add annotation

app.py

@@ -1,5 +1,5 @@
 import torch
-from NeuroPredPLM.predict import predict, batch_predict
+from NeuroPredPLM.predict import predict, batch_predict, batch_predict_beta
 import gradio as gr
 from io import StringIO
 from Bio import SeqIO
@@ -10,7 +10,7 @@ def classifier(peptide_seq):
     for record in SeqIO.parse(handle, 'fasta'):
         data.append((record.id, str(record.seq)))
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    neuropeptide_pred = predict(data, './model.pth', device)
+    neuropeptide_pred = predict(data, '/mnt/d/protein-net/NeuroPred-PLM/NeuroPred-PLM/model.pth', device)
     return neuropeptide_pred
 # {peptide_id:[Type:int(1->neuropeptide,0->non-neuropeptide), attention score:nd.array]}
 
@@ -20,7 +20,7 @@ def batch_classifier(file, cutoff):
     for record in SeqIO.parse(file.name, 'fasta'):
         data.append((record.id, str(record.seq)))
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    neuropeptide_pred = batch_predict(data, cutoff, './model.pth', device)
+    neuropeptide_pred = batch_predict_beta(data, cutoff, '/mnt/d/protein-net/NeuroPred-PLM/NeuroPred-PLM/model.pth', device)
     return neuropeptide_pred
 
 with gr.Blocks() as demo:
@@ -39,6 +39,7 @@ with gr.Blocks() as demo:
                 single_cutoff = gr.Slider(0, 1, step=0.1, value=0.5, interactive=True, label="Threshold")
                 text_button = gr.Button("Submit")
             with gr.Column(scale=2):
+                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.")
                 text_output = gr.outputs.Label(num_top_classes=2, label='Output')
     with gr.Tab("Batch Model"):
         with gr.Row():
@@ -55,6 +56,7 @@ with gr.Blocks() as demo:
                 image_button = gr.Button("Submit")
         with gr.Column():
             # gr.Markdown(" ### Flip text or image files using this demo.")
+            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.")
             frame_output = gr.DataFrame(headers=["Sequence Id", "Sequence", "Probability of neuropeptides", "Neuropeptide"],
             datatype=["str", "str", "str", 'str'],)