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add file monitoring with marshmallow/swagger
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89 changes: 62 additions & 27 deletions
89
regression_problems/predict_real_estate_prices_in_california/flask/main.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,49 +1,84 @@ | ||
| from flask import Flask, request, jsonify, render_template | ||
| from flask import Flask, request, jsonify, render_template, send_from_directory | ||
| import pickle | ||
| import pandas as pd | ||
| from marshmallow import Schema, fields, ValidationError | ||
| from flask_swagger_ui import get_swaggerui_blueprint | ||
| from flask_wtf.csrf import CSRFProtect | ||
| from flask_limiter import Limiter | ||
| from flask_limiter.util import get_remote_address | ||
|
|
||
| app = Flask(__name__) | ||
|
|
||
| # Configuração da proteção CSRF | ||
| csrf = CSRFProtect(app) | ||
|
|
||
| # Configuração de rate limiting | ||
| limiter = Limiter( | ||
| get_remote_address, | ||
| app=app, | ||
| default_limits=["200 per day", "50 per hour"] | ||
| ) | ||
|
|
||
| # Swagger setup http://localhost:5000/swagger | ||
| SWAGGER_URL = '/swagger' | ||
| API_URL = '/static_swagger/swagger.json' | ||
| swaggerui_blueprint = get_swaggerui_blueprint(SWAGGER_URL, API_URL, config={'app_name': "Real Estate Price Prediction API"}) | ||
| app.register_blueprint(swaggerui_blueprint, url_prefix=SWAGGER_URL) | ||
|
|
||
| # Adicionar uma rota para servir o arquivo swagger.json | ||
| @app.route('/static_swagger/<path:filename>') | ||
| def serve_static(filename): | ||
| return send_from_directory('static_swagger', filename) | ||
|
|
||
| # Carregar o modelo treinado | ||
| model = pickle.load(open('../model_trained/model_regression_immobile.pkl', 'rb')) | ||
|
|
||
| # Carregar a base de dados | ||
| #data = pd.read_csv('../data/arquivo.csv') | ||
| # Definição do schema de validação usando marshmallow | ||
| class InputSchema(Schema): | ||
| MedInc = fields.Float(required=True) | ||
| HouseAge = fields.Float(required=True) | ||
| AveRooms = fields.Float(required=True) | ||
| AveBedrms = fields.Float(required=True) | ||
| Population = fields.Int(required=True) | ||
| AveOccup = fields.Float(required=True) | ||
| RoomDensity = fields.Float(required=True) | ||
| Latitude = fields.Float(required=True) | ||
| Longitude = fields.Float(required=True) | ||
|
|
||
| @app.route('/') | ||
| def index(): | ||
| return render_template('index.html') | ||
|
|
||
| @app.route('/predict', methods=['POST']) | ||
| @limiter.limit("10 per minute") # Limite de 10 requisições por minuto para este endpoint | ||
| @csrf.exempt # Exemplo: desabilitar CSRF para a rota de API (opcional, com cuidado) | ||
| def predict(): | ||
| # Receber dados do cliente | ||
| req_data = request.get_json() | ||
|
|
||
| # Verificar se todos os campos necessários estão presentes | ||
| required_fields = ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'] | ||
| if not all(field in req_data for field in required_fields): | ||
| return jsonify({'error': 'Missing required fields'}), 400 | ||
|
|
||
| # Criar DataFrame a partir dos dados recebidos | ||
| input_data = { | ||
| 'MedInc': req_data['MedInc'], | ||
| 'HouseAge': req_data['HouseAge'], | ||
| 'AveRooms': req_data['AveRooms'], | ||
| 'AveBedrms': req_data['AveBedrms'], | ||
| 'Population': req_data['Population'], | ||
| 'AveOccup': req_data['AveOccup'], | ||
| 'Latitude': req_data['Latitude'], | ||
| 'Longitude': req_data['Longitude'] | ||
| } | ||
|
|
||
| input_df = pd.DataFrame([input_data]) | ||
|
|
||
| try: | ||
