DEV Community: Rohith

Building an AI-Powered Carbon Footprint Awareness Platform with Flask, SQLite, and Groq (Llama 3.1)

Rohith — Fri, 19 Jun 2026 06:42:04 +0000

🌿 Introduction

As climate awareness grows, individuals are looking for actionable ways to reduce their personal carbon footprints. However, most carbon calculators are either too complex or offer generic, unhelpful advice.

To solve this, I built CarbonWise—a production-ready Carbon Footprint Awareness Platform. It combines deterministic scientific carbon calculations with real-time, personalized AI reduction strategies using the Groq LLM API.

Here is a technical deep-dive into how I built, secured, and optimized this application for the PromptWars: Virtual challenge.

🏗️ Architecture & System Design

The application is designed to be lightweight, secure, and highly performant, avoiding heavy framework overhead.

System Data Flow

  ┌──────────────────────────────────────────────────────────┐
  │                       User Browser                       │
  └─────────────┬──────────────────────────────▲─────────────┘
                │ HTTPS (POST / GET)           │ Rendered HTML/CSS
  ┌─────────────▼──────────────────────────────┴─────────────┐
  │                   Flask App (app.py)                     │
  └─────────────┬──────────────┬───────────────▲─────────────┘
                │              │               │
  ┌─────────────▼──────────┐ ┌─▼─────────────┐ │
  │ SQLite DB (carbon.db)  │ │Secure Session │ │
  │   - Users & Logs       │ │  Cookies      │ │
  │   - WAL Mode Enabled   │ └───────────────┘ │
  └────────────────────────┘                   │ Structured JSON Insights
  ┌────────────────────────────────────────────┴─────────────┐
  │                 Groq API (Llama 3.1)                     │
  │     - Model: llama-3.1-8b-instant                        │
  └──────────────────────────────────────────────────────────┘

Backend: Flask (Python) handles routing, user session state, and database operations.
Database: SQLite manages users and logs. We activated WAL (Write-Ahead Logging) mode to enable concurrent reads and writes.
AI Engine: Connects to the Groq API using the ultra-fast Meta Llama 3.1 8B model (llama-3.1-8b-instant).
Frontend: Rendered server-side with Jinja2 templates and styled with a custom dark-mode glassmorphism design system in Vanilla CSS.

⚙️ Feature Deep-Dive

1. Deterministic Carbon Calculations (`carbon_engine.py`)

To prevent calculation drift, the engine uses fixed conversion factors for logging three main categories:

Travel: Car (0.21 kg/km), Motorbike (0.05 kg/km), Bus (0.08 kg/km), and Flight (0.255 kg/km).
Food: Vegetarian meal (0.5 kg/meal) and Non-vegetarian meal (2.5 kg/meal).
Energy: Electricity (0.82 kg/kWh).

def calculate_co2(activity_type: str, quantity: float) -> float:
    if activity_type not in EMISSION_FACTORS:
        raise ValueError(f"Unknown activity type '{activity_type}'")
    if quantity <= 0:
        raise ValueError("Quantity must be positive")
    return round(EMISSION_FACTORS[activity_type] * quantity, 4)

2. Structured AI Insights & Session Caching (`ai_engine.py`)

To keep the dashboard fast and prevent redundant external HTTP calls, I implemented a session caching mechanism:

When a user loads the dashboard, the platform checks if AI insights are cached in the session.
If missing, it requests insights from the Groq API.
The system enforces a strict JSON schema output from the model: summary, top_emission_sources, suggestions (3–5 tips), eco_score (0–100), and motivation.
When the user logs a new activity in /log, the cache is invalidated so the dashboard gets fresh insights on the next load.

