Feature Completion and Enhancements for Marriage Matchmaking App

[utkarshupdy]

Overview

This pull request completes the required functionality for the Marriage Matchmaking App by implementing the following features and improvements:

Add User Update Endpoint: A PUT /users/{user_id} endpoint has been implemented to allow partial updates of user profiles.
Add User Deletion Endpoint: A DELETE /users/{user_id} endpoint has been implemented to allow deletion of user profiles by ID.
Find Matches for a User: A GET /users/{user_id}/matches endpoint has been added to find potential matches for a user based on:
1-Shared interests
2-Age proximity (±3 years)
3-Geographical proximity (city proximity scoring)
4-Email Validation: Validation has been added to ensure that email addresses conform to a standard format using a regular
expression.
5-Pagination Support: The GET /users endpoint now supports pagination with skip and limit query parameters.

Changes in the Codebase

**main.py:**
1- CRUD endpoints for user creation, retrieval, update, and deletion.
2- Matching algorithm based on interests, city proximity, and age difference.
3- Email validation during user creation.

**schemas.py:**
1- Pydantic models for user input/output validation and data serialization.
2- Added support for scoring in the UserWithScore model for matchmaking results.

**models.py:**
1-Refined SQLAlchemy model for User to align with Pydantic schemas.

**database.py:**
1-Configured SQLite database setup using SQLAlchemy.

**requirements.txt:**
1-Added dependencies for FastAPI, SQLAlchemy, and geopy.

Key Features Implemented

Update User by ID:
1-Allows updating user details partially (only provided fields).
2-Validates input data to ensure correctness.

Delete User by ID:
1- Deletes a user profile from the database.

Matchmaking Algorithm:
1-Calculates a match score using:
2-Interest similarity: Based on the overlap of interests between two users.
3-City proximity: Using geographic coordinates and distance scoring.
4-Age proximity: Users within ±3 years are prioritized.
5-Results are sorted in descending order of match score.

Email Validation:
1-Ensures that email addresses are valid during user creation via regix.

Assumptions and Notes

1-Age Proximity: Matches are restricted to users within ±3 years of the given user's age.
2-Geographical Scoring: Uses geopy to calculate distances between coordinates; a proximity score is calculated based on 3-closeness.
3-Interest Overlap: Interests are treated as a list of strings; overlapping interests increase the match score.
4-Database: SQLite is used for simplicity. Suitable for small-scale applications.

### Testing
Unit Testing:
1-Tested all endpoints for expected behavior.
2-Verified error handling for invalid inputs (e.g., invalid email or nonexistent user ID).

Manual Testing:
1-Verified matchmaking results for various users with differing attributes.
2-Confirmed email validation and CRUD operations.

Dependencies

Ensure the following Python packages are installed:

FastAPI
SQLAlchemy
geopy
pydantic

### Instructions to Run
Install dependencies: pip install -r requirements.txt
Start the FastAPI server: uvicorn main:app --reload
Use the provided endpoints for CRUD operations and matchmaking.

Future Work
1-Enhance matching logic to incorporate user preferences or weights for scoring criteria.
2-Add support for advanced filtering in the matchmaking endpoint.
3-Integrate testing frameworks for automated testing.