diff --git a/api/Neural_Network2.py b/api/Neural_Network2.py
index 8bcb1b5a..09a8cf63 100644
--- a/api/Neural_Network2.py
+++ b/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)}"
 
-
diff --git a/api/app.py b/api/app.py
index 64b6fdaa..d480e789 100644
--- a/api/app.py
+++ b/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()
\ No newline at end of file
diff --git a/src/pages/train.tsx b/src/pages/train.tsx
index 1a1373b2..6f83486b 100644
--- a/src/pages/train.tsx
+++ b/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>
   );
-}
+}
\ No newline at end of file