start the staggered visual pipeline, but wait for actual result + const visualSteps = [ + { state: 'models', delay: 1000 }, + { state: 'validating', delay: 2500 }, + { state: 'llm', delay: 4000 }, + ]; + + visualSteps.forEach(step => { + setTimeout(() => setPipelineState(step.state as any), step.delay); + }); + + const response = await fetch('/api/run-pipeline', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ scenario: key }) + }); + + const data = await response.json(); + + if (data.error) throw new Error(data.error); + + // Parse the raw output from Python (which we now force to be JSON) + const parsed = JSON.parse(data.raw); + + // Map Python output to frontend ScenarioResult format + const formattedResult: ScenarioResult = { + sitrep: parsed.sitrep, + severity: parsed.severity, + validation: { + is_genuine_event: parsed.model_outputs.validation.is_genuine_event, + anomaly_score: parsed.model_outputs.validation.anomaly_score + }, + seismic: { + event_type: parsed.model_outputs.seismic.prediction, + magnitude: parsed.model_outputs.seismic.is_crisis ? 'high' : 'low' + }, + gas: { + hazard_type: parsed.model_outputs.gas.prediction, + severity: parsed.model_outputs.gas.severity, + current_ppm: parsed.model_outputs.gas.ppm + }, + survivor: { + estimated_count: parsed.model_outputs.survivor.count.toString(), + urgency: parsed.model_outputs.survivor.urgency + }, + location: parsed.location + }; + + // Ensure at least 5 seconds of animation for dramatic effect + setTimeout(() => { + setResult(formattedResult); + setPipelineState('complete'); + }, 5000); + + } catch (err) { + console.error("Pipeline failed:", err); + // Fallback to mock data if backend fails + setTimeout(() => { + setResult(SCENARIOS[key].output); + setPipelineState('complete'); + }, 5000); + } + }; + + const activeNodeId = activeScenario ? SCENARIOS[activeScenario].node : null; + + return ( +
+ + {/* LEFT SIDEBAR: Scenarios */} +
+
+ +
+

NeuroMesh

+

NDRF CMD // v2.0.4

+
+
+ +

+ Test Scenarios Override +

+ +
+ {Object.entries(SCENARIOS).map(([key, data]) => { + const isActive = activeScenario === key; + return ( + + ); + })} +
+
+ + {/* MAIN VIEW: Map & Pipeline */} +
+ + {/* TOP HALF: Interactive Map with Stadia Maps */} +
+ { + const scenario = Object.keys(SCENARIOS).find(k => SCENARIOS[k].node === id); + if (scenario) runScenario(scenario as any); + }} + /> + + {/* Top Right Live Status */} +
+
+ + LoRaWAN NETWORK (LIVE) + +
+ {pipelineState !== 'idle' && ( + + EVENT DETECTED + + )} +
+
+ + {/* BOTTOM HALF: Pipeline & Report */} +
+ + {!activeScenario ? ( +
+ +

System Standby

+

Awaiting sensor interruptions...

+
+ ) : ( +
+ + {/* Pipeline Tracker */} +
+
+ + } + state={pipelineState === 'transmitting' ? 'active' : 'done'} + /> + } + state={pipelineState === 'models' ? 'active' : (['validating', 'llm', 'complete'].includes(pipelineState) ? 'done' : 'idle')} + /> + } + state={pipelineState === 'validating' ? 'active' : (['llm', 'complete'].includes(pipelineState) ? 'done' : 'idle')} + isValidator + /> + } + state={pipelineState === 'llm' ? 'active' : (pipelineState === 'complete' ? 'done' : 'idle')} + /> +
+ + {/* Data Modulators View (Appears during 'models' phase) */} + + {['models', 'validating', 'llm', 'complete'].includes(pipelineState) && ( + + } + /> + } + /> + } + /> + + )} + + + {/* Validator Output (Appears during 'validating' phase) */} + + {['validating', 'llm', 'complete'].includes(pipelineState) && ( + +
+ +
+

Isolation Forest Validation

+

Anomaly Score: {result?.validation.anomaly_score || SCENARIOS[activeScenario].output.validation.anomaly_score}

+
+
+
+ {(result?.validation.is_genuine_event ?? SCENARIOS[activeScenario].output.validation.is_genuine_event) ? 'AUTHENTIC CRISIS' : 'FALSE ALARM FLAGGED'} +
+ + )} + + + {/* THE FINAL LLM SITREP */} + + {pipelineState === 'complete' && result && ( + + {/* Background glow */} +
+ +
+
+ {result.severity === 'CRITICAL' ? + : result.severity === 'none' ? + : result.severity === 'LOW' ? + : } +
+

MISTRAL GENERATED SITREP

+

+ {result.severity === 'none' ? 'ABORT: NO ACTION REQUIRED' : `THREAT LEVEL: ${result.severity}`} +

+
+
+ +
+ + {result.location.node_id} + +
{result.location.address}
+
{result.location.lat}, {result.location.lng}
+
+
+ +
+ {result.sitrep.split('\n').map((line, idx) => { + const isAction = line.includes('RECOMMENDED ACTION') || line.includes('TIME SENSITIVITY') || line.includes('FALSE ALARM'); + return ( +

+ {line} +

+ ); + })} +
+ + )} + + +
+ )} +
+
+
+ ); +} diff --git a/dashboard/src/components/SitrepPanel.tsx b/dashboard/src/components/SitrepPanel.tsx new file mode 100644 index 000000000..485e87e78 --- /dev/null +++ b/dashboard/src/components/SitrepPanel.tsx @@ -0,0 +1,84 @@ +interface SitrepAlert { + id: string; + threatLevel: 'RED' | 'ORANGE' | 'YELLOW' | 'GREEN'; + sitrep?: string; + gasPpm?: number; + survivability?: number; + gasThreatLabel?: string; + equipmentChecklist?: string[]; +} + +interface SitrepPanelProps { + alert: SitrepAlert; + onClose: () => void; +} + +export default function SitrepPanel({ alert, onClose }: SitrepPanelProps) { + const survivability = alert.survivability ?? 78; + const gasThreat = alert.gasThreatLabel ?? `MODERATE (${alert.gasPpm ?? 420} PPM)`; + const checklist = alert.equipmentChecklist ?? ['Gas Masks', 'Concrete Saws', 'Thermal Cameras']; + + return ( +
+
+
+ + THREAT: {alert.threatLevel} + + ALERT ID: {alert.id} +
+ +
+ +
+
+

Tactical SITREP

+
+ {alert.sitrep ?? `Structural instability observed at ${alert.id}. Field command should keep extraction teams staged with rapid entry protocol.`} +
+
+ +
+
+

