brain

api/Neural_Network2.py

@@ -33,14 +33,17 @@ def __init__(self):
  self.batch_count = 0
 
  def on_batch_end(self, batch, logs=None):
+ print("batch end")
  self.batch_count += 1
  if self.batch_count % 50 == 0:
  self.update_progress(logs)
 
  def on_epoch_end(self, epoch, logs=None):
+ print("epoch end")
  self.update_progress(logs)
 
  def update_progress(self, logs):
+ print("updating progress")
  total_epochs = self.params['epochs']
  current_batch = self.model._train_counter 
  total_batches = self.params['steps'] * total_epochs
@@ -99,12 +102,20 @@ def create_and_train_model(train_texts, train_labels, name, epochs=5, batch_size
  tf.keras.layers.Dense(num_classes, activation='softmax')
  ])
 
+
  model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
 
  try:
  progress_callback = TrainingProgressCallback()
-
- history = model.fit(train_dataset, epochs=epochs, batch_size=batch_size)
+
+ print("train")
+ print(train_dataset)
+ print("epochs")
+ print(epochs)
+ print("batch_size")
+ print(batch_size)
+ # history = model.fit(train_dataset, epochs=epochs, batch_size=batch_size, verbose=2, callbacks=[progress_callback])
+ history = model.fit(train_dataset, epochs=epochs, batch_size=batch_size, verbose=2)
 
  model_filename = f"api/models/{str(num_classes)}-Trained-Model-{name}.weights.h5"
  model.save_weights(model_filename)
@@ -119,4 +130,3 @@ def create_and_train_model(train_texts, train_labels, name, epochs=5, batch_size
  except Exception as e:
  return f"Error during model creation/training: {str(e)}"
 
-

api/app.py

@@ -11,8 +11,13 @@
 import nltk
 import json
 import asyncio
+import logging
 nltk.download('wordnet')
 
+
+# log = logging.getLogger('werkzeug')
+# log.setLevel(logging.ERROR)
+
 app = Flask(__name__)
 server_thread = None
 CORS(app) # Permite todas as origens por padrão (não recomendado para produção)
@@ -83,12 +88,13 @@ def train_model():
 
  # reseta status
  training_progress = {
- 'training_progress': 1,
+ 'training_progress': 0,
  'training_in_progress': True
  }
  with open('training_progress.json', 'w') as file:
  json.dump(training_progress, file)
 
+ print("Beginning training")
  create_and_train_model(selected_data, selected_label, name, epochs, batch_size)
 
  return jsonify({"message": "Model train started successfully."}), 200 
@@ -113,6 +119,6 @@ def get_training_status():
  #shutdown_server()
 
 if __name__ == '__main__':
- app.run(host='0.0.0.0', port=5000)
+ app.run(host='127.0.0.1', port=5000, debug=True)
  #server_thread = threading.Thread(target=run_flask_app)
  #server_thread.start()

src/pages/train.tsx

@@ -25,7 +25,7 @@ export default function Train() {
 
  const handleSubmit = async () => {
  setIsLoading(true);
- setLoadingProgress(1);
+ setLoadingProgress(0);
 
  let selectedData = data.map((row) => ({
  value: row[selectedColumn],
@@ -51,23 +51,23 @@ export default function Train() {
  let retryCount = 0;
 
 
- const url = "http://localhost:5000/neural-network";
+ // const url = "http://localhost:5000/neural-network";
 
 
- async function postData(url: string, data: { data: any[]; label: any[]; batch_size: number; epochs: number; learning_rate: number; name: string; }) {
- try {
- const response = await axios.post(url, data);
- } catch (error) {
- if (retryCount < maxRetries) {
- retryCount++;
- console.error(`Error occurred, retrying (attempt ${retryCount})...`);
- postData(url, data); // Retry recursively
- } else {
- console.error("Max retry limit reached. Unable to post data.");
- throw error; // Throw the error after maximum retries
- }
- }
- }
+ // async function postData(url: string, data: { data: any[]; label: any[]; batch_size: number; epochs: number; learning_rate: number; name: string; }) {
+ //  try {
+ //  const response = await axios.post(url, data);
+ //  } catch (error) {
+ //  if (retryCount < maxRetries) {
+ //  retryCount++;
+ //  console.error(`Error occurred, retrying (attempt ${retryCount})...`);
+ //  postData(url, data); // Retry recursively
+ //  } else {
+ //  console.error("Max retry limit reached. Unable to post data.");
+ //  throw error; // Throw the error after maximum retries
+ //  }
+ //  }
+ // }
 
  await axios
  .post("http://localhost:5000/neural-network", sendData)
@@ -141,7 +141,7 @@ export default function Train() {
  setLoadingProgress(
  training_in_progress || training_progress === 100
  ? training_progress
- : 1
+ : 0
  );
  } catch (error) {
  console.error("Erro ao buscar progresso:", error);
@@ -341,4 +341,4 @@ export default function Train() {
  </div>
  </div>
  );
-}
+}