Create app.py

app.py

@@ -0,0 +1,25 @@
+import streamlit as st
+import pandas as pd
+from simpletransformers.classification import ClassificationModel
+
+import os
+import numpy as np
+
+np.random.seed(123)
+
+# Set the Streamlit app title
+st.title("Molecule Toxicity Predictions")
+
+# Set the model path
+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, })
+# Predict based on the input
+target_name= st.text_input('Enter a SMILES string:')
+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]})
+    st.dataframe(df_pred)