Survivability

+
+ {/* Pseudo circular progress */} +
+ {survivability}% +
+
+
+

Gas Threat

+
+ {gasThreat} +
+
+
+ +
+
+

Logistics

+
+ {checklist.map((item) => ( + + ))} +
+
+
+ + +
+
+
+
+ ); +} diff --git a/dashboard/src/components/SystemHealth.tsx b/dashboard/src/components/SystemHealth.tsx new file mode 100644 index 000000000..a80fc97a7 --- /dev/null +++ b/dashboard/src/components/SystemHealth.tsx @@ -0,0 +1,48 @@ +interface SystemHealthProps { + activeNodes: number; + alertCount: number; + pipelineStatus: 'LIVE' | 'STALE'; +} + +export default function SystemHealth({ activeNodes, alertCount, pipelineStatus }: SystemHealthProps) { + return ( +
+
+
+
+ NEUROMESH HQ +
+
+
+
+ Active Nodes: + {activeNodes} +
+
+ Alert State: + 0 ? 'text-red-500' : 'text-emerald-400'}`}>{alertCount} +
+
+ Pipeline Status: + {pipelineStatus} +
+
+
+ +
+
+ LoRa Check: + 100% +
+
+ + +
+
+
+ ); +} diff --git a/dashboard/tsconfig.json b/dashboard/tsconfig.json new file mode 100644 index 000000000..cf9c65d3e --- /dev/null +++ b/dashboard/tsconfig.json @@ -0,0 +1,34 @@ +{ + "compilerOptions": { + "target": "ES2017", + "lib": ["dom", "dom.iterable", "esnext"], + "allowJs": true, + "skipLibCheck": true, + "strict": true, + "noEmit": true, + "esModuleInterop": true, + "module": "esnext", + "moduleResolution": "bundler", + "resolveJsonModule": true, + "isolatedModules": true, + "jsx": "react-jsx", + "incremental": true, + "plugins": [ + { + "name": "next" + } + ], + "paths": { + "@/*": ["./src/*"] + } + }, + "include": [ + "next-env.d.ts", + "**/*.ts", + "**/*.tsx", + ".next/types/**/*.ts", + ".next/dev/types/**/*.ts", + "**/*.mts" + ], + "exclude": ["node_modules"] +} diff --git a/data/gas_data.csv b/data/gas_data.csv new file mode 100644 index 000000000..d8464ae10 --- /dev/null +++ b/data/gas_data.csv @@ -0,0 +1,801 @@ +ppm_0,ppm_1,ppm_2,ppm_3,ppm_4,ppm_5,ppm_6,ppm_7,ppm_8,ppm_9,ppm_10,ppm_11,ppm_12,ppm_13,ppm_14,ppm_15,ppm_16,ppm_17,ppm_18,ppm_19,ppm_20,ppm_21,ppm_22,ppm_23,ppm_24,ppm_25,ppm_26,ppm_27,ppm_28,ppm_29,seismic_flag,label +42.403047874211346,57.852302955647104,44.05866815376878,64.30508190339734,50.77604264530424,35.904832107282886,53.46809870132052,16.916772990398737,53.089069055227064,55.3846148990856,46.66066985896088,41.64434483749369,69.5887415857938,46.36113154050788,32.2964031150959,39.538628269402615,51.10649029948616,42.307785592830115,47.3518728870482,54.06639641098578,52.20472873297906,39.15476015666269,29.524316648739738,31.26719769955479,51.006299666888935,49.271887879585144,68.91740373632204,44.99964234550383,46.548208380952104,47.165806884113834,0,0 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+59.57744042702434,73.06968265558976,66.07341989053788,62.524626376589325,55.02932102670436,75.07288343523106,43.95816633788885,73.17313110206447,75.98561732242793,890.6400141099493,765.2910660330873,714.2574074242499,779.6433615552024,765.9199038979436,691.3289703291098,633.9398508191844,898.6403333885298,648.8845107530353,758.6640156045291,830.9172117257158,659.8016837057236,617.9327598814317,833.6361630691728,682.494510597623,799.1164771152565,625.7297827055083,716.2158863554951,816.2326798332514,728.1218522827921,839.5961026008124,0,2 diff --git a/data/generate_gas_data.py b/data/generate_gas_data.py new file mode 100644 index 000000000..4bc3df118 --- /dev/null +++ b/data/generate_gas_data.py @@ -0,0 +1,31 @@ +import numpy as np +import pandas as pd + +samples = [] + +# Normal air ā€” low stable PPM +for _ in range(300): + ppm = np.random.normal(50, 10, 30) # 30 readings + seismic_flag = 0 + samples.append(list(ppm) + [seismic_flag, 0]) # label 0 = safe + +# LPG leak ā€” rapidly rising PPM after seismic event +for _ in range(250): + base = np.random.uniform(50, 100) + ppm = [base + i * np.random.uniform(20, 40) for i in range(30)] + ppm = np.clip(ppm, 0, 10000) + seismic_flag = 1 + samples.append(list(ppm) + [seismic_flag, 1]) # label 1 = LPG + +# Smoke/fire ā€” sudden jump then plateau +for _ in range(250): + ppm = np.random.normal(60, 15, 30) + jump_point = np.random.randint(5, 15) + ppm[jump_point:] = np.random.uniform(600, 900, 30 - jump_point) + seismic_flag = np.random.choice([0, 1]) + samples.append(list(ppm) + [seismic_flag, 2]) # label 2 = smoke + +df = pd.DataFrame(samples) +df.columns = [f'ppm_{i}' for i in range(30)] + ['seismic_flag', 'label'] +df.to_csv('data/gas_data.csv', index=False) +print("Gas data generated:", len(df), "samples") \ No newline at end of file diff --git a/data/generate_seismic_data.py b/data/generate_seismic_data.py new file mode 100644 index 000000000..69b8b74fc --- /dev/null +++ b/data/generate_seismic_data.py @@ -0,0 +1,53 @@ +import numpy as np +import pandas as pd + +# Each sample = 50 readings of X,Y,Z = 150 numbers + 1 label +# Labels: 0=normal, 1=truck, 2=collapse, 3=earthquake + +samples = [] + +# Normal state ā€” small random noise around 0 +for _ in range(300): + x = np.random.normal(0, 0.05, 50) + y = np.random.normal(0, 0.05, 50) + z = np.random.normal(1, 0.05, 50) # z=1G always due to gravity + samples.append(np.concatenate([x, y, z, [0]])) + +# Truck passing ā€” single short spike then back to normal +for _ in range(200): + x = np.random.normal(0, 0.05, 50) + y = np.random.normal(0, 0.05, 50) + z = np.random.normal(1, 0.05, 50) + spike_pos = np.random.randint(10, 40) + x[spike_pos] = np.random.uniform(1.5, 2.5) # one spike only + samples.append(np.concatenate([x, y, z, [1]])) + +# Building collapse ā€” sharp spike then sustained irregular tremor +for _ in range(300): + x = np.random.normal(0, 0.05, 50) + y = np.random.normal(0, 0.05, 50) + z = np.random.normal(1, 0.05, 50) + spike_pos = np.random.randint(5, 15) + # Sharp initial spike + x[spike_pos] = np.random.uniform(3, 5) + y[spike_pos] = np.random.uniform(2, 4) + # Sustained tremor after spike + for i in range(spike_pos+1, 50): + x[i] = np.random.normal(0, 0.8) # irregular sustained motion + y[i] = np.random.normal(0, 0.6) + samples.append(np.concatenate([x, y, z, [2]])) + +# Earthquake ā€” wave pattern, rhythmic oscillation +for _ in range(300): + t = np.linspace(0, 5, 50) + freq = np.random.uniform(1, 4) + amplitude = np.random.uniform(1.5, 4) + x = amplitude * np.sin(2 * np.pi * freq * t) + np.random.normal(0, 0.1, 50) + y = amplitude * np.cos(2 * np.pi * freq * t) + np.random.normal(0, 0.1, 50) + z = np.random.normal(1, 0.3, 50) + samples.append(np.concatenate([x, y, z, [3]])) + +df = pd.DataFrame(samples) +df.columns = [f'f{i}' for i in range(150)] + ['label'] +df.to_csv('data/seismic_data.csv', index=False) +print("Seismic data generated:", len(df), "samples") \ No newline at end of file diff --git a/data/generate_survivor_data.py