Update app.py

app.py

@@ -15,11 +15,11 @@ torch.manual_seed(4)
 np.random.seed(4)
 
 
-def start_fastapi():
+#def start_fastapi():
     subprocess.Popen(["uvicorn", "fastapi_app:app", "--host", "0.0.0.0", "--port", "9999"])
 
 # Start the FastAPI server
-start_fastapi()
+#start_fastapi()
 
 
 
@@ -34,9 +34,9 @@ path = 'ToxicityPrediction/Models/transformers/checkpoint-149-epoch-1'
 
 # Load the model from the stage
 #loaded_model = ClassificationModel('roberta', path, use_cuda = False)
-#----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})
+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() ----
+rob_chem_model.model.eval() 
 #target_name= st.text_input('Enter a SMILES string:')
 target_name = st.text_area("Enter smiles (one per line):", "")
 
@@ -44,7 +44,6 @@ 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(target_name_list)
-    #df_pred = pd.DataFrame({'Smiles':target_name_list,'predictions': predictions})
-    #st.dataframe(df_pred)
-    pass
+    predictions, raw_outputs = rob_chem_model.predict(target_name_list)
+    df_pred = pd.DataFrame({'Smiles':target_name_list,'Predictions': predictions})
+    st.dataframe(df_pred)