Changed text

app.py

@@ -10,29 +10,10 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import gradio as gr
 
-tokenizer = PreTrainedTokenizerFast(tokenizer_file="./tranception/utils/tokenizers/Basic_tokenizer",
-                                                unk_token="[UNK]",
-                                                sep_token="[SEP]",
-                                                pad_token="[PAD]",
-                                                cls_token="[CLS]",
-                                                mask_token="[MASK]"
-                                    )
 #######################################################################################################################################
 ###############################################  HELPER FUNCTIONS  ####################################################################
 #######################################################################################################################################
 
-import torch
-import transformers
-from transformers import PreTrainedTokenizerFast
-import tranception
-import datasets
-from tranception import config, model_pytorch
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import numpy as np
-import gradio as gr
-
 AA_vocab = "ACDEFGHIKLMNPQRSTVWY"
 tokenizer = PreTrainedTokenizerFast(tokenizer_file="./tranception/utils/tokenizers/Basic_tokenizer",
                                                 unk_token="[UNK]",
@@ -166,7 +147,6 @@ def score_and_create_matrix_all_singles(sequence,mutation_range_start=None,mutat
     score_heatmaps.append(create_scoring_matrix_visual(scores,sequence,image_index,window_start,window_end,AA_vocab))
     window_start += max_number_positions_per_heatmap
     window_end = min(mutation_range_end,window_start+max_number_positions_per_heatmap-1)
-  print(score_heatmaps)
   return score_heatmaps, suggest_mutations(scores)
 
 def extract_sequence(example):
@@ -186,7 +166,7 @@ def clear_inputs(protein_sequence_input,mutation_range_start,mutation_range_end)
 tranception_design = gr.Blocks()
 
 with tranception_design:
-    gr.Markdown("# Interactive in silico directed evolution with Tranception")
+    gr.Markdown("# In silico directed evolution for protein redesign with Tranception")
     gr.Markdown(" Perform in silico directed evolution with Tranception to iteratively improve the fitness of a protein of interest, one mutation at a time. At each step, the Tranception model computes the log likelihood ratios of all possible single amino acid substitution Vs the starting sequence, and outputs a fitness heatmap and recommandations to guide the selection of the mutation to apply.")
     
     with gr.Tabs():
@@ -247,7 +227,7 @@ with tranception_design:
     gr.Markdown("<br>")
     gr.Markdown("# Fitness predictions for all single amino acid substitutions in mutation range")
     gr.Markdown("Inference may take a few seconds for short proteins & mutation ranges to several minutes for longer ones")
-    output_image = gr.Gallery(label="Fitness predictions for all single amino acid substitutions in mutation range",type="filepath") #Using Gallery to be able to scroll large matrix images
+    output_image = gr.Gallery(label="Fitness predictions for all single amino acid substitutions in mutation range",type="filepath") #Using Gallery to break down large scoring matrices into smaller images
     
     output_recommendations = gr.Textbox(label="Mutation recommendations")