EcoMate-AI

EcoMate-AI analyzes receipts, bills, and daily activities to estimate your carbon footprint and recommend greener choices, powered by multimodal AI.

Upload an image of a receipt or describe your day in plain text. The app extracts individual activities, maps them to verified CO₂ emission factors, and generates a personalized sustainability report with actionable tips.

View Live Demo  |  Portfolio

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Features

• Multimodal input — upload receipt images or enter free-form text
• OCR extraction — reads items, quantities, and services from scanned receipts
• CO₂ estimation — maps activities to verified global emission factors
• Personalized tips — AI-generated suggestions ranked by impact
• Global comparison — contextualizes your footprint against regional and world averages
• Interactive visualizations — category breakdowns and trend charts

Tech stack

Layer	Tools
Frontend	Streamlit
Backend	FastAPI
AI / OCR	OpenAI GPT-4o, Vision API
Data	pandas, NumPy
Visualization	Plotly, Matplotlib

Getting started

git clone https://github.com/ShamikOfficial/EcoMate-AI.git
cd EcoMate-AI
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env        # add your OpenAI API key
streamlit run app/main.py

Project structure

EcoMate-AI/
├── app/
│   ├── main.py             # Streamlit frontend
│   ├── api.py              # FastAPI backend
│   ├── genai_model.py      # GenAI inference layer
│   ├── services/           # Carbon calculation logic
│   └── utils/              # Preprocessing helpers
├── data/                   # Emission factor datasets
└── requirements.txt

How it works

1. User uploads a receipt image or types a description of their activities
2. OCR pipeline extracts line items and quantities
3. Each item is classified and matched to an emission factor (kg CO₂e)
4. Total footprint is calculated and broken down by category
5. GPT-4o generates ranked, personalized recommendations

License

MIT