Update app.py

app.py

@@ -7,7 +7,6 @@ import re
 import numpy as np
 import os
 import shutil
-from IPython.display import FileLink
 from huggingface_hub import hf_hub_download
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from sklearn.utils.class_weight import compute_class_weight
@@ -18,7 +17,7 @@ print(f"Using device: {device}")
 
 # Model and dataset paths
 model_name = "Fredaaaaaa/hybrid_model"
-output_dir = "/kaggle/working/drug_interaction_model"
+output_dir = "/home/user/app/drug_interaction_model"
 
 # Create output directory
 os.makedirs(output_dir, exist_ok=True)
@@ -49,12 +48,10 @@ with open(os.path.join(output_dir, 'label_encoder.pkl'), 'wb') as f:
 df.to_csv(os.path.join(output_dir, 'labeled_severity.csv'), index=False)
 
 # Create zip archive
-zip_path = "/kaggle/working/drug_interaction_model.zip"
-shutil.make_archive("/kaggle/working/drug_interaction_model", 'zip', output_dir)
+zip_path = "/home/user/app/drug_interaction_model.zip"
+shutil.make_archive("/home/user/app/drug_interaction_model", 'zip', output_dir)
 print(f"📦 Model saved and zipped at: {zip_path}")
-
-# Provide download link
-display(FileLink('drug_interaction_model.zip'))
+print(f"To download, access the file at: {zip_path} from your environment or server.")
 
 # Compute class weights
 unique_classes = df['severity'].unique()
@@ -139,6 +136,7 @@ def get_smiles_from_api(drug_name):
             if 'PropertyTable' in smiles_data and 'Properties' in smiles_data['PropertyTable']:
                 properties = smiles_data['PropertyTable']['Properties']
                 if properties and 'CanonicalSMILES' in properties[0]:
+                    print(f"SMILES found for {drug_name}: {properties[0]['CanonicalSMILES']}")
                     return properties[0]['CanonicalSMILES']
         print(f"No SMILES found for drug {drug_name}")
         return None