Merge pull request #3 from TailUFPB/csv-table-changes

Csv table changes

api/DataProcesser.py

@@ -21,6 +21,17 @@ def handle_classify(self, df, classifier):
 
  return classifier_switcher.get(classifier, lambda: "Invalid Classifier")(df)
 
+ def generate_statistics(self, df):
+ unique_labels = df['output_column'].unique()
+
+ statistics = {
+ 'total_rows': len(df),
+ 'unique_labels': list(unique_labels),
+ 'label_counts': df['output_column'].value_counts().to_dict()
+ }
+
+ return statistics
+
  def preprocess_text(self, texto):
  if self.input_column is not None: # Verifique se a coluna foi definida
  # tiro tudo que não for texto e espaço
@@ -36,23 +47,15 @@ def preprocess_text(self, texto):
  return "Coluna não escolhida. Escolha a coluna primeiro."
 
  def classify_emotions(self, df):
- df['output_column'] = df['input_column'].apply(
- self.preprocess_text).apply(make_prediction)
-
- result_csv = df # converte o df pra csv
- return result_csv
+ df['output_column'] = df['input_column'].apply(make_prediction)
+ return df
 
  def lin_regression_model(self, df):
- df['output_column'] = df['input_column'].apply(
- self.preprocess_text).apply(make_prediction_nblin)
-
- result_csv = df # converte o df pra csv
- return result_csv
+ df['output_column'] = df['input_column'].apply(make_prediction_nblin)
+ return df
 
  def nb_news_application(self, df):
  df['output_column'] = df['input_column'].apply(news_prediction)
-
- result_csv = df
- return result_csv
+ return df
 
  ##TODO métodos com o processamento de classificação

api/NbEmotionsModel.py

@@ -15,4 +15,4 @@ def make_prediction(my_sentence):
 
  new_sentence = tfidf_vectorizer.transform([my_sentence])
  prediction = model.predict(new_sentence)
- return prediction
+ return prediction[0]

api/app.py

@@ -1,22 +1,31 @@
 from flask import Flask, jsonify, request
 from flask_cors import CORS
 from DataProcesser import DataProcesser
+from available_classifiers import get_available_classifiers
 
 import os
+import atexit
+import threading
 import pandas as pd
 import nltk
 import json
 nltk.download('wordnet')
 
 app = Flask(__name__)
+server_thread = None
 CORS(app) # Permite todas as origens por padrão (não recomendado para produção)
 
-UPLOAD_FOLDER = './uploads'
-app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 app.config['JSONIFY_PRETTYPRINT_REGULAR'] = True
 
 data_processer = DataProcesser()
 
+def run_flask_app():
+ global server_thread
+ app.run(host='0.0.0.0', port=5000, debug=True, use_reloader=False)
+
+def shutdown_server():
+ print("Server shutting down...")
+ os.kill(os.getpid(), 9)
 
 @app.route('/', methods=['GET'])
 def hello_world():
@@ -29,29 +38,23 @@ def upload_file():
  selected_data = received_data.get('data')
  selected_classifier = int(received_data.get('classifier'))
 
- #print("selected data: ", selected_data)
- #print("selected classifier: ", selected_classifier)
-
  df = pd.DataFrame(selected_data, columns=['input_column'])
  result = data_processer.handle_classify(df, selected_classifier)
+ stats = data_processer.generate_statistics(result)
 
- return jsonify({'result': result.to_json()})
-
-@app.route('/nb-news-model', methods=["POST"])
-def news_model():
- result = data_processer.nb_news_application()
- result_json = result.to_json()
- return jsonify({"result": result_json})
+ return jsonify({'result': result.to_json(), 'stats': stats})
 
-@app.route('/nb-emotions-model', methods=["POST"])
-def emotions_model():
- result = data_processer.classify_emotions()
- return jsonify({"result": result.to_json()})
+@app.route('/get-classifiers', methods=["GET"])
+def get_classifiers():
+ classifiers = get_available_classifiers()
+ return jsonify(classifiers)
 
-@app.route('/lin-regression-model', methods=["POST"])
-def lin_regression_model():
- result = data_processer.lin_regression_model()
- return jsonify({"result": result.to_json()})
+@app.get('/shutdown')
+def shutdown():
+ shutdown_server()
+ return 'Server shutting down...'
 
