From c37d1c9c268e4ca8b0933de73502ffcefa6d42b5 Mon Sep 17 00:00:00 2001
From: Lenoctambule <106790775+lenoctambule@users.noreply.github.com>
Date: Fri, 27 Mar 2026 22:16:17 +0100
Subject: [PATCH] refactor: use tqdm instead of custom load bar

---
 autoencoder.py   | 53 +++++++++++++++++++++++++-----------------------
 requirements.txt |  3 ++-
 2 files changed, 30 insertions(+), 26 deletions(-)

diff --git a/autoencoder.py b/autoencoder.py
index 982a798..9f2e7e9 100644
--- a/autoencoder.py
+++ b/autoencoder.py
@@ -4,6 +4,7 @@ from utils import (regularize,
                    dynamic_loss_plot_update,
                    dynamic_loss_plot_finish)
 import types
+from tqdm import tqdm
 
 LOADER = ['⡿', '⣟', '⣯', '⣷', '⣾', '⣽', '⣻', '⢿']
 
@@ -88,32 +89,34 @@ class Autoencoder:
         epoch = 0
         no_improv = 0
         prev_error = float('inf')
-        while True:
-            print(
-                f"{LOADER[epoch % len(LOADER)]} Training \t({epoch=} error={prev_error:.2f})", # noqa
-            )
-            error = 0
-            for x in data_set:
-                input = x.flatten()
-                error += self.train(input)
-            error /= len(data_set)
-            if prev_error - error <= 1e-8:
-                no_improv += 1
-            else:
-                no_improv = 0
-            prev_error = float(error)
-            losses.append(error)
+        with tqdm(bar_format="{desc} {elapsed} {rate_fmt}") as lbar :
+            while True:
+                lbar.set_description(
+                    f"{LOADER[epoch % len(LOADER)]} Training ({epoch=} error={prev_error:.2f})",
+                )
+                lbar.update()
+                error = 0
+                for x in data_set:
+                    input = x.flatten()
+                    error += self.train(input)
+                error /= len(data_set)
+                if prev_error - error <= 1e-8:
+                    no_improv += 1
+                else:
+                    no_improv = 0
+                prev_error = float(error)
+                losses.append(error)
+                if display_loss is True:
+                    dynamic_loss_plot_update(ax, line, losses)
+                if no_improv > patience:
+                    break
+                if epoch > max_epoch:
+                    break
+                epoch += 1
             if display_loss is True:
-                dynamic_loss_plot_update(ax, line, losses)
-            if no_improv > patience:
-                break
-            if epoch > max_epoch:
-                break
-            epoch += 1
-        if display_loss is True:
-            dynamic_loss_plot_finish(ax, line)
-        print("#Training complete !")
-        return losses
+                dynamic_loss_plot_finish(ax, line)
+            print("#Training complete !")
+            return losses
 
     def encode(self, v: np.ndarray) -> np.ndarray:
         return self.encoder.forward(v)
diff --git a/requirements.txt b/requirements.txt
index 9144eaa..e53d596 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
 numpy
 matplotlib
-requests
\ No newline at end of file
+requests
+tqdm
\ No newline at end of file