AI-Powered Expense Classifier

An intelligent API that classifies natural-language expense descriptions into categories like Transportation, Food & Drink, Entertainment, etc powered by machine learning and FastAPI.

It also supports:

• Batch classification
• OCR classification from images/receipts
• Confidence scoring for each prediction

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Features

• Text Classification — Predicts expense categories from written descriptions.
• OCR Support — Extracts and classifies text from images (e.g. receipts).
• ML-Powered — Trained using scikit-learn with TfidfVectorizer and LogisticRegression.
• Batch Input — Classify multiple expenses in a single call.
• FastAPI Backend — Modern async API with auto-generated Swagger docs.

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Setup Instructions

1. Clone the repo

git clone https://github.com/your-username/ai-expense-classifier.git
cd ai-expense-classifier

2. Create and activate virtual environment

python3 -m venv venv
source venv/bin/activate

3. Install dependencies

pip install fastapi uvicorn scikit-learn pandas joblib pillow pytesseract python-multipart

4. Model training

python app/ml/train_model.py

This script trains a logistic regression classifier on the mock dataset and saves it to app/models/expense_classifier.pkl

5. Run the API

uvicorn app.main:app --reload

Visit:

http://localhost:8000

http://localhost:8000/docs (Swagger UI)

Example usage

POST /classify
{
  "description": "Flight to Toronto"
}

Response

{
  "description": "Flight to Toronto",
  "predicted_category": "Travel",
  "confidence": 0.84
}