基于NLP的垃圾邮件分类器.zip

# SPAM or HAM Classifier API This repository contains code for a FastAPI-based API that classifies text paragraphs into SPAM or HAM (non-SPAM) using a Multinomial Naive Bayes classifier. The classifier is trained on a dataset of labeled messages, and the API provides an endpoint for making predictions. # Files data_get.py: Python script containing Natural Language Processing (NLP) functions, data preprocessing, and model training. api_app.py: FastAPI script defining the API structure, endpoints, and integration with the NLP functions for text classification. Setup and Dependencies Before running the API, ensure you have the required dependencies installed. Run the following commands in your terminal: pip install -r requirements.txt # Running the API To start the FastAPI server, run the following command: uvicorn api_app:app --host 127.0.0.1 --port 8000 --reload This will launch the API at http://127.0.0.1:8000. # API Endpoints # 1. /status (GET) Description: Check the API's connection status. Endpoint: /status Response: { "status": "API connected successfully" } # 2. /spam_classifier_ (POST) Description: Classify text paragraphs into SPAM or HAM. Endpoint: /spam_classifier_ Request Method: POST # Request Payload Format: { "input_text": '["this is your email text which you can simply copy and paste"]' } - Response Format: "SPAM" or "HAM". # Usage Check the API status: curl -X GET "http://127.0.0.1:8000/status" Classify text paragraphs (example using cURL): curl -X POST "http://127.0.0.1:8000/spam_classifier_" -H "Content-Type: application/json" -d '{"input_text": "Your text here"}' # Example Response: "HAM" Additional Notes The NLP functions and model training are implemented in data_get.py. The FastAPI structure and API endpoints are defined in api_app.py. The model is a Multinomial Naive Bayes classifier trained on a dataset of labeled messages. Feel free to explore and integrate this API into your projects!