diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..090149439 --- /dev/null +++ b/.gitignore @@ -0,0 +1,31 @@ +# OS files +.DS_Store +Thumbs.db + +# Environments +venv/ +env/ +.env +.env.local + +# Python +__pycache__/ +*.py[cod] +*$py.class + +# Node +node_modules/ +.next/ +out/ +build/ +dist/ + +# Scratch & Logs +dashboard/scratch/ +scratch/ +*.log + +# Models (optional, but often large) +# models/*.keras (keeping if user wants them in git) +# models/*.keras.bak +*.bak diff --git a/README.md b/README.md index c5c886b3e..59382669b 100644 --- a/README.md +++ b/README.md @@ -1,86 +1,111 @@ -# HackToFuture 4.0 — Template +
+ + + +

🌐 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. +## 🚨 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 - ---- +## 💡 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: +## 🚀 Features -- What are you building? -- How does it solve the problem? -- What makes your solution unique? +- 🟢 **Live Telemetry Dashboard:** An interactive Web GIS UI plotting mesh nodes and live status. +- 🧠 **Multi-Model Inference:** + - Seismic Activity Classification (Keras/TensorFlow) + - Hazardous Gas Prediction (Scikit-Learn Random Forest) + - Survivor Count Estimation (Scikit-Learn) +- 🛡️ **Crisis Validator (Isolation Forest):** Identifies erratic sensor behavior or false positives. +- ⚡ **Automated LLM SitReps:** LangGraph orchestration to output clear "Recommended Actions" and "Threat Levels". +- 📡 **Simulated LoRaWAN Network:** Fallback communication visualization for unconnected zones. --- -## Features +## 🛠️ 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 +## ⚙️ Project Setup Instructions Provide clear steps to run your project: ```bash -# Clone the repository -git clone - -# Install dependencies -... - -# Run the project -... +# 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 ``` + +
+
+ Built with ❤️ for HackToFuture 4 +
\ 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 — 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/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..6451cf819 --- /dev/null +++ b/dashboard/next.config.ts @@ -0,0 +1,8 @@ +import type { NextConfig } from "next"; +import path from "path"; + +const nextConfig: NextConfig = { + /* config options here */ +}; + +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" + }, + "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": 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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/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..93072f215 --- /dev/null +++ b/dashboard/src/app/layout.tsx @@ -0,0 +1,34 @@ +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..8b686209a --- /dev/null +++ b/dashboard/src/app/page.tsx @@ -0,0 +1,9 @@ +import SimulationDashboard from '@/components/SimulationDashboard'; + +export default function Home() { + return ( +
+ +
+ ); +} diff --git a/dashboard/src/components/MapComponent.tsx b/dashboard/src/components/MapComponent.tsx new file mode 100644 index 000000000..62a5d9b06 --- /dev/null +++ b/dashboard/src/components/MapComponent.tsx @@ -0,0 +1,81 @@ +'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 +// Custom high-tech marker function +const createCustomIcon = (isActive: boolean) => { + return L.divIcon({ + className: 'custom-div-icon', + html: ` +
+
+
+
+ `, + iconSize: [32, 32], + iconAnchor: [16, 16], + }); +}; + +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/SimulationDashboard.tsx b/dashboard/src/components/SimulationDashboard.tsx new file mode 100644 index 000000000..b24a3575e --- /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 – 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 – 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 – 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 — 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 || "Unknown", + magnitude: parsed.model_outputs.seismic?.is_crisis ? 'high' : 'low' + }, + gas: { + hazard_type: parsed.model_outputs.gas?.prediction || "Scanning", + severity: parsed.model_outputs.gas?.severity || "Normal", + current_ppm: parsed.model_outputs.gas?.ppm || 0 + }, + survivor: { + estimated_count: parsed.model_outputs.survivor?.estimated_count || "Unknown", + urgency: parsed.model_outputs.survivor?.urgency || "Unknown" + }, + 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/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 @@ 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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 — 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 — 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 — 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 — 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 — 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 — 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 — 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 — 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..d06147ec7 Binary files /dev/null and b/models/validator_model.pkl differ 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 — 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 — 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/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) — 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/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 — 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 — 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 — 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