decorator test

appStore/sdg_analysis.py

@@ -44,40 +44,37 @@ def app():
 
 
     with st.container():
-        if check_streamlit():
-            st.write(True)
-        else:
-            st.write(False)       
+       
             
-        # if 'filepath' in st.session_state:
-        #     paraList = runSDGPreprocessingPipeline()
-        #     if len(paraList) > 150:
-        #         warning_msg = ": This might take sometime, please sit back and relax."
-        #     else:
-        #         warning_msg = ""
-
-        #     with st.spinner("Running SDG Classification{}".format(warning_msg)):
-
-        #         df, x = sdg_classification(paraList)
-
-        #         plt.rcParams['font.size'] = 25
-        #         colors = plt.get_cmap('Blues')(np.linspace(0.2, 0.7, len(x)))
-        #         # plot
-        #         fig, ax = plt.subplots()
-        #         ax.pie(x, colors=colors, radius=2, center=(4, 4),
-        #             wedgeprops={"linewidth": 1, "edgecolor": "white"}, 
-        #             frame=False,labels =list(x.index))
-        #         # fig.savefig('temp.png', bbox_inches='tight',dpi= 100)
-        #         st.markdown("#### Anything related to SDGs? ####")
-
-        #         c4, c5, c6 = st.columns([2, 2, 2])
-
-        #         with c5:
-        #             st.pyplot(fig)
+        if 'filepath' in st.session_state:
+            paraList = runSDGPreprocessingPipeline()
+            if len(paraList) > 150:
+                warning_msg = ": This might take sometime, please sit back and relax."
+            else:
+                warning_msg = ""
+
+            with st.spinner("Running SDG Classification{}".format(warning_msg)):
+
+                df, x = sdg_classification(paraList)
+
+                plt.rcParams['font.size'] = 25
+                colors = plt.get_cmap('Blues')(np.linspace(0.2, 0.7, len(x)))
+                # plot
+                fig, ax = plt.subplots()
+                ax.pie(x, colors=colors, radius=2, center=(4, 4),
+                    wedgeprops={"linewidth": 1, "edgecolor": "white"}, 
+                    frame=False,labels =list(x.index))
+                # fig.savefig('temp.png', bbox_inches='tight',dpi= 100)
+                st.markdown("#### Anything related to SDGs? ####")
+
+                c4, c5, c6 = st.columns([2, 2, 2])
+
+                with c5:
+                    st.pyplot(fig)
                     
-        #         c7, c8, c9 = st.columns([1, 10, 1])
-        #         with c8:
-        #             st.table(df)
+                c7, c8, c9 = st.columns([1, 10, 1])
+                with c8:
+                    st.table(df)
 
 
 #     1. Keyword heatmap \n

utils/sdg_classifier.py

@@ -27,24 +27,6 @@ def load_sdgClassifier():
         task="text-classification")
     return doc_classifier
 
-def check_streamlit():
-    """
-    Function to check whether python code is run within streamlit
-
-    Returns
-    -------
-    use_streamlit : boolean
-        True if code is run within streamlit, else False
-    """
-    try:
-        from streamlit.scriptrunner.script_run_context import get_script_run_ctx
-        if not get_script_run_ctx():
-            use_streamlit = False
-        else:
-            use_streamlit = True
-    except ModuleNotFoundError:
-        use_streamlit = False
-    return use_streamlit
 
 def sdg_classification(haystackdoc:List[Document])->Tuple[DataFrame,Series]:
     """

utils/streamlitcheck.py

@@ -1,2 +1,19 @@
+def check_streamlit():
+    """
+    Function to check whether python code is run within streamlit
 
+    Returns
+    -------
+    use_streamlit : boolean
+        True if code is run within streamlit, else False
+    """
+    try:
+        from streamlit.scriptrunner.script_run_context import get_script_run_ctx
+        if not get_script_run_ctx():
+            use_streamlit = False
+        else:
+            use_streamlit = True
+    except ModuleNotFoundError:
+        use_streamlit = False
+    return use_streamlit