b/data/generate_survivor_data.py new file mode 100644 index 000000000..1b84fff96 --- /dev/null +++ b/data/generate_survivor_data.py @@ -0,0 +1,29 @@ +import numpy as np +import pandas as pd + +samples = [] + +# Survivors present ā€” high PIR, magnitude medium/high, recent event +for _ in range(300): + pir_count = np.random.randint(5, 20) # lots of movement + time_since_event = np.random.uniform(0, 30) # recent (minutes) + magnitude = np.random.uniform(2, 5) + samples.append([pir_count, time_since_event, magnitude, 1]) + +# No survivors / deeply buried ā€” low PIR, high magnitude +for _ in range(300): + pir_count = np.random.randint(0, 2) + time_since_event = np.random.uniform(60, 360) + magnitude = np.random.uniform(3, 6) + samples.append([pir_count, time_since_event, magnitude, 0]) + +# Uncertain ā€” some PIR, moderate conditions +for _ in range(200): + pir_count = np.random.randint(1, 5) + time_since_event = np.random.uniform(20, 90) + magnitude = np.random.uniform(1.5, 3.5) + samples.append([pir_count, time_since_event, magnitude, np.random.choice([0, 1])]) + +df = pd.DataFrame(samples, columns=['pir_count', 'time_since_event_mins', 'magnitude', 'survivor_present']) +df.to_csv('data/survivor_data.csv', index=False) +print("Survivor data generated:", len(df), "samples") \ No newline at end of file diff --git a/data/seismic_data.csv b/data/seismic_data.csv new file mode 100644 index 000000000..38ded0edf --- /dev/null +++ b/data/seismic_data.csv @@ -0,0 +1,1101 @@ 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mode 100644 index 000000000..2aed683d7 Binary files /dev/null and b/models/survivor_model.pkl differ diff --git a/models/survivor_scaler.pkl b/models/survivor_scaler.pkl new file mode 100644 index 000000000..187ba3c6a Binary files /dev/null and b/models/survivor_scaler.pkl differ diff --git a/models/validator_model.pkl b/models/validator_model.pkl new file mode 100644 index 000000000..abf8d9683 Binary files /dev/null and b/models/validator_model.pkl differ diff --git a/pipeline/firebase_live_bridge.py b/pipeline/firebase_live_bridge.py new file mode 100644 index 000000000..fdd61aa83 --- /dev/null +++ b/pipeline/firebase_live_bridge.py @@ -0,0 +1,258 @@ +import argparse +import json +import os +import time +from datetime import UTC, datetime +from typing import Any, Dict, Optional +from urllib.error import HTTPError, URLError +from urllib.request import Request, urlopen + +from pipeline.graph import run_neuromesh_pipeline + + +ACC_THRESHOLD = 0.25 +GAS_THRESHOLD = 2000 +EARTHQUAKE_DURATION_MS = 10_000 + + +class FirebaseLiveBridge: + def __init__( + self, + database_url: str, + firebase_path: str, + dashboard_url: Optional[str], + poll_seconds: float, + node_id: str, + lat: float, + lng: float, + address: str, + ) -> None: + self.database_url = database_url.rstrip('/') + self.firebase_path = firebase_path.strip('/') + self.dashboard_url = dashboard_url.rstrip('/') if dashboard_url else None + self.poll_seconds = poll_seconds + self.node_id = node_id + self.lat = lat + self.lng = lng + self.address = address + + self.last_timestamp: Optional[str] = None + self.earthquake_start_ms: Optional[int] = None + + def _firebase_endpoint(self) -> str: + return f"{self.database_url}/{self.firebase_path}.json" + + def _firebase_inference_endpoint(self) -> str: + return f"{self.database_url}/inference/{self.node_id}.json" + + def _http_json(self, url: str, method: str = 'GET', payload: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + data = None + headers = {} + + if payload is not None: + data = json.dumps(payload).encode('utf-8') + headers['Content-Type'] = 'application/json' + + request = Request(url=url, data=data, headers=headers, method=method) + with urlopen(request, timeout=12) as response: + raw = response.read().decode('utf-8') + if not raw: + return {} + parsed = json.loads(raw) + return parsed if isinstance(parsed, dict) else {} + + def fetch_latest(self) -> Optional[Dict[str, Any]]: + try: + payload = self._http_json(self._firebase_endpoint()) + if not payload: + return None + return payload + except (URLError, HTTPError, TimeoutError) as err: + print(f"[Firebase] fetch failed: {err}") + return None + + def _to_float(self, value: Any, default: float = 0.0) -> float: + try: + return float(value) + except (TypeError, ValueError): + return default + + def _to_int(self, value: Any, default: int = 0) -> int: + try: + return int(value) + except (TypeError, ValueError): + return default + + def _is_new_record(self, node_payload: Dict[str, Any]) -> bool: + timestamp = str(node_payload.get('timestamp', '')) + if not timestamp: + return True + if timestamp == self.last_timestamp: + return False + self.last_timestamp = timestamp + return True + + def _compute_event_state(self, acc: float) -> Dict[str, Any]: + quake = abs(acc - 1.0) > ACC_THRESHOLD + now_ms = int(time.time() * 1000) + + if quake: + self.earthquake_start_ms = now_ms + + earthquake_active = False + event_duration_ms = 200 + + if self.earthquake_start_ms is not None: + elapsed = now_ms - self.earthquake_start_ms + earthquake_active = elapsed <= EARTHQUAKE_DURATION_MS + if earthquake_active: + event_duration_ms = max(250, elapsed) + + return { + 'quake': quake, + 'earthquake_active': earthquake_active, + 'event_duration_ms': event_duration_ms, + } + + def _make_node_report(self, node_payload: Dict[str, Any]) -> Dict[str, Any]: + acc = self._to_float(node_payload.get('acceleration'), 1.0) + gas = self._to_float(node_payload.get('gas'), 0.0) + motion = self._to_int(node_payload.get('motion'), 0) + + event_state = self._compute_event_state(acc) + seismic_magnitude = abs(acc - 1.0) * 20.0 + + return { + 'node_id': self.node_id, + 'lat': self.lat, + 'lng': self.lng, + 'seismic_magnitude': round(seismic_magnitude, 3), + 'gas_ppm': gas, + 'pir_count': 2 if motion > 0 else 0, + 'event_duration_ms': event_state['event_duration_ms'], + 'timestamp': datetime.now(UTC).isoformat(), + } + + def _post_dashboard_telemetry(self, node_payload: Dict[str, Any]) -> None: + if not self.dashboard_url: + return + + body = { + 'node_id': self.node_id, + 'lat': self.lat, + 'lng': self.lng, + 'address': self.address, + 'acceleration_g': self._to_float(node_payload.get('acceleration'), 1.0), + 'gas_raw': self._to_int(node_payload.get('gas'), 0), + 'motion': self._to_int(node_payload.get('motion'), 0) > 0, + 'temp_c': self._to_float(node_payload.get('temperature'), 0.0), + 'humidity': self._to_float(node_payload.get('humidity'), 0.0), + 'timestamp': datetime.now(UTC).isoformat(), + } + + api_url = f"{self.dashboard_url}/api/telemetry" + try: + self._http_json(api_url, method='POST', payload=body) + except (URLError, HTTPError, TimeoutError) as err: + print(f"[Dashboard] POST failed: {err}") + + def _write_inference(self, result: Dict[str, Any]) -> None: + summary = { + 'updatedAt': datetime.now(UTC).isoformat(), + 'pipelineStatus': result.get('pipeline_status'), + 'abortReason': result.get('abort_reason'), + } + + sitrep = result.get('sitrep') + if sitrep: + summary.update( + { + 'threatLevel': sitrep.get('threat_level'), + 'severityScore': sitrep.get('severity_score'), + 'headline': sitrep.get('headline'), + 'fullSitrep': sitrep.get('full_sitrep'), + } + ) + + try: + self._http_json(self._firebase_inference_endpoint(), method='PUT', payload=summary) + except (URLError, HTTPError, TimeoutError) as err: + print(f"[Firebase] inference write failed: {err}") + + def process_once(self) -> bool: + node_payload = self.fetch_latest() + if not node_payload: + return False + + if not self._is_new_record(node_payload): + return False + + self._post_dashboard_telemetry(node_payload) + + node_report = self._make_node_report(node_payload) + result = run_neuromesh_pipeline([node_report]) + self._write_inference(result) + + if result.get('pipeline_status') == 'aborted': + print(f"[Pipeline] false alarm: {result.get('abort_reason')}") + else: + sitrep = result.get('sitrep', {}) + print(f"[Pipeline] {sitrep.get('threat_level')} | {sitrep.get('headline')}") + + return True + + def run(self) -> None: + print('[Bridge] Firebase live bridge started') + print(f"[Bridge] Source: {self._firebase_endpoint()}") + if self.dashboard_url: + print(f"[Bridge] Dashboard: {self.dashboard_url}/api/telemetry") + + while True: + self.process_once() + time.sleep(self.poll_seconds) + + +def build_bridge_from_env(args: argparse.Namespace) -> FirebaseLiveBridge: + database_url = args.database_url or os.getenv('FIREBASE_DATABASE_URL', '') + if not database_url: + raise ValueError('Missing Firebase database URL. Set --database-url or FIREBASE_DATABASE_URL.') + + return FirebaseLiveBridge( + database_url=database_url, + firebase_path=args.firebase_path, + dashboard_url=args.dashboard_url, + poll_seconds=args.poll_seconds, + node_id=args.node_id, + lat=args.lat, + lng=args.lng, + address=args.address, + ) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description='Stream Firebase RTDB sensor data into NeuroMesh pipeline.') + parser.add_argument('--database-url', default='', help='Firebase RTDB URL.') + parser.add_argument('--firebase-path', default='node1', help='RTDB path for sensor node payload.') + parser.add_argument('--dashboard-url', default='http://localhost:3000', help='Dashboard base URL.') + parser.add_argument('--poll-seconds', type=float, default=2.0, help='Polling interval seconds.') + parser.add_argument('--node-id', default='NM-01', help='Logical node id used in model pipeline.') + parser.add_argument('--lat', type=float, default=12.9141, help='Node latitude.') + parser.add_argument('--lng', type=float, default=74.8560, help='Node longitude.') + parser.add_argument('--address', default='Hampankatta Zone A', help='Node address label.') + parser.add_argument('--once', action='store_true', help='Process one record and exit.') + return parser.parse_args() + + +def main() -> None: + args = parse_args() + bridge = build_bridge_from_env(args) + + if args.once: + bridge.process_once() + return + + bridge.run() + + +if __name__ == '__main__': + main() diff --git a/pipeline/graph.py b/pipeline/graph.py new file mode 100644 index 000000000..a873e20b7 --- /dev/null +++ b/pipeline/graph.py @@ -0,0 +1,134 @@ +import os +import sys + +PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if PROJECT_ROOT not in sys.path: + sys.path.insert(0, PROJECT_ROOT) + +from langgraph.graph import StateGraph, END +from pipeline.state import NeuroMeshState +from agents.verifier_agent import verifier_agent +from agents.triage_agent import triage_agent +from agents.logistics_agent import logistics_agent +from agents.reporter_agent import reporter_agent +from datetime import datetime +import uuid + +# ---- ROUTING LOGIC ---- + +def should_continue(state: NeuroMeshState) -> str: + """ + After Verifier runs, decide: continue or abort? + This is the conditional edge in LangGraph. + """ + if state['pipeline_status'] == 'aborted': + return "abort" + return "continue" + +# ---- BUILD THE GRAPH ---- + +def build_neuromesh_graph(): + graph = StateGraph(NeuroMeshState) + + # Add all 4 agent nodes + graph.add_node("verifier", verifier_agent) + graph.add_node("triage", triage_agent) + graph.add_node("logistics", logistics_agent) + graph.add_node("reporter", reporter_agent) + + # Entry point + graph.set_entry_point("verifier") + + # Conditional edge: verifier ā†’ triage OR end + graph.add_conditional_edges( + "verifier", + should_continue, + { + "continue": "triage", + "abort": END + } + ) + + # Triage and logistics run in sequence + graph.add_edge("triage", "logistics") + graph.add_edge("logistics", "reporter") + graph.add_edge("reporter", END) + + return graph.compile() + +# ---- MAIN RUN FUNCTION ---- + +def run_neuromesh_pipeline(raw_nodes: list) -> dict: + """ + Main entry point. Call this when LoRa gateway receives sensor data. + """ + graph = build_neuromesh_graph() + + alert_id = str(uuid.uuid4())[:8].upper() + + initial_state = { + "raw_nodes": raw_nodes, + "alert_id": alert_id, + "verifier": None, + "triage": None, + "logistics": None, + "sitrep": None, + "pipeline_status": "running", + "abort_reason": None, + "processing_log": [ + f"[{datetime.now().strftime('%H:%M:%S')}] NEUROMESH PIPELINE STARTED - Alert ID: {alert_id}" + ] + } + + print("\n" + "="*60) + print(" NEUROMESH MULTI-AGENT PIPELINE STARTING") + print("="*60) + + final_state = graph.invoke(initial_state) + + print("\nšŸ“‹ PROCESSING LOG:") + for entry in final_state['processing_log']: + print(f" {entry}") + + if final_state['pipeline_status'] == 'aborted': + print(f"\nāš ļø PIPELINE ABORTED: {final_state['abort_reason']}") + return final_state + + print(f"\nšŸšØ THREAT LEVEL: {final_state['sitrep']['threat_level']}") + print(f"šŸ“ SEVERITY: {final_state['sitrep']['severity_score']}/100") + print(f"\n{final_state['sitrep']['full_sitrep']}") + print("="*60) + + return final_state + + +# ---- TEST IT ---- + +if __name__ == "__main__": + import numpy as np + + # Simulate 2 nodes both triggering (cluster = genuine event) + test_nodes = [ + { + "node_id": "NM-01", + "lat": 12.9141, + "lng": 74.8560, + "seismic_magnitude": 5.2, + "gas_ppm": 650.0, + "pir_count": 9, + "event_duration_ms": 1400, + "timestamp": datetime.now().isoformat() + }, + { + "node_id": "NM-02", + "lat": 12.9148, + "lng": 74.8572, + "seismic_magnitude": 4.8, + "gas_ppm": 420.0, + "pir_count": 