diff --git a/colabfold/plot.py b/colabfold/plot.py
index f5b6dd76..3121f4a6 100644
--- a/colabfold/plot.py
+++ b/colabfold/plot.py
@@ -1,10 +1,14 @@
 from pathlib import Path
 import numpy as np
 
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+
 def plot_predicted_alignment_error(
     jobname: str, num_models: int, outs: dict, result_dir: Path, show: bool = False
 ):
-    from matplotlib import pyplot as plt
     plt.figure(figsize=(3 * num_models, 2), dpi=100)
     for n, (model_name, value) in enumerate(outs.items()):
         plt.subplot(1, num_models, n + 1)
@@ -18,17 +22,16 @@ def plot_predicted_alignment_error(
 
 
 def plot_msa_v2(feature_dict, sort_lines=True, dpi=100):
-    from matplotlib import pyplot as plt
     seq = feature_dict["msa"][0]
     if "asym_id" in feature_dict:
-      Ls = [0]
-      k = feature_dict["asym_id"][0]
-      for i in feature_dict["asym_id"]:
-        if i == k: Ls[-1] += 1
-        else: Ls.append(1)
-        k = i
+        Ls = [0]
+        k = feature_dict["asym_id"][0]
+        for i in feature_dict["asym_id"]:
+            if i == k: Ls[-1] += 1
+            else: Ls.append(1)
+            k = i
     else:
-      Ls = [len(seq)]    
+        Ls = [len(seq)]    
     Ln = np.cumsum([0] + Ls)
 
     try:
@@ -58,6 +61,7 @@ def plot_msa_v2(feature_dict, sort_lines=True, dpi=100):
     
     Nn = np.cumsum(np.append(0,Nn))
     lines = np.concatenate(lines,0)
+    
     plt.figure(figsize=(8,5), dpi=dpi)
     plt.title("Sequence coverage")
     plt.imshow(lines,
@@ -78,11 +82,11 @@ def plot_msa_v2(feature_dict, sort_lines=True, dpi=100):
     return plt
 
 def plot_msa(msa, query_sequence, seq_len_list, total_seq_len, dpi=100):
-    from matplotlib import pyplot as plt
     # gather MSA info
     prev_pos = 0
     msa_parts = []
-    Ln = np.cumsum(np.append(0, [len for len in seq_len_list]))
+    # Corrección: Cambiado 'len' a 'l' para no sobrescribir la función integrada
+    Ln = np.cumsum(np.append(0, [l for l in seq_len_list]))
     for id, l in enumerate(seq_len_list):
         chain_seq = np.array(query_sequence[prev_pos : prev_pos + l])
         chain_msa = np.array(msa[:, prev_pos : prev_pos + l])
@@ -97,6 +101,7 @@ def plot_msa(msa, query_sequence, seq_len_list, total_seq_len, dpi=100):
         non_gaps[non_gaps == 0] = np.nan
         msa_parts.append((non_gaps[:] * seqid[:, None]).tolist())
         prev_pos += l
+        
     lines = []
     lines_to_sort = []
     prev_has_seq = [True] * len(seq_len_list)
@@ -120,12 +125,11 @@ def plot_msa(msa, query_sequence, seq_len_list, total_seq_len, dpi=100):
                 line += msa_parts[id][line_num]
             lines_to_sort.append(line)
         prev_has_seq = has_seq
+        
     lines_to_sort = np.array(lines_to_sort)
     lines_to_sort = lines_to_sort[np.argsort(-np.nanmax(lines_to_sort, axis=1))]
     lines += lines_to_sort.tolist()
 
-    # Nn = np.cumsum(np.append(0, Nn))
-    # lines = np.concatenate(lines, 1)
     xaxis_size = len(lines[0])
     yaxis_size = len(lines)
 
@@ -143,10 +147,6 @@ def plot_msa(msa, query_sequence, seq_len_list, total_seq_len, dpi=100):
     )
     for i in Ln[1:-1]:
         plt.plot([i, i], [0, yaxis_size], color="black")
-    # for i in Ln_dash[1:-1]:
-    #    plt.plot([i, i], [0, lines.shape[0]], "--", color="black")
-    # for j in Nn[1:-1]:
-    #    plt.plot([0, lines.shape[1]], [j, j], color="black")
 
     plt.plot((np.isnan(lines) == False).sum(0), color="black")
     plt.xlim(0, xaxis_size)