Weather App

Okay, this is a complex request, but I'll provide a conceptual outline and code snippets to illustrate how you might implement a minimal MCP (Microservice Communication Protocol) server for weather data in Python, along with testing and pre-commit setup. I'll focus on clarity and structure rather than production-readiness. **Conceptual Overview** 1. **MCP Definition (Protocol Buffer):** Define the data structures and service interface using Protocol Buffers. This is the core of MCP. 2. **Server Implementation (Python):** Implement a gRPC server in Python that serves weather data based on the MCP definition. 3. **Client (Example):** A simple client to demonstrate how to request data from the server. 4. **Testing (pytest):** Write unit tests to verify the server's functionality. 5. **Pre-commit Hooks:** Configure pre-commit to run linters (e.g., `flake8`, `black`) and tests before committing code. **1. MCP Definition (weather.proto)** Create a file named `weather.proto`: ```protobuf syntax = "proto3"; package weather; service WeatherService { rpc GetWeather(WeatherRequest) returns (WeatherResponse) {} } message WeatherRequest { string city = 1; } message WeatherResponse { string city = 1; float temperature = 2; string condition = 3; } ``` **Explanation:** * `syntax = "proto3";`: Specifies the Protocol Buffer version. * `package weather;`: Defines the package name for the generated code. * `service WeatherService`: Defines the service with a single RPC method `GetWeather`. * `WeatherRequest`: The request message, containing the city name. * `WeatherResponse`: The response message, containing weather information. **2. Generate gRPC Code (Python)** You'll need the `grpcio-tools` package: ```bash pip install grpcio-tools ``` Then, compile the `.proto` file: ```bash python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. weather.proto ``` This will generate two Python files: `weather_pb2.py` (the data structures) and `weather_pb2_grpc.py` (the gRPC service definitions). **3. Server Implementation (server.py)** ```python import grpc from concurrent import futures import weather_pb2 import weather_pb2_grpc class WeatherServicer(weather_pb2_grpc.WeatherServiceServicer): def GetWeather(self, request, context): city = request.city # Simulate weather data (replace with actual data source) if city == "London": temperature = 15.0 condition = "Cloudy" elif city == "New York": temperature = 25.0 condition = "Sunny" else: temperature = 10.0 condition = "Unknown" return weather_pb2.WeatherResponse(city=city, temperature=temperature, condition=condition) def serve(): server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) weather_pb2_grpc.add_WeatherServiceServicer_to_server(WeatherServicer(), server) server.add_insecure_port('[::]:50051') # Listen on all interfaces, port 50051 server.start() server.wait_for_termination() if __name__ == '__main__': print("Starting gRPC server...") serve() ``` **Explanation:** * `WeatherServicer`: Implements the `WeatherServiceServicer` interface defined in `weather_pb2_grpc.py`. * `GetWeather`: The actual implementation of the RPC method. It receives a `WeatherRequest` and returns a `WeatherResponse`. This example uses hardcoded data for simplicity. In a real application, you'd fetch weather data from an external API or database. * `serve`: Creates a gRPC server, registers the `WeatherServicer`, and starts the server. **4. Client (client.py)** ```python import grpc import weather_pb2 import weather_pb2_grpc def run(): with grpc.insecure_channel('localhost:50051') as channel: stub = weather_pb2_grpc.WeatherServiceStub(channel) city = "London" request = weather_pb2.WeatherRequest(city=city) response = stub.GetWeather(request) print(f"Weather in {response.city}:") print(f" Temperature: {response.temperature}°C") print(f" Condition: {response.condition}") if __name__ == '__main__': run() ``` **Explanation:** * Creates a gRPC channel to connect to the server. * Creates a `WeatherServiceStub` to make RPC calls. * Sends a `WeatherRequest` for the city "London". * Prints the received `WeatherResponse`. **5. Testing (test_server.py)** Install `pytest` and `grpcio-testing`: ```bash pip install pytest grpcio-testing ``` ```python import pytest import grpc from grpc import aio from concurrent import futures import weather_pb2 import weather_pb2_grpc from server import WeatherServicer # Import your server implementation @pytest.fixture def grpc_server(): server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) weather_pb2_grpc.add_WeatherServiceServicer_to_server(WeatherServicer(), server) port = server.add_insecure_port('[::]:0') # Let the OS choose a port server.start() yield server, port # Provide the server and port to the