# Check session cache
insights = session.get("ai_insights")
if not insights:
    all_acts = _get_all_activities_7days(user_id)
    insights = get_ai_insights(all_acts, daily_total, category_breakdown)
    session["ai_insights"] = insights

3. Database Performance Tuning (Covering Indexes)

To make sure data queries are incredibly fast, I optimized the database schema by introducing a covering index:

CREATE INDEX IF NOT EXISTS idx_activities_covering 
ON activities(user_id, timestamp, activity_type, co2_kg);

With this index in place, queries for the weekly analytics dashboard and category breakdowns are satisfied directly from index pages, bypassing expensive full-table scans. We also optimized category queries to aggregate in a single SQL operation:

SELECT 
    CASE 
        WHEN activity_type IN ('car', 'bike', 'bus', 'flight') THEN 'travel'
        WHEN activity_type IN ('veg', 'non_veg') THEN 'food'
        WHEN activity_type IN ('electricity') THEN 'electricity'
    END AS category,
    COALESCE(SUM(co2_kg), 0) AS total
FROM   activities
WHERE  user_id   = ? AND timestamp >= DATE('now', '-6 days')
GROUP  BY category

4. Fortified Security & Defense in Depth

Passwords: Encrypted using PBKDF2-SHA256 password hashing through Werkzeug.
SQL Injection Prevention: All SQLite queries are fully parameterized.
XSS & Clickjacking Protection: Injected security headers into every HTTP response:
- X-Content-Type-Options: nosniff
- X-Frame-Options: DENY
- X-XSS-Protection: 1; mode=block
- Referrer-Policy: strict-origin-when-cross-origin
- Content-Security-Policy (CSP) restricting assets to 'self' and specific secure fonts.
Session Cookies: Locked with Secure, HttpOnly, and SameSite='Lax' flags.

5. WCAG AA Accessibility Compliance

Wrapped all visual emojis in <span role="img" aria-label="..."> tags for screen-reader compatibility.
Added a .sr-only class to visually hide labels while exposing them to accessibility scanners.
Ensured every page has exactly one prominent <h1> header.
Increased the brightness of text variables to meet the WCAG AA contrast ratio threshold of 4.5:1 on dark backgrounds.

🧪 Testing & Verification

I built a comprehensive test suite in tests/test_app.py containing 47 total assertions across five key test domains:

test_carbon_calculation: Asserts deterministic conversion factors.
test_api_response: Asserts HTTP route statuses (200, 302 redirects) for both authenticated and guest states.
test_ai_json_structure: Verifies parser fallback behavior, JSON key validation, and markdown fence-stripping.
test_login: Tests registration, hashing verification, and correct login redirection.
test_input_validation: Rejects invalid types, zero quantities, empty inputs, and non-numeric strings.

pytest tests/test_app.py -v
# Output: 47 passed in 35.58s

💡 Lessons Learned from vibe coding with Google Antigravity

Building this platform with Google Antigravity shifted my focus from syntax debugging to high-level intent design.

Rapid Prototyping: Iterating on the dark glassmorphism design system took minutes instead of hours.
Continuous Refactoring: Safely adding security headers, accessibility properties, and session caching was completed quickly while maintaining a clean git commit history.
Production Focus: Allowed me to focus on writing thorough tests and setting up Gunicorn database optimizations.

The resulting codebase is performant, accessible, secure, and ready to scale. Let's make the web a greener place! 🌿

This build is part of my journey with **RohithBuilds, where I document building real AI applications with zero fluff.

If you want to learn how to build production-grade AI systems, check out my courses at rohith-builds.onrender.com.

220+ free AI prompts for Indian developers — steal them all

Rohith — Sun, 14 Jun 2026 07:29:29 +0000

I spent 3 months collecting, testing, and organizing AI prompts that actually work for developers.
Not the generic "write me a cover letter" stuff you find everywhere.
These are prompts built specifically for what Indian developers deal with every day:

Cracking placement interviews
Writing code explanations for your resume
Debugging in Python at 2 AM with no mentor
Preparing for service-based vs product-based company interviews
Building projects that stand out in a crowd of 10,000 applicants

I organized all 220+ of them into one free vault.
Here it is → rohith-builds.onrender.com
No login needed to browse. No paywall. Just open and use.

Here's a taste — 20 prompts you can steal right now
For coding interviews:

"Explain this Python code like I'm preparing for a TCS NQT exam: [paste code]"
"Give me 5 variations of this leetcode problem that Infosys commonly asks"
"I have an interview at Wipro tomorrow. Quiz me on Python OOP concepts"
"Convert this brute force solution to optimized — explain each step simply"
"What are the top 10 Python questions asked in FAANG India interviews in 2026?"

