wnagleiofficial commited on
Commit
45b7faf
·
1 Parent(s): 9d54723
Files changed (1) hide show
  1. app.py +4 -4
app.py CHANGED
@@ -10,7 +10,7 @@ def classifier(peptide_seq):
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  for record in SeqIO.parse(handle, 'fasta'):
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  data.append((record.id, str(record.seq)))
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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- neuropeptide_pred = predict(data, '/mnt/d/protein-net/NeuroPred-PLM/NeuroPred-PLM/model.pth', device)
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  return neuropeptide_pred
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  # {peptide_id:[Type:int(1->neuropeptide,0->non-neuropeptide), attention score:nd.array]}
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@@ -20,7 +20,7 @@ def batch_classifier(file, cutoff):
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  for record in SeqIO.parse(file.name, 'fasta'):
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  data.append((record.id, str(record.seq)))
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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- neuropeptide_pred = batch_predict(data, cutoff, '/mnt/d/protein-net/NeuroPred-PLM/NeuroPred-PLM/model.pth', device)
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  return neuropeptide_pred
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  with gr.Blocks() as demo:
@@ -39,7 +39,7 @@ with gr.Blocks() as demo:
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  single_cutoff = gr.Slider(0, 1, step=0.1, value=0.5, interactive=True, label="Threshold")
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  text_button = gr.Button("Submit")
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  with gr.Column(scale=2):
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- gr.Markdown("#### The larger the probability score of neuropeptide output, the more likely it is to belong to neuropeptide. Generally, result greater than the threshold (default:0.5) is considered to belong to neuropeptides.")
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  text_output = gr.outputs.Label(num_top_classes=2, label='Output')
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  with gr.Tab("Batch Model"):
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  with gr.Row():
@@ -56,7 +56,7 @@ with gr.Blocks() as demo:
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  image_button = gr.Button("Submit")
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  with gr.Column():
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  # gr.Markdown(" ### Flip text or image files using this demo.")
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- gr.Markdown("#### The larger the probability score of neuropeptide output, the more likely it is to belong to neuropeptide. Generally, result greater than the threshold (default:0.5) is considered to belong to neuropeptides.")
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  frame_output = gr.DataFrame(headers=["Sequence Id", "Sequence", "Probability of neuropeptides", "Neuropeptide"],
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  datatype=["str", "str", "str", 'str'],)
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  for record in SeqIO.parse(handle, 'fasta'):
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  data.append((record.id, str(record.seq)))
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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+ neuropeptide_pred = predict(data, './model.pth', device)
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  return neuropeptide_pred
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  # {peptide_id:[Type:int(1->neuropeptide,0->non-neuropeptide), attention score:nd.array]}
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  for record in SeqIO.parse(file.name, 'fasta'):
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  data.append((record.id, str(record.seq)))
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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+ neuropeptide_pred = batch_predict(data, cutoff, './model.pth', device)
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  return neuropeptide_pred
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  with gr.Blocks() as demo:
 
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  single_cutoff = gr.Slider(0, 1, step=0.1, value=0.5, interactive=True, label="Threshold")
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  text_button = gr.Button("Submit")
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  with gr.Column(scale=2):
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+ gr.Markdown("Note: the larger the probability score of neuropeptide output, the more likely it is to belong to neuropeptide. Generally, result greater than the threshold (default:0.5) is considered to belong to neuropeptides.")
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  text_output = gr.outputs.Label(num_top_classes=2, label='Output')
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  with gr.Tab("Batch Model"):
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  with gr.Row():
 
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  image_button = gr.Button("Submit")
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  with gr.Column():
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  # gr.Markdown(" ### Flip text or image files using this demo.")
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+ gr.Markdown("Note: the larger the probability score of neuropeptide output, the more likely it is to belong to neuropeptide. Generally, result greater than the threshold (default:0.5) is considered to belong to neuropeptides.")
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  frame_output = gr.DataFrame(headers=["Sequence Id", "Sequence", "Probability of neuropeptides", "Neuropeptide"],
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  datatype=["str", "str", "str", 'str'],)
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