| # Receber dados do cliente | ||
| req_data = request.get_json() | ||
|
|
||
| # Validar entrada | ||
| schema = InputSchema() | ||
| validated_data = schema.load(req_data) | ||
|
|
||
| # Criar DataFrame a partir dos dados validados | ||
| input_df = pd.DataFrame([validated_data]) | ||
|
|
||
| # Prever usando o modelo carregado | ||
| prediction = model.predict(input_df) | ||
| return jsonify({'prediction': prediction[0]}) | ||
| except ValidationError as err: | ||
| # Erros de validação do input | ||
| return jsonify({'error': 'Validation Error', 'messages': err.messages}), 400 | ||
| except KeyError as err: | ||
| # Se algum campo esperado não estiver presente | ||
| return jsonify({'error': f'Missing required field: {err}'}), 400 | ||
| except ValueError as err: | ||
| # Erros relacionados ao valor dos dados de entrada | ||
| return jsonify({'error': f'Invalid input: {err}'}), 400 | ||
| except Exception as e: | ||
| return jsonify({'error': str(e)}), 400 | ||
| # Erro geral | ||
| return jsonify({'error': 'An error occurred', 'message': str(e)}), 500 | ||
|
|
||
| if __name__ == "__main__": | ||
| app.run(host='0.0.0.0', port=5000) | ||
| app.run(host='0.0.0.0', port=5000) |
82 changes: 82 additions & 0 deletions
82
...ssion_problems/predict_real_estate_prices_in_california/flask/static_swagger/swagger.json
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,82 @@ | ||
| { | ||
| "swagger": "2.0", | ||
| "info": { | ||
| "description": "API para prever preços de imóveis", | ||
| "version": "1.0.0", | ||
| "title": "Real Estate Price Prediction API" | ||
| }, | ||
| "host": "localhost:5000", | ||
| "basePath": "/", | ||
| "schemes": [ | ||
| "http" | ||
| ], | ||
| "paths": { | ||
| "/predict": { | ||
| "post": { | ||
| "tags": ["prediction"], | ||
| "summary": "Obter previsão de preço", | ||
| "description": "Envia dados de entrada e obtém a previsão de preço do imóvel.", | ||
| "consumes": ["application/json"], | ||
| "produces": ["application/json"], | ||
| "parameters": [ | ||
| { | ||
| "in": "body", | ||
| "name": "body", | ||
| "description": "Dados de entrada", | ||
| "required": true, | ||
| "schema": { | ||
| "$ref": "#/definitions/InputData" | ||
| } | ||
| } | ||
| ], | ||
| "responses": { | ||
| "200": { | ||
| "description": "Previsão obtida com sucesso", | ||
| "schema": { | ||
| "type": "object", | ||
| "properties": { | ||
| "prediction": { | ||
| "type": "number", | ||
| "example": 150000.0 | ||
| } | ||
| } | ||
| } | ||
| }, | ||
| "400": { | ||
| "description": "Erro de validação de entrada" | ||
| }, | ||
| "500": { | ||
| "description": "Erro interno do servidor" | ||
| } | ||
| } | ||
| } | ||
| } | ||
| }, | ||
| "definitions": { | ||
| "InputData": { | ||
| "type": "object", | ||
| "required": [ | ||
| "MedInc", | ||
| "HouseAge", | ||
| "AveRooms", | ||
| "AveBedrms", | ||
| "Population", | ||
| "AveOccup", | ||
| "RoomDensity", | ||
| "Latitude", | ||
| "Longitude" | ||
| ], | ||
| "properties": { | ||
| "MedInc": {"type": "number", "example": 8.3252}, | ||
| "HouseAge": {"type": "number", "example": 41.0}, | ||
| "AveRooms": {"type": "number", "example": 6.9841}, | ||
| "AveBedrms": {"type": "number", "example": 1.0238}, | ||
| "Population": {"type": "integer", "example": 322}, | ||
| "AveOccup": {"type": "number", "example": 2.5556}, | ||
| "Latitude": {"type": "number", "example": 37.88}, | ||
| "Longitude": {"type": "number", "example": -122.23}, | ||
| "RoomDensity": {"type": "number", "example": -122.23} | ||
| } | ||
| } | ||
| } | ||
| } |
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