statistics

app.py

@@ -23,30 +23,30 @@ with st.container():
         st.markdown("<h2 style='text-align: center; color: black;'> Climate Policy Intelligence App </h2>", unsafe_allow_html=True)
         st.write(' ')
 
-with st.expander("ℹ️ - About this app", expanded=False):
-    st.write(
-        """
-        Climate Policy Understanding App is an open-source\
-        digital tool which aims to assist policy analysts and \
-        other users in extracting and filtering relevant \
-        information from public documents.
-
-        What Happens in background?
-
-        - Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
-        In this step the document is broken into smaller paragraphs \
-        (based on word/sentence count).
-        - Step 2: The paragraphs are fed to **Target Classifier** which detects if
-        the paragraph contains any *Target* related information or not.
-        - Step 3: The paragraphs which are detected containing some target \
-        related information are then fed to multiple classifier to enrich the 
-        Information Extraction.
-
-        Classifiers
-        - Netzero: 
+# with st.expander("ℹ️ - About this app", expanded=False):
+#     st.write(
+#         """
+#         Climate Policy Understanding App is an open-source\
+#         digital tool which aims to assist policy analysts and \
+#         other users in extracting and filtering relevant \
+#         information from public documents.
+
+#         What Happens in background?
+
+#         - Step 1: Once the document is provided to app, it undergoes *Pre-processing*.\
+#         In this step the document is broken into smaller paragraphs \
+#         (based on word/sentence count).
+#         - Step 2: The paragraphs are fed to **Target Classifier** which detects if
+#         the paragraph contains any *Target* related information or not.
+#         - Step 3: The paragraphs which are detected containing some target \
+#         related information are then fed to multiple classifier to enrich the 
+#         Information Extraction.
+
+#         Classifiers
+#         - Netzero: 
                
-        """)
-    st.write("")
+#         """)
+#     st.write("")
 apps = [processing.app, target_extraction.app, netzero.app, ghg.app,
         sector.app, adapmit.app]
 multiplier_val =100/len(apps)