Add temp patch

app.py

@@ -238,37 +238,53 @@ if "job_button" in st.session_state:
     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 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,
+    #     )
+    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,
+    )
 
     # feture table
     with feature_column: