Update README.md

README.md

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 # Smart Budget Recommendation API
-
-A FastAPI-based service that provides intelligent budget recommendations based on past spending behavior. This service analyzes historical expense data and suggests personalized budgets for each category.
-
-## Features
-
-- **Smart Budget Recommendations**: Analyzes past spending patterns and recommends personalized budgets
-- **Category-based Analysis**: Provides recommendations for each expense category
-- **Confidence Scoring**: Each recommendation includes a confidence score based on data quality
-- **MongoDB Integration**: Stores expenses and budgets in MongoDB
-- **RESTful API**: Clean API endpoints for expense tracking and budget management
-
-## Example Recommendation
-
-```json
-{
-  "category": "Groceries",
-  "average_expense": 3800.0,
-  "recommended_budget": 4000.0,
-  "reason": "Your average monthly grocery expense is Rs.3,800. We suggest setting your budget to Rs.4,000 for next month (includes a 5% buffer for variability).",
-  "confidence": 0.85
-}
-```
-
-## API Endpoints
-
-### Health Check
-- `GET /health` - Check API and database connection status
-
-### Expenses
-- `POST /expenses` - Create a new expense
-- `GET /expenses?user_id={user_id}` - Get expenses for a user
-
-### Budgets
-- `POST /budgets` - Create a new budget
-- `GET /budgets?user_id={user_id}` - Get budgets for a user
-
-### Recommendations
-- `GET /recommendations/{user_id}?month={month}&year={year}` - Get smart budget recommendations
-- `GET /category-expenses/{user_id}?months={months}` - Get average expenses by category
-
-## Local Development
-
-### Prerequisites
-- Python 3.11+
-- MongoDB connection string
-
-### Setup
-
-1. Install dependencies:
-```bash
-pip install -r requirements.txt
-```
-
-2. Set environment variables:
-```bash
-export MONGODB_URI="mongodb://expenseuser:Kem_6o%3F%[email protected]:27017/expense?authSource=admin"
-# optional
-export OPENAI_API_KEY="your_openai_key"
-```
-
-3. Run the application:
-```bash
-uvicorn app.main:app --reload
-```
-
-The API will be available at `http://localhost:8000`
-
-## Docker Deployment
-
-### Build Docker Image
-```bash
-docker build -t smart-budget-recommendation .
-```
-
-### Run Docker Container
-```bash
-docker run -p 7860:7860 -e MONGODB_URI="your_mongodb_uri" smart-budget-recommendation
-```
-
-## Hugging Face Deployment
-
-### Prerequisites
-1. Hugging Face account
-2. Docker installed
-3. MongoDB connection string stored as a secret in Hugging Face
-
-### Steps
-
-1. **Store secrets in Hugging Face**:
-   - Go to your Hugging Face Space settings
-   - Navigate to "Variables and secrets"
-   - Add `MONGODB_URI` with your MongoDB connection string
-   - *(Optional)* Add `OPENAI_API_KEY` if you want GPT-powered recommendations
-
-2. **Create Hugging Face Space**:
-   - Create a new Space on Hugging Face
-   - Choose "Docker" as the SDK
-
-3. **Upload Files**:
-   - Upload all files from this repository to your Space
-   - Ensure `Dockerfile` is in the root directory
-
-4. **Configure Space**:
-   - The Dockerfile will automatically build and run the application
-   - The app will use the `MONGODB_URI` secret from Hugging Face environment variables
-
-### Hugging Face Space Configuration
-
-The Dockerfile is configured to:
-- Use Python 3.11
-- Install all dependencies
-- Expose port 7860 (Hugging Face default)
-- Run the FastAPI application with Uvicorn
-
-Hugging Face will automatically:
-- Build the Docker image
-- Set environment variables from secrets
-- Deploy the container
-- Provide a public URL
-
-## API Documentation
-
-Once deployed, access interactive API documentation at:
-- Swagger UI: `http://your-url/docs`
-- ReDoc: `http://your-url/redoc`
-
-## Algorithm Details
-
-The Smart Budget Recommendation engine:
-
-1. **Data Collection**: Analyzes expenses from the past 6 months
-2. **Category Analysis**: Groups expenses by category and calculates:
-   - Average monthly spending
-   - Standard deviation (variability)
-   - Number of transactions
-   - Months of data available
-3. **Budget Calculation**: 
-   - Base: Average monthly expense
-   - Buffer: 5-10% based on spending variability
-   - Rounding: Rounds to nearest 100 for cleaner numbers
-4. **Confidence Scoring**: 
-   - Based on data quality (months analyzed, transaction count, consistency)
-   - Returns score from 0-1
-5. **(Optional) GPT Refinement**:
-   - If `OPENAI_API_KEY` is set, GPT reviews the history and suggests increase/decrease/keep along with a natural-language reason.
-
-## Environment Variables
-
-- `MONGODB_URI`: MongoDB connection string (required)
-- `OPENAI_API_KEY`: Optional. If set, GPT-based recommendations augment the rule-based engine.
-
-## License
-
-Copyright © 2025 WalletSync. All Rights Reserved