For building projects:

"Give me 5 project ideas using Python and AI that no Indian fresher has built yet"
"I'm building [project]. Write a README that makes it look impressive on GitHub"
"Suggest a dataset from data.gov.in I can use to build an AI project about [topic]"
"Turn this basic Python script into something that looks portfolio-worthy"
"What APIs are free and work well in India for building AI apps?"

For job applications:

"Rewrite my resume bullet points to match this job description: [paste JD]"
"I'm a 2025 fresher with no experience. Write a cover letter for this Python role"
"What skills should I add to my resume to get past ATS filters for AI jobs in India?"
"Write a cold message to a hiring manager at [company] on LinkedIn — 3 sentences max"
"I got rejected at [company]. What should I learn before applying again?"

For learning Python faster:

"I know basic Python. Give me a 30-day plan to become AI-ready"
"Explain decorators in Python using a real-world Indian example"
"I keep forgetting list comprehensions. Teach me with 5 progressively harder examples"
"What's the difference between a junior and senior Python developer in India — practically?"
"Give me a mini project I can finish in one weekend that uses Python + AI"

Where to find all 220+
Everything is organized by category inside Rohith Builds:

Interview prep prompts
Project building prompts
Resume and job application prompts
Learning accelerator prompts
AI agent building prompts

👉 rohith-builds.onrender.com
Free. No account needed. Just open the Prompt Vault and start using.

One request
If you find even 5 prompts useful — share this post with one friend who's preparing for placements or learning Python.
That's the only way free resources like this survive.
Drop your favourite prompt from the list in the comments — or share one you've been using that I should add to the vault.
— Rohith

I built a free Python AI platform for Indian developers. Here's everything inside.

Rohith — Sun, 14 Jun 2026 06:51:10 +0000

6 months ago I was a final year CS student
with no real projects and no clear path
into tech.

So I built one.

Not just a project. A full platform.

Here's everything inside Rohith Builds
— completely free, forever.

What's Inside

1. 100 Structured Lessons (Python → AI)

Not random tutorials. A structured path:

Day 1-20: Python basics
Day 21-40: Backend & APIs
Day 41-60: SQL & Databases

Day 61-80: AI & LLMs
Day 81-100: AI Agents & Launch

Every lesson uses Indian examples.
Zomato orders. Cricket scores.
IRCTC bookings. Aadhaar validation.

Because most tutorials use pizza and
baseball. We use what we actually know.

2. Rohi — AI Tutor

Stuck on a concept?

Ask Rohi. he's powered by Groq LLM,
understands Indian context, and is
available 24/7.

No waiting for a mentor to reply.
No expensive bootcamp.
Just ask and learn.

3. Jobs Board — Updated Daily

This took the longest to build.

An autonomous scraper that:
→ Queries LinkedIn Guest API
→ Searches Naukri via AOL gateway
→ Finds Indeed listings
→ Sends each to Groq AI for analysis
→ Filters only junior/fresher roles
→ Checks freshness (last 4 days only)
→ Removes duplicates automatically
→ Inserts into PostgreSQL

Result: 53+ curated jobs. Updated daily.
Filter by: Python, Backend, AI, Frontend
Filter by: 2025, 2026, 2027 graduates

No more scrolling through senior roles
pretending to be junior.

4. 220+ Prompt Vault

Every prompt I've used to:

Debug faster
Write better code
Prepare for interviews
Build projects

Copy. Use. Customize.

5. Improve Any Prompt (Free Tool)

Paste any AI prompt.
Get an optimized version instantly.
No signup needed.

The Tech Stack

Built with:

Python + Flask
PostgreSQL (Neon)
Groq API (Llama-3.3-70b)
Render deployment
SQLAlchemy + psycopg2

No React. No Next.js. No hype stack.
Just Python and PostgreSQL doing real work.

Why I Made It Free

Because every resource that helped me
was either:

Paywalled after lesson 3
In English accent I had to rewind
Using examples I couldn't relate to
Assuming I had a MacBook and ₹50k

This platform assumes nothing except:
you want to learn.

Try It

rohith-builds.onrender.com

Jobs board (no signup):
rohith-builds.onrender.com/jobs

Prompt vault (no signup):
rohith-builds.onrender.com/vault

Built this while working an unpaid
training role,
and posting daily content.

If I can build this, you can learn from it.

Start with Day 1. See you on Day 100.

— Rohith (@rohith_builds)