Update README.md

Author: agrimgarg08

README.md

@@ -1,136 +1,76 @@
-# drishti
-AI-Driven Sustainability Operating System for Urban Water Systems
+# DRISHTI  
+## AI-Driven Sustainability Operating System for Urban Water Systems (particularly the Yamuna)
 
-## Quickstart (Local)
+Delhi contributes a majority of the Yamuna’s pollution despite covering only a small stretch of the river.  
+Current interventions are reactive, fragmented, and lack continuous monitoring.
 
-1. Create a virtualenv and install dependencies:
+**DRISHTI** is a lightweight, real-time decision support platform that helps authorities detect pollution early, predict risks, and take targeted action.
 
-```bash
-python -m venv .venv
-.\.venv\Scripts\activate
-pip install -r requirements.txt
-```
-
-2. Seed a few sensors and create the DB:
-
-```bash
-python -m scripts.init_db
-```
-
-3. Start the FastAPI backend:
-
-```bash
-uvicorn backend.main:app --reload
-```
-
-4. (Optional) Run the data generator to simulate sensor readings:
-
-```bash
-python scripts/generate_data.py --url http://localhost:8000 --sensors 5 --count 200 --delay 0.3
-```
-
-5. Start the Streamlit frontend:
-
-```bash
-streamlit run frontend/app.py
-```
-
-6. Visit http://localhost:8501 for the dashboard. The backend runs on http://localhost:8000.
+Instead of waiting for damage, we enable a **monitor → predict → intervene** strategy.
 
 ---
 
-This repository is a hackathon-friendly prototype. It uses a lightweight SQLite DB by default but can be pointed to a Supabase/Postgres instance by setting the `DATABASE_URL` environment variable.
-
-## Supabase Auth & DB (recommended for deployment) ✅
-
-- Create a free Supabase project at https://app.supabase.com and note the **Project URL** and **anon/public key** (Project -> Settings -> API).
-- Enable Email auth in Supabase (Auth -> Settings).
-- Set environment variables locally or in deployment:
-  - `DATABASE_URL` (Postgres connection string from Supabase)
-  - `SUPABASE_URL` (your project URL, e.g. https://xyz.supabase.co)
-  - `SUPABASE_KEY` (anon/public key for client-side auth calls)
-  - (Optional) `SUPABASE_SERVICE_ROLE_KEY` for any server-side admin tasks — keep secret.
-
-### Applying the DB schema
-
-A SQL migration is included at `migrations/initial.sql` and a small runner `scripts/apply_migrations.py` uses your `DATABASE_URL` to apply it.
-
-Example (local):
+## 🚨 Problem
 
-```bash
-set DATABASE_URL=postgresql://user:pass@host:5432/dbname   # Windows (PowerShell: $env:DATABASE_URL=...)
-python scripts/apply_migrations.py
-```
+- Untreated sewage enters through multiple drains
+- Monitoring is sparse and manual
+- Illegal/ intermittent dumping goes unnoticed
+- STPs are overloaded or bypassed
+- Agencies lack coordinated, real-time data
 
-If you prefer the Supabase UI, you can paste `migrations/initial.sql` into the SQL editor and run it there.
+Result:  
+By the time pollution is noticed, it’s already too late.
 
-### Frontend & backend env vars
-
-- Frontend (Streamlit Cloud): set `API_BASE`, `SUPABASE_URL`, `SUPABASE_KEY`.
-- Backend (Railway/Render): set `DATABASE_URL` and optionally `SUPABASE_SERVICE_ROLE_KEY`.
-
-### Security notes
-
-- Do not commit service-role keys to source control.
-- Use Supabase anon key only in client; use service role key or DB connection string only for the server.
-
-
-## Tests
-
-Run quick smoke tests locally (no server needed):
-
-```bash
-python -m scripts.test_api
-python -m scripts.test_ml
-```
-
-These use FastAPI's TestClient and simple synthetic data to verify core endpoints and ML functions.
-
-## Dashboard (Streamlit)
-
-The Streamlit app shows an interactive sensor map and details:
-- Hover sensors on the map to view latest readings, `last_service`, and unresolved alert count.
-- Select a sensor to see metrics and time-series charts.
-- Use "Refresh sensor data" in the sidebar to clear the cache and refetch from Supabase.
-
-To run locally:
+---
 
-```bash
-streamlit run frontend/app.py
-```
+## 💡 Our Solution
 
-When deployed to Streamlit Cloud, set `SUPABASE_URL` and `SUPABASE_KEY` in the app Secrets.
+We built a **smart environmental monitoring system** that:
 
-## Deploy (short)
+- Continuously collects sensor data
+- Detects anomalies automatically
+- Simulates pollution risk
+- Tracks issues
+- Visualizes everything on one dashboard
 
-- Backend: Railway or Render — point `start` to `uvicorn backend.main:app --host 0.0.0.0 --port $PORT` and set `DATABASE_URL` as secret.
-- Frontend: Streamlit Cloud — push repo, set `API_BASE` to your backend and `SUPABASE_*` env vars in the UI.
+It acts as the **control center for river health**.
 
 ---
 
-# Streamlit Cloud Deployment
+## 🧠 System Flow
+
+Sensors → Database → AI/ML → Backend API → Dashboard → Action
 
-1. Push the repo to GitHub.
-2. On streamlit.io/cloud, create a new app from this repo.
-3. In the app settings > Secrets, add:
-   - SUPABASE_URL
-   - SUPABASE_KEY
-4. Deploy. Use the sidebar to enter a table name and view/insert rows.
+- Sensors send water quality data
+- ML detects abnormal spikes
+- Alerts are generated automatically
+- Officials raise issues and take action
+- Policies can be simulated before deployment
 
 ---
 
-# Quick local setup
+## ✨ Features
 
-## Unix
+### 📊 Real-Time Monitoring
+- Live sensor readings
+- Time-series charts
+- Water quality metrics (pH, DO, BOD, COD, turbidity, ammonia, temperature, conductivity)
 
-1. ./setup_venv.sh
-2. source .venv/bin/activate
-3. streamlit run streamlit_app.py
+### 🚨 Intelligent Alerts
+- Automatic anomaly detection
+- Severity-based warnings
+- Location-specific flags
 
-## Windows
+### 🔮 Prediction / Simulation Engine
+- Short-term pollution forecasting
+- Risk scoring for drains/segments
 
-1. setup_venv.bat
-2. .venv\Scripts\activate
-3. streamlit run streamlit_app.py
+### 📝 Issue Tracker
+- Raise issues
+- Track resolution status
 
-Before deploying to Streamlit Cloud, add SUPABASE_URL and SUPABASE_KEY in the app Secrets.
+### 🗺 Interactive Dashboard
+- Sensor map
+- Alerts table
+- Charts & analytics
+- Authentication