Update app.py

app.py

@@ -7,11 +7,12 @@ import torch
 from torch.utils.data import DataLoader
 from transformers import AutoTokenizer
 
-sys.path.append("/home/user/app/")
-from scripts.foldseek_util import get_struc_seq
-from scripts.utils import seed_everything
-from scripts.models import PLTNUM_PreTrainedModel
-from scripts.datasets import PLTNUMDataset
+sys.path.append("/home/user/app/scripts")
+from foldseek_util import get_struc_seq
+from utils import seed_everything
+from models import PLTNUM_PreTrainedModel
+from datasets import PLTNUMDataset
+
 
 class Config:
     batch_size = 2
@@ -23,6 +24,7 @@ class Config:
     task = "classification"
     sequence_col = "sequence"
 
+
 # Assuming 'predict_stability' is your function that predicts protein stability
 def predict_stability(cfg, model_choice, organism_choice, pdb_file=None, sequence=None):
     # Check if pdb_file is provided
@@ -50,7 +52,7 @@ def predict_stability(cfg, model_choice, organism_choice, pdb_file=None, sequenc
         return f"Predicted Stability using {model_choice} for {organism_choice}: Example Output with sequence {output}..."
     else:
         return "No valid input provided."
-    
+
 
 def get_foldseek_seq(pdb_path):
     parsed_seqs = get_struc_seq(
@@ -108,8 +110,7 @@ def predict(cfg, sequence):
     outputs = {}
     outputs["raw prediction values"] = predictions
     outputs["binary prediction values"] = [1 if x > 0.5 else 0 for x in predictions]
-    return outputs   
-
+    return outputs
 
 
 # Gradio Interface
@@ -120,32 +121,41 @@ with gr.Blocks() as demo:
         **Predict the protein half-life from its sequence or PDB file.**
         """
     )
-    
-    gr.Image("https://github.com/sagawatatsuya/PLTNUM/blob/main/model-image.png?raw=true", label="Model Image")
+
+    gr.Image(
+        "https://github.com/sagawatatsuya/PLTNUM/blob/main/model-image.png?raw=true",
+        label="Model Image",
+    )
 
     # Model and Organism selection in the same row to avoid layout issues
     with gr.Row():
         model_choice = gr.Radio(
-            choices=["SaProt", "ESM2"], 
-            label="Select PLTNUM's base model.", 
-            value="SaProt"
+            choices=["SaProt", "ESM2"],
+            label="Select PLTNUM's base model.",
+            value="SaProt",
         )
         organism_choice = gr.Radio(
-            choices=["Mouse", "Human"], 
-            label="Select the target organism.", 
-            value="Mouse"
+            choices=["Mouse", "Human"],
+            label="Select the target organism.",
+            value="Mouse",
         )
-    
+
     with gr.Tabs():
         with gr.TabItem("Upload PDB File"):
             gr.Markdown("### Upload your PDB file:")
             pdb_file = gr.File(label="Upload PDB File")
-            
+
             predict_button = gr.Button("Predict Stability")
-            prediction_output = gr.Textbox(label="Stability Prediction", interactive=False)
+            prediction_output = gr.Textbox(
+                label="Stability Prediction", interactive=False
+            )
+
+            predict_button.click(
+                fn=predict_stability,
+                inputs=[model_choice, organism_choice, pdb_file],
+                outputs=prediction_output,
+            )
 
-            predict_button.click(fn=predict_stability, inputs=[model_choice, organism_choice, pdb_file], outputs=prediction_output)
-        
         with gr.TabItem("Enter Protein Sequence"):
             gr.Markdown("### Enter the protein sequence:")
             sequence = gr.Textbox(
@@ -154,10 +164,16 @@ with gr.Blocks() as demo:
                 lines=8,
             )
             predict_button = gr.Button("Predict Stability")
-            prediction_output = gr.Textbox(label="Stability Prediction", interactive=False)
+            prediction_output = gr.Textbox(
+                label="Stability Prediction", interactive=False
+            )
+
+            predict_button.click(
+                fn=predict_stability,
+                inputs=[model_choice, organism_choice, sequence],
+                outputs=prediction_output,
+            )
 
-            predict_button.click(fn=predict_stability, inputs=[model_choice, organism_choice, sequence], outputs=prediction_output)
-    
     gr.Markdown(
         """
         ### How to Use:
@@ -168,7 +184,7 @@ with gr.Blocks() as demo:
         - **Predict**: Click 'Predict Stability' to receive the prediction.
         """
     )
-    
+
     gr.Markdown(
         """
         ### About the Tool