Refactor the logic of checking status

app.py

@@ -166,14 +166,6 @@ with upload_column:
                 # on_change=update_uploader_session_info,
             )
 
-            if uploaded_file:
-                # st.write(f"the uploaded file: {uploaded_file}")
-                # when new file is uploaded is different from the previous one
-                if st.session_state.uploaded_file != uploaded_file:
-                    st.session_state.uploaded_file_changed = True
-                else:
-                    st.session_state.uploaded_file_changed = False
-                st.session_state.uploaded_file = uploaded_file
                 # when new file is the same as the previous one
                 # else:
                 #     st.session_state.uploaded_file_changed = False
@@ -232,60 +224,51 @@ st.write(
 # Generate predictions when the user uploads a file
 # if submit_job_button:
 
-# if "job_button" in st.session_state:
-#     when new file is uploaded
-#     update_uploader_session_info()
-#     st.write(
-#         f"the state of uploaded file changed after checking: {st.session_state.uploaded_file_changed}"
-#     )
-#     if st.session_state.uploaded_file_changed:
-#         temp_dir = tempfile.mkdtemp()
-#         # Create a temporary file path for the uploaded file
-#         temp_file_path = os.path.join(temp_dir, uploaded_file.name)
-#         # Save the uploaded file to the temporary file path
-#         with open(temp_file_path, "wb") as temp_file:
-#             temp_file.write(uploaded_file.read())
-
-#         mol_features, info_df, results = generate_predictions(
-#             input_fname=temp_file_path,
-#             sep="\s+|\t+",
-#             clf=classifiers_dict[classifier],
-#             _models_dict=all_models,
-#             sampling=resample_methods_dict[resampler],
-#             time_per_mol=120,
-#             mol_features=None,
-#             info_df=None,
-#         )
-#         st.session_state.mol_features = mol_features
-#         st.session_state.info_df = info_df
-#     else:
-#         mol_features, info_df, results = generate_predictions(
-#             input_fname=None,
-#             sep="\s+|\t+",
-#             clf=classifiers_dict[classifier],
-#             _models_dict=all_models,
-#             sampling=resample_methods_dict[resampler],
-#             time_per_mol=120,
-#             mol_features=st.session_state.mol_features,
-#             info_df=st.session_state.info_df,
-#         )
-if submit_job_button and uploaded_file:
-    temp_dir = tempfile.mkdtemp()
-    # Create a temporary file path for the uploaded file
-    temp_file_path = os.path.join(temp_dir, uploaded_file.name)
-    # Save the uploaded file to the temporary file path
-    with open(temp_file_path, "wb") as temp_file:
-        temp_file.write(uploaded_file.read())
-    mol_features, info_df, results = generate_predictions(
-        input_fname=temp_file_path,
-        sep="\s+|\t+",
-        clf=classifiers_dict[classifier],
-        _models_dict=all_models,
-        sampling=resample_methods_dict[resampler],
-        time_per_mol=120,
-        mol_features=None,
-        info_df=None,
+if submit_job_button:
+    if uploaded_file:
+        # st.write(f"the uploaded file: {uploaded_file}")
+        # when new file is uploaded is different from thprevious one
+        if st.session_state.uploaded_file != uploaded_file:
+            st.session_state.uploaded_file_changed = True
+        else:
+            st.session_state.uploaded_file_changed = False
+        st.session_state.uploaded_file = uploaded_file
+    # when new file is uploaded
+    # update_uploader_session_info()
+    st.write(
+        f"the state of uploaded file changed after checking: {st.session_state.uploaded_file_changed}"
     )
+    if st.session_state.uploaded_file_changed:
+        temp_dir = tempfile.mkdtemp()
+        # Create a temporary file path for the uploaded file
+        temp_file_path = os.path.join(temp_dir, uploaded_file.name)
+        # Save the uploaded file to the temporary file path
+        with open(temp_file_path, "wb") as temp_file:
+            temp_file.write(uploaded_file.read())
+
+        mol_features, info_df, results = generate_predictions(
+            input_fname=temp_file_path,
+            sep="\s+|\t+",
+            clf=classifiers_dict[classifier],
+            _models_dict=all_models,
+            sampling=resample_methods_dict[resampler],
+            time_per_mol=120,
+            mol_features=None,
+            info_df=None,
+        )
+        st.session_state.mol_features = mol_features
+        st.session_state.info_df = info_df
+    else:
+        mol_features, info_df, results = generate_predictions(
+            input_fname=None,
+            sep="\s+|\t+",
+            clf=classifiers_dict[classifier],
+            _models_dict=all_models,
+            sampling=resample_methods_dict[resampler],
+            time_per_mol=120,
+            mol_features=st.session_state.mol_features,
+            info_df=st.session_state.info_df,
+        )
 
     # feture table
     with feature_column:

requirements.txt

@@ -1,6 +1,8 @@
+setuptools==59.8.0
 numpy==1.24.4
 scipy==1.10.1
-scikit-learn==0.24.2
+# scikit-learn==0.24.2
+git+https://github.com/scikit-learn/scikit-learn@0.24.2
 joblib==1.3.2
 pandas==2.0.3
 openpyxl==3.1.2