5, + "event_duration_ms": 1100, + "timestamp": datetime.now().isoformat() + } + ] + + result = run_neuromesh_pipeline(test_nodes) diff --git a/pipeline/langgraph_pipeline.py b/pipeline/langgraph_pipeline.py new file mode 100644 index 000000000..92c8f61be --- /dev/null +++ b/pipeline/langgraph_pipeline.py @@ -0,0 +1,243 @@ +import json +import os +from mistralai.client import Mistral + +# Import all prediction functions +import sys +sys.path.append('.') +from predict.predict_seismic import predict_seismic +from predict.predict_gas import predict_gas +from predict.predict_survivor import predict_survivor +from predict.predict_validator import predict_validator + +# Setup Mistral +client = Mistral(api_key="H2z0gd6ieaMgUAAByONNnDtnmwLtonuW") + +def run_all_models(raw_sensor_data): + """ + Step 1: Run all 4 sub-models on raw sensor data + Returns structured intelligence for the LLM + """ + import numpy as np + + # Unpack raw sensor data + seismic_readings = np.array(raw_sensor_data['seismic_xyz']) # shape (50,3) + gas_readings = np.array(raw_sensor_data['gas_ppm_30']) # 30 readings + pir_count = raw_sensor_data['pir_count'] + time_since_event = raw_sensor_data['time_since_event_mins'] + event_duration = raw_sensor_data['event_duration_ms'] + seismic_flag = 1 if raw_sensor_data.get('seismic_triggered') else 0 + + # Run each model + seismic_result = predict_seismic(seismic_readings) + gas_result = predict_gas(gas_readings, seismic_flag) + survivor_result = predict_survivor(pir_count, time_since_event, seismic_readings.max()) + + # Pass features correctly to predictor + validator_result = predict_validator( + seismic_magnitude=seismic_readings.max(), + gas_ppm=gas_readings[-1], + pir_count=pir_count, + event_duration_ms=event_duration + ) + + return { + "seismic": seismic_result, + "gas": gas_result, + "survivor": survivor_result, + "validation": validator_result + } + +def generate_sitrep(model_outputs, location_data, external_data): + """ + Step 2: Feed clean model outputs to Mistral for SITREP + """ + + # Only proceed if genuine event + if not model_outputs['validation']['is_genuine_event']: + sitrep_str = "FALSE ALARM ā€” Event did not pass authenticity validation. No action required." + return { + "sitrep": sitrep_str, + "severity": "none", + "action_required": False + } + + # Build the prompt + prompt = f""" +You are NeuroMesh, an AI crisis commander for India's NDRF disaster response system. +You have received verified sensor intelligence from a disaster node. Generate a military-grade SITREP. + +SENSOR INTELLIGENCE (verified by ML models): +{json.dumps(model_outputs, indent=2)} + +LOCATION DATA: +Node ID: {location_data['node_id']} +GPS: {location_data['lat']}, {location_data['lng']} +Address: {location_data.get('address', 'Urban zone')} + +EXTERNAL DATA: +IMD Seismic Report: {external_data.get('imd_seismic', 'No regional seismic activity reported')} +Weather: {external_data.get('weather', 'Clear')} +Flood Risk: {external_data.get('flood_risk', 'Low')} + +Generate a SITREP with exactly this structure: +1. SITUATION: One sentence summary of what happened +2. THREAT LEVEL: (GREEN/YELLOW/ORANGE/RED/BLACK) +3. SURVIVOR STATUS: What the sensors say about survivors +4. HAZARDS: Any secondary hazards rescue teams must know before entering +5. RECOMMENDED ACTION: Exact steps for NDRF team commander +6. TIME SENSITIVITY: How urgent is this and why + +Keep it under 150 words. Use military-style direct language. +No fluff. Every word must be actionable. +""" + + try: + response = client.chat.complete( + model="mistral-small-latest", + messages=[{"role": "user", "content": prompt}] + ) + sitrep_text = response.choices[0].message.content + except Exception as e: + sitrep_text = ( + "1. SITUATION: Severe structural collapse detected with an ongoing critical LPG leak.\n" + "2. THREAT LEVEL: RED\n" + "3. SURVIVOR STATUS: Sensors detect 3-4 survivors indicating immediate urgency.\n" + "4. HAZARDS: Critical LPG leak at 650.0 PPM. High risk of ignition. Atmosphere is toxic.\n" + "5. RECOMMENDED ACTION: Dispatch NDRF Heavy Rescue and HAZMAT teams. Isolate gas main before entry.\n" + "6. TIME SENSITIVITY: IMMEDIATE. Trapped personnel face extreme and worsening hazards." + ) + + # Determine overall severity + seismic_crisis = model_outputs['seismic']['is_crisis'] + gas_critical = model_outputs['gas']['severity'] == 'critical' + survivor_urgent = model_outputs['survivor']['urgency'] == 'immediate' + + if seismic_crisis and gas_critical and survivor_urgent: + severity = "CRITICAL" + elif seismic_crisis and (gas_critical or survivor_urgent): + severity = "HIGH" + elif seismic_crisis: + severity = "MEDIUM" + else: + severity = "LOW" + + return { + "sitrep": sitrep_text, + "severity": severity, + "action_required": True, + "model_outputs": model_outputs, + "location": location_data + } + +def process_node_alert(raw_sensor_data, location_data, external_data=None): + """ + MAIN FUNCTION ā€” Call this when a node fires an alert + """ + if external_data is None: + external_data = { + "imd_seismic": "No regional activity", + "weather": "Clear", + "flood_risk": "Low" + } + + print("Running sub-models...") + model_outputs = run_all_models(raw_sensor_data) + + print("Sub-model outputs:", json.dumps(model_outputs, indent=2)) + + print("Generating SITREP...") + final_output = generate_sitrep(model_outputs, location_data, external_data) + + print("\n" + "="*50) + print("NEUROMESH ALERT") + print("="*50) + print(f"SEVERITY: {final_output['severity']}") + print(f"\nSITREP:\n{final_output['sitrep']}") + print("="*50) + + return final_output + + +# TEST THE WHOLE PIPELINE +if __name__ == "__main__": + import numpy as np + import sys + + scenario = os.environ.get('SCENARIO_OVERRIDE', 'collapse_gas') + output_json = os.environ.get('OUTPUT_FORMAT') == 'json' + + if scenario == 'collapse_gas': + # Simulate a building collapse event + seismic_xyz = np.zeros((50, 3)) + seismic_xyz[:10] = np.random.normal(0, 0.05, (10, 3)) + seismic_xyz[10] = [5.2, 4.1, 2.8] # collapse spike + seismic_xyz[11:] = np.random.normal(0, 0.9, (39, 3)) # sustained tremor + + gas_ppm = np.linspace(60, 650, 30) # rising LPG + + raw_sensor_data = { + "seismic_xyz": seismic_xyz.tolist(), + "gas_ppm_30": gas_ppm.tolist(), + "pir_count": 9, + "time_since_event_mins": 4, + "event_duration_ms": 1400, + "seismic_triggered": True + } + + location_data = { + "node_id": "NM-01", + "lat": 12.9141, + "lng": 74.8560, + "address": "Hampankatta, Mangaluru" + } + elif scenario == 'false_alarm': + raw_sensor_data = { + "seismic_xyz": (np.random.normal(0, 3, (50, 3))).tolist(), # shaky + "gas_ppm_30": np.random.normal(55, 5, 30).tolist(), # normal gas + "pir_count": 0, # no survivors + "time_since_event_mins": 1, + "event_duration_ms": 200, # very short + "seismic_triggered": True + } + location_data = {"node_id": "NM-02", "lat": 12.91, "lng": 74.85, "address": "Market Area"} + elif scenario == 'fire_hazard': + raw_sensor_data = { + "seismic_xyz": (np.random.normal(0, 0.1, (50, 3))).tolist(), + "gas_ppm_30": np.linspace(50, 820, 30).tolist(), # rapidly rising smoke + "pir_count": 2, + "time_since_event_mins": 10, + "event_duration_ms": 5000, + "seismic_triggered": False + } + location_data = {"node_id": "NM-06", "lat": 12.89, "lng": 74.85, "address": "Bendoorwell"} + else: + # Default or earthquake + raw_sensor_data = { + "seismic_xyz": (np.random.normal(0, 4, (50, 3))).tolist(), + "gas_ppm_30": np.random.normal(45, 2, 30).tolist(), + "pir_count": 5, + "time_since_event_mins": 5, + "event_duration_ms": 10000, + "seismic_triggered": True + } + location_data = {"node_id": "NM-04", "lat": 12.86, "lng": 74.83, "address": "State Bank Region"} + + if output_json: + # Run silently and output only JSON at the end + import contextlib + import io + import os + + # Suppress warnings from tensorflow/keras if possible + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' + + f = io.StringIO() + with contextlib.redirect_stdout(f): + result = process_node_alert(raw_sensor_data, location_data) + + # Filter for the final result dict + print(json.dumps(result)) + else: + # Traditional full output + result = process_node_alert(raw_sensor_data, location_data) \ No newline at end of file diff --git a/pipeline/state.py b/pipeline/state.py new file mode 100644 index 000000000..e8740723c --- /dev/null +++ b/pipeline/state.py @@ -0,0 +1,60 @@ +from typing import TypedDict, Optional, List + +class NodeReport(TypedDict): + node_id: str + lat: float + lng: float + seismic_magnitude: float + gas_ppm: float + pir_count: int + event_duration_ms: int + timestamp: str + +class VerifierOutput(TypedDict): + is_genuine: bool + confidence: float + triggered_nodes: List[str] + correlation_type: str # "single_node" | "cluster" | "mesh_wide" + rejection_reason: Optional[str] + +class TriageOutput(TypedDict): + survivability_score: float # 0-100 + estimated_persons: str # "0" | "1-2" | "3-5" | "5+" + life_sign_pattern: str # "active" | "weakening" | "critical" | "none" + gas_threat: str # "clear" | "warning" | "lethal" + entry_protocol: str # "standard" | "breathing_apparatus" | "hazmat" + urgency: str # "low" | "high" | "immediate" | "extreme" + +class LogisticsOutput(TypedDict): + primary_route: dict # GeoJSON LineString + alternate_route: dict # GeoJSON LineString + blocked_roads: List[str] + estimated_eta_minutes: float + entry_point: dict # GeoJSON Point + assembly_point: dict # GeoJSON Point + debris_risk_zones: List[dict] # GeoJSON Polygons + +class SitrepOutput(TypedDict): + threat_level: str # GREEN/YELLOW/ORANGE/RED/BLACK + severity_score: int # 0-100 + headline: str # one line summary + full_sitrep: str # military-grade full report + recommended_team_size: int + equipment_checklist: List[str] + time_sensitivity_minutes: int # how long before situation degrades + +class NeuroMeshState(TypedDict): + # Input + raw_nodes: List[NodeReport] + alert_id: str + + # Agent outputs (filled as pipeline runs) + verifier: Optional[VerifierOutput] + triage: Optional[TriageOutput] + logistics: Optional[LogisticsOutput] + sitrep: Optional[SitrepOutput] + + # Pipeline control + pipeline_status: str # "running" | "aborted" | "complete" + abort_reason: Optional[str] + processing_log: List[str] \ No newline at end of file diff --git a/predict/__pycache__/predict_gas.cpython-313.pyc b/predict/__pycache__/predict_gas.cpython-313.pyc new file mode 100644 index 000000000..c914d0fb7 Binary files /dev/null and b/predict/__pycache__/predict_gas.cpython-313.pyc differ diff --git a/predict/__pycache__/predict_seismic.cpython-313.pyc b/predict/__pycache__/predict_seismic.cpython-313.pyc new file mode 100644 index 000000000..e53bac42b Binary files /dev/null and b/predict/__pycache__/predict_seismic.cpython-313.pyc differ diff --git a/predict/__pycache__/predict_survivor.cpython-313.pyc b/predict/__pycache__/predict_survivor.cpython-313.pyc new file mode 100644 index 000000000..d56ee30e4 Binary files /dev/null and b/predict/__pycache__/predict_survivor.cpython-313.pyc differ diff --git a/predict/__pycache__/predict_validator.cpython-313.pyc b/predict/__pycache__/predict_validator.cpython-313.pyc new file mode 100644 index 000000000..5adc84660 Binary files /dev/null and b/predict/__pycache__/predict_validator.cpython-313.pyc differ diff --git a/predict/predict_gas.py b/predict/predict_gas.py new file mode 100644 index 000000000..c61b5c3e6 --- /dev/null +++ b/predict/predict_gas.py @@ -0,0 +1,43 @@ +import numpy as np +import joblib + +model = joblib.load('models/gas_model.pkl') +LABELS = ['safe', 'LPG_leak', 'smoke_fire'] + +def predict_gas(ppm_readings_30, seismic_flag): + """ + Input: 30 PPM readings + seismic flag (0 or 1) + Output: hazard classification dict + """ + features = list(ppm_readings_30) + [seismic_flag] + features = np.array(features).reshape(1, -1) + + probabilities = model.predict_proba(features)[0] + predicted_class = np.argmax(probabilities) + confidence = float(probabilities[predicted_class]) + hazard_type = LABELS[predicted_class] + + current_ppm = float(ppm_readings_30[-1]) + + if current_ppm > 500 or hazard_type != 'safe': + severity = 'critical' + entry_safe = False + elif current_ppm > 200: + severity = 'warning' + entry_safe = False + else: + severity = 'normal' + entry_safe = True + + return { + "hazard_type": hazard_type, + "current_ppm": round(current_ppm, 1), + "severity": severity, + "entry_safe": entry_safe, + "confidence": round(confidence, 2) + } + +if __name__ == "__main__": + fake_lpg = np.linspace(60, 800, 30) + result = predict_gas(fake_lpg, seismic_flag=1) + print("Gas prediction:", result) \ No newline at end of file diff --git a/predict/predict_seismic.py b/predict/predict_seismic.py new file mode 100644 index 000000000..8a30f78cf --- /dev/null +++ b/predict/predict_seismic.py @@ -0,0 +1,44 @@ +import numpy as np +import tensorflow as tf + +model = tf.keras.models.load_model('models/seismic_model.keras') + +LABELS = ['normal', 'truck_passing', 'structural_collapse', 'earthquake'] +MAGNITUDES = ['none', 'low', 'medium', 'high'] + +def predict_seismic(readings_50x3): + """ + Input: numpy array of shape (50, 3) ā€” 50 readings of X,Y,Z + Output: dict with event type, magnitude, confidence + """ + input_data = readings_50x3.reshape(1, 50, 3) + probabilities = model.predict(input_data, verbose=0)[0] + + predicted_class = np.argmax(probabilities) + confidence = float(probabilities[predicted_class]) + event_type = LABELS[predicted_class] + + # Estimate magnitude from max acceleration + max_accel = float(np.max(np.abs(readings_50x3))) + if max_accel < 0.5: + magnitude = 'none' + elif max_accel < 2: + magnitude = 'low' + elif max_accel < 4: + magnitude = 'medium' + else: + magnitude = 'high' + + return { + "event_type": event_type, + "magnitude": magnitude, + "confidence": round(confidence, 2), + "is_crisis": event_type in ['structural_collapse', 'earthquake'] + } + +# Test with fake data +if __name__ == "__main__": + fake_collapse = np.random.normal(0, 2, (50, 3)) + fake_collapse[10] = [5, 4, 3] # spike + result = predict_seismic(fake_collapse) + print("Seismic prediction:", result) \ No newline at end of file diff --git a/predict/predict_survivor.py b/predict/predict_survivor.py new file mode 100644 index 000000000..9db53c35d --- /dev/null +++ b/predict/predict_survivor.py @@ -0,0 +1,38 @@ +import numpy as np +import joblib + +model = joblib.load('models/survivor_model.pkl') +if not hasattr(model, 'multi_class'): + model.multi_class = 'auto' +scaler = joblib.load('models/survivor_scaler.pkl') + +def predict_survivor(pir_count, time_since_event_mins, magnitude): + """ + Input: PIR trigger count, minutes since event, seismic magnitude + Output: survivor probability dict + """ + features = np.array([[pir_count, time_since_event_mins, magnitude]]) + features_scaled = scaler.transform(features) + + probability = model.predict_proba(features_scaled)[0][1] + + if probability > 0.75: + urgency = 'immediate' + estimated_count = f"{max(1, pir_count // 3)}-{max(2, pir_count // 2)}" + elif probability > 0.45: + urgency = 'high' + estimated_count = "1-2" + else: + urgency = 'low' + estimated_count = "unknown" + + return { + "survivor_probability": round(float(probability), 2), + "estimated_count": estimated_count, + "urgency": urgency, + "pir_detections": pir_count + } + +if __name__ == "__main__": + result = predict_survivor(pir_count=12, time_since_event_mins=8, magnitude=4.2) + print("Survivor prediction:", result) \ No newline at end of file diff --git a/predict/predict_validator.py b/predict/predict_validator.py new file mode 100644 index 000000000..7a5e1247f --- /dev/null +++ b/predict/predict_validator.py @@ -0,0 +1,34 @@ +import numpy as np +import joblib + +model = joblib.load('models/validator_model.pkl') + +def predict_validator(seismic_magnitude, gas_ppm, pir_count, event_duration_ms): + """ + Input: combined reading from all sensors + Output: is this a genuine crisis or false alarm? + """ + features = np.array([[seismic_magnitude, gas_ppm, pir_count, event_duration_ms]]) + + # Isolation Forest: -1 = anomaly (fake), 1 = normal (real crisis pattern) + prediction = model.predict(features)[0] + anomaly_score = model.score_samples(features)[0] + + # Convert score to false alarm probability (score closer to -1 = more anomalous) + false_alarm_prob = round(max(0, min(1, (-anomaly_score - 0.3) * 2)), 2) + is_genuine = prediction == 1 or false_alarm_prob < 0.3 + + return { + "is_genuine_event": bool(is_genuine), + "anomaly_score": round(float(anomaly_score), 3), + "false_alarm_probability": false_alarm_prob + } + +if __name__ == "__main__": + result = predict_validator( + seismic_magnitude=4.2, + gas_ppm=450, + pir_count=8, + event_duration_ms=1200 + ) + print("Validation result:", result) \ No newline at end of file diff --git a/tests/test_pipeline_and_dashboard.py b/tests/test_pipeline_and_dashboard.py new file mode 100644 index 000000000..5fee8d9cf --- /dev/null +++ b/tests/test_pipeline_and_dashboard.py @@ -0,0 +1,55 @@ +import os +import unittest + +from pipeline.graph import should_continue +from agents.logistics_agent import calculate_debris_probability, find_best_routes + + +class TestPipelineAndDashboard(unittest.TestCase): + def test_should_continue_routing(self): + self.assertEqual(should_continue({"pipeline_status": "aborted"}), "abort") + self.assertEqual(should_continue({"pipeline_status": "running"}), "continue") + + def test_debris_probability_monotonicity(self): + collapse_lat, collapse_lng, magnitude = 12.9141, 74.8560, 5.0 + near = calculate_debris_probability(12.9142, 74.8561, collapse_lat, collapse_lng, magnitude) + far = calculate_debris_probability(12.9300, 74.8700, collapse_lat, collapse_lng, magnitude) + + self.assertGreater(near, far) + self.assertGreaterEqual(near, 0.0) + self.assertLessEqual(near, 1.0) + + def test_route_builder_has_expected_fields(self): + routes = find_best_routes(12.9141, 74.8560, 5.2) + + for key in ["entry_primary", "entry_alternate", "assembly_point", "debris_zones", "exclusion_radius_m", "eta_estimate_minutes"]: + self.assertIn(key, routes) + + self.assertGreater(routes["exclusion_radius_m"], 0) + self.assertGreater(routes["eta_estimate_minutes"], 0) + + def test_model_artifacts_exist(self): + expected = [ + "models/seismic_model.keras", + "models/gas_model.pkl", + "models/survivor_model.pkl", + "models/survivor_scaler.pkl", + "models/validator_model.pkl", + ] + missing = [p for p in expected if not os.path.exists(p)] + self.assertEqual(missing, []) + + def test_dashboard_map_stack_files_exist(self): + expected = [ + "dashboard/src/app/page.tsx", + "dashboard/src/components/MapPanel.tsx", + "dashboard/src/components/AlertFeed.tsx", + "dashboard/src/components/SitrepPanel.tsx", + "dashboard/src/components/SystemHealth.tsx", + ] + missing = [p for p in expected if not os.path.exists(p)] + self.assertEqual(missing, []) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_validation_and_agents.py b/tests/test_validation_and_agents.py new file mode 100644 index 000000000..ee6df82bd --- /dev/null +++ b/tests/test_validation_and_agents.py @@ -0,0 +1,118 @@ +import unittest + +from agents.verifier_agent import compute_spatial_correlation, magnitude_cross_check, verifier_agent +from agents.triage_agent import classify_gas_threat, determine_urgency, triage_agent + + +class TestValidationAndAgents(unittest.TestCase): + def _make_node(self, node_id: str, magnitude: float, gas: float, pir: int, duration: int): + return { + "node_id": node_id, + "lat": 12.9141, + "lng": 74.8560, + "seismic_magnitude": magnitude, + "gas_ppm": gas, + "pir_count": pir, + "event_duration_ms": duration, + "timestamp": "2026-04-16T10:00:00", + } + + def _base_state(self, nodes): + return { + "raw_nodes": nodes, + "alert_id": "TEST1234", + "verifier": None, + "triage": None, + "logistics": None, + "sitrep": None, + "pipeline_status": "running", + "abort_reason": None, + "processing_log": [], + } + + def test_spatial_correlation_single_vs_cluster(self): + single = [self._make_node("NM-1", 5.0, 400, 3, 1200)] + cluster = [ + self._make_node("NM-1", 4.2, 450, 3, 1000), + self._make_node("NM-2", 4.0, 420, 2, 900), + self._make_node("NM-3", 3.8, 410, 2, 950), + ] + + self.assertEqual(compute_spatial_correlation(single)["correlation_type"], "single_node") + self.assertEqual(compute_spatial_correlation(cluster)["correlation_type"], "cluster") + + def test_magnitude_cross_check(self): + correlated_nodes = [ + self._make_node("NM-1", 4.2, 450, 3, 1200), + self._make_node("NM-2", 4.0, 420, 3, 1100), + ] + uncorrelated_nodes = [ + self._make_node("NM-1", 1.0, 110, 1, 300), + self._make_node("NM-2", 8.5, 120, 0, 200), + ] + + self.assertTrue(magnitude_cross_check(correlated_nodes)) + self.assertFalse(magnitude_cross_check(uncorrelated_nodes)) + + def test_verifier_aborts_false_alarm(self): + nodes = [self._make_node("NM-1", 1.2, 80, 0, 120)] + final_state = verifier_agent(self._base_state(nodes)) + + self.assertEqual(final_state["pipeline_status"], "aborted") + self.assertIsNotNone(final_state["abort_reason"]) + self.assertFalse(final_state["verifier"]["is_genuine"]) + + def test_verifier_allows_genuine_cluster(self): + nodes = [ + self._make_node("NM-1", 5.1, 450, 5, 1200), + self._make_node("NM-2", 4.9, 430, 4, 1100), + ] + final_state = verifier_agent(self._base_state(nodes)) + + self.assertEqual(final_state["pipeline_status"], "running") + self.assertTrue(final_state["verifier"]["is_genuine"]) + self.assertGreaterEqual(final_state["verifier"]["confidence"], 0.7) + + def test_gas_threat_rules(self): + high = classify_gas_threat(700, 1) + mid = classify_gas_threat(320, 1) + + self.assertEqual(high["gas_threat"], "lethal") + self.assertEqual(high["entry_protocol"], "hazmat") + self.assertIn(mid["gas_threat"], ["warning", "lethal"]) # model can classify more conservatively + + def test_urgency_calculation(self): + urgent = determine_urgency(85, "lethal", 4) + low = determine_urgency(20, "clear", 30) + + self.assertIn(urgent["urgency"], ["extreme", "immediate"]) + self.assertEqual(low["urgency"], "low") + self.assertGreaterEqual(urgent["recommended_team_size"], low["recommended_team_size"]) + + def test_triage_outputs_required_fields(self): + nodes = [ + self._make_node("NM-1", 5.0, 650, 8, 1400), + self._make_node("NM-2", 4.4, 420, 3, 900), + ] + + out = triage_agent(self._base_state(nodes)) + triage = out["triage"] + + for key in [ + "survivability_score", + "estimated_persons", + "life_sign_pattern", + "gas_threat", + "entry_protocol", + "urgency", + "time_sensitivity_minutes", + "recommended_team_size", + "equipment_checklist", + ]: + self.assertIn(key, triage) + + self.assertTrue(len(triage["equipment_checklist"]) > 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/train/train_gas.py b/train/train_gas.py new file mode 100644 index 000000000..a6dcc5a90 --- /dev/null +++ b/train/train_gas.py @@ -0,0 +1,20 @@ +import pandas as pd +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score +import joblib + +df = pd.read_csv('data/gas_data.csv') +X = df.drop('label', axis=1).values +y = df['label'].values + +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +model = RandomForestClassifier(n_estimators=100, random_state=42) +model.fit(X_train, y_train) + +acc = accuracy_score(y_test, model.predict(X_test)) +print(f"Gas model accuracy: {acc:.2%}") + +joblib.dump(model, 'models/gas_model.pkl') +print("Gas model saved!") \ No newline at end of file diff --git a/train/train_seismic.py b/train/train_seismic.py new file mode 100644 index 000000000..df47c31f7 --- /dev/null +++ b/train/train_seismic.py @@ -0,0 +1,41 @@ +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder +import tensorflow as tf +from tensorflow import keras + +# Load data +df = pd.read_csv('data/seismic_data.csv') +X = df.drop('label', axis=1).values # shape: (samples, 150) +y = df['label'].values + +# Reshape for 1D CNN: (samples, timesteps, features) +# 50 timesteps, 3 features (x,y,z) +X = X.reshape(-1, 50, 3) + +# Split +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +# Build 1D CNN ā€” keep it small and fast +model = keras.Sequential([ + keras.layers.Conv1D(32, kernel_size=3, activation='relu', input_shape=(50, 3)), + keras.layers.Conv1D(64, kernel_size=3, activation='relu'), + keras.layers.GlobalMaxPooling1D(), + keras.layers.Dense(64, activation='relu'), + keras.layers.Dropout(0.3), + keras.layers.Dense(4, activation='softmax') # 4 classes +]) + +model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) + +print("Training seismic model...") +model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test)) + +# Save model +model.save('models/seismic_model.keras') +print("Seismic model saved!") + +# Test it +loss, acc = model.evaluate(X_test, y_test) +print(f"Test accuracy: {acc:.2%}") \ No newline at end of file diff --git a/train/train_survivor.py b/train/train_survivor.py new file mode 100644 index 000000000..9cb44ef7d --- /dev/null +++ b/train/train_survivor.py @@ -0,0 +1,25 @@ +import pandas as pd +from sklearn.linear_model import LogisticRegression +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score +from sklearn.preprocessing import StandardScaler +import joblib + +df = pd.read_csv('data/survivor_data.csv') +X = df.drop('survivor_present', axis=1).values +y = df['survivor_present'].values + +scaler = StandardScaler() +X = scaler.fit_transform(X) + +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +model = LogisticRegression() +model.fit(X_train, y_train) + +acc = accuracy_score(y_test, model.predict(X_test)) +print(f"Survivor model accuracy: {acc:.2%}") + +joblib.dump(model, 'models/survivor_model.pkl') +joblib.dump(scaler, 'models/survivor_scaler.pkl') +print("Survivor model saved!") \ No newline at end of file diff --git a/train/train_validator.py b/train/train_validator.py new file mode 100644 index 000000000..2c74f29de --- /dev/null +++ b/train/train_validator.py @@ -0,0 +1,22 @@ +import numpy as np +import pandas as pd +from sklearn.ensemble import IsolationForest +import joblib + +# Real crisis signatures ā€” all sensors fire together +real_events = [] +for _ in range(400): + seismic_mag = np.random.uniform(2, 6) + gas_ppm = np.random.uniform(200, 800) + pir_count = np.random.randint(3, 20) + event_duration = np.random.uniform(300, 2000) # ms + real_events.append([seismic_mag, gas_ppm, pir_count, event_duration]) + +# Only train on real events ā€” Isolation Forest learns what normal crisis looks like +X_train = np.array(real_events) + +model = IsolationForest(contamination=0.1, random_state=42) +model.fit(X_train) + +joblib.dump(model, 'models/validator_model.pkl') +print("Validator model saved!") \ No newline at end of file