tests server.stop(0) def test_get_weather(grpc_server): server, port = grpc_server with grpc.insecure_channel(f'localhost:{port}') as channel: stub = weather_pb2_grpc.WeatherServiceStub(channel) request = weather_pb2.WeatherRequest(city="London") response = stub.GetWeather(request) assert response.city == "London" assert response.temperature == 15.0 assert response.condition == "Cloudy" def test_get_weather_new_york(grpc_server): server, port = grpc_server with grpc.insecure_channel(f'localhost:{port}') as channel: stub = weather_pb2_grpc.WeatherServiceStub(channel) request = weather_pb2.WeatherRequest(city="New York") response = stub.GetWeather(request) assert response.city == "New York" assert response.temperature == 25.0 assert response.condition == "Sunny" def test_get_weather_unknown(grpc_server): server, port = grpc_server with grpc.insecure_channel(f'localhost:{port}') as channel: stub = weather_pb2_grpc.WeatherServiceStub(channel) request = weather_pb2.WeatherRequest(city="SomeRandomCity") response = stub.GetWeather(request) assert response.city == "SomeRandomCity" assert response.temperature == 10.0 assert response.condition == "Unknown" ``` **Explanation:** * `grpc_server` fixture: Creates and starts a gRPC server before each test and stops it afterward. It uses a dynamically assigned port to avoid conflicts. * `test_get_weather`: Tests the `GetWeather` method for a specific city ("London"). * `test_get_weather_new_york`: Tests the `GetWeather` method for a specific city ("New York"). * `test_get_weather_unknown`: Tests the `GetWeather` method for an unknown city. Run the tests: ```bash pytest ``` **6. Pre-commit Setup** Install `pre-commit`: ```bash pip install pre-commit ``` Create a `.pre-commit-config.yaml` file in the root of your project: ```yaml repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 # Use the latest stable version hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-added-large-files - repo: https://github.com/psf/black rev: 24.2.0 # Use the latest stable version hooks: - id: black - repo: https://github.com/pycqa/flake8 rev: 7.0.0 # Use the latest stable version hooks: - id: flake8 additional_dependencies: ["flake8-protobuf"] # Add protobuf support - repo: local hooks: - id: pytest name: pytest entry: pytest language: system types: [python] pass_filenames: false ``` **Explanation:** * This configuration uses several popular pre-commit hooks: * `trailing-whitespace`, `end-of-file-fixer`, `check-yaml`, `check-added-large-files`: Basic cleanup and checks. * `black`: Code formatter. * `flake8`: Linter. The `additional_dependencies` line adds support for linting Protocol Buffer-related code. * `pytest`: Runs your tests. Install the pre-commit hooks: ```bash pre-commit install ``` Now, every time you commit, pre-commit will run these checks and formatters. If any of them fail, the commit will be aborted. **Running the Example** 1. **Generate gRPC code:** `python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. weather.proto` 2. **Start the server:** `python server.py` (in a separate terminal) 3. **Run the client:** `python client.py` 4. **Run the tests:** `pytest` 5. **Commit your changes:** `git commit -m "Your commit message"` (pre-commit will run) **Important Considerations and Improvements** * **Error Handling:** The server implementation lacks proper error handling. You should handle exceptions and return appropriate gRPC status codes. * **Data Source:** The server uses hardcoded weather data. Replace this with a real data source (e.g., an API or database). * **Authentication/Authorization:** For production systems, you'll need to add authentication and authorization to secure your gRPC service. * **Logging:** Implement logging for debugging and monitoring. * **Configuration:** Use environment variables or configuration files to manage server settings (e.g., port number, data source connection details). * **Asynchronous gRPC:** Consider using `grpc.aio` for asynchronous gRPC, which can improve performance, especially for I/O-bound operations. * **Health Checks:** Implement a health check endpoint to allow monitoring systems to verify the server's availability. * **Code Generation:** Consider using a build system (e.g., `make`, `nox`) to automate the code generation process. * **Dependency Management:** Use a tool like `poetry` or `pipenv` to manage your project's dependencies. * **CI/CD:** Integrate your tests and pre-commit hooks into a CI/CD pipeline to automate the build, test, and deployment process. This comprehensive example provides a solid foundation for building a gRPC-based microservice with testing and pre-commit setup. Remember to adapt and extend it to meet the specific requirements of your application.