 if __name__ == '__main__':
- app.run(host='0.0.0.0', port=5000, debug=True)
+ server_thread = threading.Thread(target=run_flask_app)
+ server_thread.start()
+ atexit.register(shutdown_server)

api/available_classifiers.py

@@ -0,0 +1,6 @@
+def get_available_classifiers():
+ return [
+ {'id': 0, 'name': 'Naive-Bayes Emotions'},
+ {'id': 1, 'name': 'Naive-Bayes News Topics'},
+ {'id': 2, 'name': 'Linear Regression Emotions'}
+ ]

main.js

@@ -1,17 +1,19 @@
 const { app, BrowserWindow } = require("electron");
 const childProcess = require("child_process");
 const path = require("path");
+const axios = require("axios");
 const url = require("url");
 
 var processes = [];
 
+/*
 const child = childProcess.spawn("python", ["./api/app.py"], {
  detached: false,
  stdio: "ignore",
 });
 
 processes.push(child);
-
+*/
 function createWindow() {
  // cria a janela
  const win = new BrowserWindow({
@@ -56,12 +58,15 @@ app.on("before-quit", () => {});
 
 app.on("window-all-closed", () => {
  if (process.platform !== "darwin") {
- /*
- // TENTA fechar o processo do python
- processes.forEach(function (proc) {
- proc.kill();
- }); */
+ //console.log("Fechando")
+ axios.get("http://127.0.0.1:5000/shutdown").then(response => {
+
+ }).catch(error => {
+
+ })
+ setTimeout(() => {
+ app.quit();
+ }, 2000);
 
- app.quit();
  }
 });

src/components/csvTable/csvTable.css

@@ -0,0 +1,30 @@
+.csv-table th, .csv-table td {
+ max-width: 500px;
+ height: 100px;
+ overflow: hidden;
+ text-overflow: ellipsis;
+ white-space: nowrap;
+}
+
+.csv-table td {
+ text-align: left;
+}
+
+.csv-div {
+ margin-top: 1.25rem;
+ width: 90%;
+ height: 80%;
+ max-height: 1500px;
+ max-width: 1200px;
+ margin-left: 1.25rem;
+ overflow-x: auto;
+ overflow-y: auto;
+ border-radius: 1rem;
+ border-width: 0.125rem;
+ border-color: #ccc;
+ }
+
+ .csv-div th, .csv-div td {
+ border: 0.0625rem solid #ccc;
+ padding: 0.625rem;
+ }

src/components/csvTable/csvTable.tsx

@@ -1,4 +1,5 @@
 import React from "react";
+import './csvTable.css';
 
 interface Props {
  head: any[];
@@ -7,8 +8,8 @@ interface Props {
 
 export default function CsvTable({ data, head }: Props) {
  return (
- <div className="mt-5 w-11/12 mx-3 overflow-hidden rounded-3xl border-2 border-grey">
- <table className="w-full">
+ <div className="csv-div border-grey">
+ <table className="csv-table">
  <thead>
  <tr className="rounded-3xl bg-main-darker">
  {head.map((headTitle, index) => {

src/components/resultTable/resultTable.tsx

@@ -23,7 +23,7 @@ export default function ResultTable({ data, classifierName }: Props) {
  .map((row: any, index: number) => {
  return (
  <tr
- key={row} 
+ key={row}
  className={`${
  index % 2 ? `bg-main-bold` : `bg-main-medium`
  } border-t-2`}

src/pages/home.tsx

@@ -14,6 +14,8 @@ export default function Home() {
 
  const [result, setResult] = useState<{ [key: string]: any }>({});
 
+ const [isLoading, setIsLoading] = useState(false);
+
  const handleChangeSelectedColumn = (event: any) => {
  setSelectedColumn(event.target.value);
  };
@@ -23,6 +25,7 @@ export default function Home() {
  };
 
  const handleSubmit = async () => {
+ setIsLoading(true);
  let selectedData = data.map((row) => row[selectedColumn]);
  console.log(selectedData)
  console.log(selectedClassifier);
@@ -55,6 +58,8 @@ export default function Home() {
  console.error("Os arrays 'input' e 'output' têm tamanhos diferentes.");
  }
  }
+
+ setIsLoading(false);
  };
 
  const handleDownloadOutputCSV = async () => {
@@ -134,7 +139,7 @@ export default function Home() {
  className="w-full bg-main-dark text-white py-2 px-4 hover:bg-main-darker focus:outline-none border-2 border-main-lighter rounded-3xl h-14"
  onClick={handleSubmit}
  >
- Enviar
+ {isLoading ? "Carregando..." : "Classificar"}
  </button>
  </div>