PascalNotin commited on
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10d0895
1 Parent(s): 8c638cc

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  1. app.py +2 -22
app.py CHANGED
@@ -10,29 +10,10 @@ import matplotlib.pyplot as plt
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  import seaborn as sns
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  import gradio as gr
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- tokenizer = PreTrainedTokenizerFast(tokenizer_file="./tranception/utils/tokenizers/Basic_tokenizer",
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- unk_token="[UNK]",
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- sep_token="[SEP]",
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- pad_token="[PAD]",
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- cls_token="[CLS]",
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- mask_token="[MASK]"
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- )
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  #######################################################################################################################################
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  ############################################### HELPER FUNCTIONS ####################################################################
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  #######################################################################################################################################
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- import torch
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- import transformers
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- from transformers import PreTrainedTokenizerFast
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- import tranception
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- import datasets
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- from tranception import config, model_pytorch
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- import pandas as pd
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- import matplotlib.pyplot as plt
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- import seaborn as sns
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- import numpy as np
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- import gradio as gr
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-
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  AA_vocab = "ACDEFGHIKLMNPQRSTVWY"
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  tokenizer = PreTrainedTokenizerFast(tokenizer_file="./tranception/utils/tokenizers/Basic_tokenizer",
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  unk_token="[UNK]",
@@ -166,7 +147,6 @@ def score_and_create_matrix_all_singles(sequence,mutation_range_start=None,mutat
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  score_heatmaps.append(create_scoring_matrix_visual(scores,sequence,image_index,window_start,window_end,AA_vocab))
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  window_start += max_number_positions_per_heatmap
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  window_end = min(mutation_range_end,window_start+max_number_positions_per_heatmap-1)
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- print(score_heatmaps)
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  return score_heatmaps, suggest_mutations(scores)
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  def extract_sequence(example):
@@ -186,7 +166,7 @@ def clear_inputs(protein_sequence_input,mutation_range_start,mutation_range_end)
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  tranception_design = gr.Blocks()
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  with tranception_design:
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- gr.Markdown("# Interactive in silico directed evolution with Tranception")
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  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.")
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  with gr.Tabs():
@@ -247,7 +227,7 @@ with tranception_design:
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  gr.Markdown("<br>")
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  gr.Markdown("# Fitness predictions for all single amino acid substitutions in mutation range")
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  gr.Markdown("Inference may take a few seconds for short proteins & mutation ranges to several minutes for longer ones")
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- 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
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  output_recommendations = gr.Textbox(label="Mutation recommendations")
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  import seaborn as sns
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  import gradio as gr
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  #######################################################################################################################################
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  ############################################### HELPER FUNCTIONS ####################################################################
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  #######################################################################################################################################
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  AA_vocab = "ACDEFGHIKLMNPQRSTVWY"
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  tokenizer = PreTrainedTokenizerFast(tokenizer_file="./tranception/utils/tokenizers/Basic_tokenizer",
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  unk_token="[UNK]",
 
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  score_heatmaps.append(create_scoring_matrix_visual(scores,sequence,image_index,window_start,window_end,AA_vocab))
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  window_start += max_number_positions_per_heatmap
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  window_end = min(mutation_range_end,window_start+max_number_positions_per_heatmap-1)
 
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  return score_heatmaps, suggest_mutations(scores)
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  def extract_sequence(example):
 
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  tranception_design = gr.Blocks()
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  with tranception_design:
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+ gr.Markdown("# In silico directed evolution for protein redesign with Tranception")
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  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.")
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  with gr.Tabs():
 
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  gr.Markdown("<br>")
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  gr.Markdown("# Fitness predictions for all single amino acid substitutions in mutation range")
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  gr.Markdown("Inference may take a few seconds for short proteins & mutation ranges to several minutes for longer ones")
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+ 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
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  output_recommendations = gr.Textbox(label="Mutation recommendations")
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