diff --git a/TTS/demos/xtts_ft_demo/xtts_demo.py b/TTS/demos/xtts_ft_demo/xtts_demo.py
index 662c9c9f..6115c552 100644
--- a/TTS/demos/xtts_ft_demo/xtts_demo.py
+++ b/TTS/demos/xtts_ft_demo/xtts_demo.py
@@ -10,6 +10,7 @@ import numpy as np
 import os
 import torch
 import torchaudio
+import traceback
 from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
 from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt
 
@@ -22,11 +23,12 @@ def clear_gpu_cache():
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
 
-
 XTTS_MODEL = None
 def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
-    clear_gpu_cache()
     global XTTS_MODEL
+    clear_gpu_cache()
+    if not xtts_checkpoint or not xtts_config or not xtts_vocab:
+        return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!"
     config = XttsConfig()
     config.load_json(xtts_config)
     XTTS_MODEL = Xtts.init_from_config(config)
@@ -39,6 +41,9 @@ def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
     return "Model Loaded!"
 
 def run_tts(lang, tts_text, speaker_audio_file):
+    if XTTS_MODEL is None or not speaker_audio_file:
+        return "You need to run the previous step to load the model !!", None, None
+
     gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
     out = XTTS_MODEL.inference(
         text=tts_text,
@@ -57,7 +62,7 @@ def run_tts(lang, tts_text, speaker_audio_file):
         out_path = fp.name
         torchaudio.save(out_path, out["wav"], 24000)
 
-    return out_path, speaker_audio_file
+    return "Speech generated !", out_path, speaker_audio_file
 
 
 
@@ -197,8 +202,7 @@ if __name__ == "__main__":
                 out_path = os.path.join(out_path, "dataset")
                 os.makedirs(out_path, exist_ok=True)
                 if audio_path is None:
-                    # ToDo: raise an error
-                    pass
+                    return "You should provide one or multiple audio files! If you provided it, probably the upload of the files is not finished yet!", "", ""
                 else:
                     train_meta, eval_meta, audio_total_size = format_audio_list(audio_path, target_language=language, out_path=out_path, gradio_progress=progress)
 
@@ -208,7 +212,7 @@ if __name__ == "__main__":
                 if audio_total_size < 120:
                     message = "The sum of the duration of the audios that you provided should be at least 2 minutes!"
                     print(message)
-                    return message, " ", " "
+                    return message, "", ""
 
                 print("Dataset Processed!")
                 return "Dataset Processed!", train_meta, eval_meta
@@ -253,8 +257,14 @@ if __name__ == "__main__":
 
             def train_model(language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path):
                 clear_gpu_cache()
+                if not train_csv or not eval_csv:
+                    return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
+                try:
+                    config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=output_path)
+                except Exception as e:
+                    traceback.print_exc()
+                    return f"The training was interrupted due an error !! Please check the console to check the error message! Error summary: {e}", "", "", "", ""
 
-                config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=output_path)
                 # copy original files to avoid parameters changes issues
                 os.system(f"cp {config_path} {exp_path}")
                 os.system(f"cp {vocab_file} {exp_path}")
@@ -276,8 +286,9 @@ if __name__ == "__main__":
                         label="XTTS config path:",
                         value="",
                     )
+
                     xtts_vocab = gr.Textbox(
-                        label="XTTS config path:",
+                        label="XTTS vocab path:",
                         value="",
                     )
                     progress_load = gr.Label(
@@ -319,6 +330,9 @@ if __name__ == "__main__":
                     tts_btn = gr.Button(value="Step 4 - Inference")
 
                 with gr.Column() as col3:
+                    progress_gen = gr.Label(
+                        label="Progress:"
+                    )
                     tts_output_audio = gr.Audio(label="Generated Audio.")
                     reference_audio = gr.Audio(label="Reference audio used.")
 
@@ -368,7 +382,7 @@ if __name__ == "__main__":
                     tts_text,
                     speaker_reference_audio,
                 ],
-                outputs=[tts_output_audio, reference_audio],
+                outputs=[progress_gen, tts_output_audio, reference_audio],
             )
 
     demo.launch(