Add Google Analytics

app.py

@@ -5,12 +5,18 @@ from io import StringIO
 import joblib
 import numpy as np
 import pandas as pd
+
 # page set up
 import streamlit as st
 from b3clf.descriptor_padel import compute_descriptors
 from b3clf.geometry_opt import geometry_optimize
-from b3clf.utils import (get_descriptors, predict_permeability,
-                         scale_descriptors, select_descriptors)
+from b3clf.utils import (
+    get_descriptors,
+    predict_permeability,
+    scale_descriptors,
+    select_descriptors,
+)
+from streamlit_extras.let_it_rain import rain
 from streamlit_ketcher import st_ketcher
 
 st.set_page_config(
@@ -211,7 +217,6 @@ with upload_column:
 # st.write(file)
 
 
-
 feature_column, prediction_column = st.columns(2)
 with feature_column:
     st.subheader("Features")
@@ -230,49 +235,58 @@ with prediction_column:
 
 
 # Generate predictions when the user uploads a file
-if file:
-    temp_dir = tempfile.mkdtemp()
-    # Create a temporary file path for the uploaded file
-    temp_file_path = os.path.join(temp_dir, file.name)
-    # Save the uploaded file to the temporary file path
-    with open(temp_file_path, "wb") as temp_file:
-        temp_file.write(file.read())
-    # X_features, results = generate_predictions(temp_file_path)
-    X_features, results = generate_predictions(
-        input_fname=temp_file_path,
-        sep="\s+|\t+",
-        clf=classifiers_dict[classifier],
-        sampling=resample_methods_dict[resampler],
-        time_per_mol=120,
-    )
-
-    # feture table
-    with feature_column:
-        st.dataframe(X_features)
-        # placeholder_features.dataframe(X_features, hide_index=False)
-        feature_file_name = file.name.split(".")[0] + "_b3clf_features.csv"
-        features_csv = X_features.to_csv(index=True)
-        st.download_button(
-            "Download features as CSV",
-            data=features_csv,
-            file_name=feature_file_name,
+if st.button(label="Submit job", key="submit_job_button", type="secondary"):
+    if file:
+        temp_dir = tempfile.mkdtemp()
+        # Create a temporary file path for the uploaded file
+        temp_file_path = os.path.join(temp_dir, file.name)
+        # Save the uploaded file to the temporary file path
+        with open(temp_file_path, "wb") as temp_file:
+            temp_file.write(file.read())
+        # X_features, results = generate_predictions(temp_file_path)
+        X_features, results = generate_predictions(
+            input_fname=temp_file_path,
+            sep="\s+|\t+",
+            clf=classifiers_dict[classifier],
+            sampling=resample_methods_dict[resampler],
+            time_per_mol=120,
         )
 
-    # prediction table
-    with prediction_column:
-        # st.subheader("Predictions")
-        if results is not None:
-            # Display the predictions in a table
-            st.dataframe(results, hide_index=True)
-            # Add a button to download the predictions as a CSV file
-            predictions_csv = results.to_csv(index=True)
-            results_file_name = file.name.split(".")[0] + "_b3clf_predictions.csv"
+        # feture table
+        with feature_column:
+            st.dataframe(X_features)
+            # placeholder_features.dataframe(X_features, hide_index=False)
+            feature_file_name = file.name.split(".")[0] + "_b3clf_features.csv"
+            features_csv = X_features.to_csv(index=True)
             st.download_button(
-                "Download predictions as CSV",
-                data=predictions_csv,
-                file_name=results_file_name,
+                "Download features as CSV",
+                data=features_csv,
+                file_name=feature_file_name,
             )
 
+        # prediction table
+        with prediction_column:
+            # st.subheader("Predictions")
+            if results is not None:
+                # Display the predictions in a table
+                st.dataframe(results, hide_index=True)
+                # Add a button to download the predictions as a CSV file
+                predictions_csv = results.to_csv(index=True)
+                results_file_name = file.name.split(".")[0] + "_b3clf_predictions.csv"
+                st.download_button(
+                    "Download predictions as CSV",
+                    data=predictions_csv,
+                    file_name=results_file_name,
+                )
+                # indicate the success of the job
+                # rain(
+                #     emoji="🎈",
+                #     font_size=54,
+                #     falling_speed=5,
+                #     animation_length=10,
+                # )
+                st.balloons()
+
 # hide footer
 # https://github.com/streamlit/streamlit/issues/892
 hide_streamlit_style = """
@@ -282,3 +296,19 @@ hide_streamlit_style = """
             </style>
             """
 st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+# add google analytics
+st.markdown(
+    """
+    <!-- Google tag (gtag.js) -->
+    <script async src="https://www.googletagmanager.com/gtag/js?id=G-WG8QYRELP9"></script>
+    <script>
+      window.dataLayer = window.dataLayer || [];
+      function gtag(){dataLayer.push(arguments);}
+      gtag('js', new Date());
+
+      gtag('config', 'G-WG8QYRELP9');
+    </script>
+    """,
+    unsafe_allow_html=True,
+)