Update app.py

app.py

@@ -21,9 +21,12 @@ path = 'ToxicityPrediction/Models/transformers/checkpoint-149-epoch-1'
 rob_chem_model = ClassificationModel('roberta', 'seyonec/SMILES_tokenized_PubChem_shard00_160k',use_cuda=False ,args={'evaluate_each_epoch':True , 'evaluate_during_training_verbose':True, 'seed':4})
 # Predict based on the input
 rob_chem_model.model.eval()
-target_name= st.text_input('Enter a SMILES string:')
+#target_name= st.text_input('Enter a SMILES string:')
+target_name = st.text_area("Enter smiles (one per line):", "")
+target_name_list = target_name.splitlines()
+target_name_list = [x.strip() for x in target_name_list]
 predict_toxicity = st.button('Predict Toxicity')
 if predict_toxicity:
-    predictions, raw_outputs = rob_chem_model.predict([str(target_name)])
-    df_pred = pd.DataFrame({'predictions': predictions, 'raw_outputs_lowerbound': raw_outputs[0][0], 'raw_outputs_upperbound': raw_outputs[0][0]})
+    predictions, raw_outputs = rob_chem_model.predict(target_name_list)
+    df_pred = pd.DataFrame({'Smiles':target_name_list,'predictions': predictions})
     st.dataframe(df_pred)