sdg info

appStore/sdg_analysis.py

@@ -44,9 +44,10 @@ def app():
             of a “context” and should limit the paragraph length deviation. \
             However, since we want to respect the sentence boundary the limit \
             can breach and hence this limit of 120 is tentative.  \n
-            **SDG cLassification:** The application assigns paragraphs to 15 of \
-            the 17 United Nations Sustainable Development Goals (SDGs). SDG 16 \
-            “Peace, Justice and Strong Institutions” and SDG 17 \
+            """
+        st.write("""**SDG cLassification:** The application assigns paragraphs \
+            to 15 of the 17 United Nations Sustainable Development Goals (SDGs).\
+            SDG 16 “Peace, Justice and Strong Institutions” and SDG 17 \
             “Partnerships for the Goals” are excluded from the analysis due to \
             their broad nature which could potentially inflate the results. \
             Each paragraph is assigned to one SDG only. Again, the results are \
@@ -60,12 +61,13 @@ def app():
             partnerships and growing community of researchers and institutions \
             interested in the classification of research according to the \
             Sustainable Development Goals. The summary table only displays \
-            paragraphs with a calculated relevancy score above 85%.  \n
-            **Keyphrase Extraction:** The application extracts 15 keyphrases from \
-            the document, calculates a respective relevancy score, and displays \
-            the results in a summary table. The keyphrases are extracted using \
-            using [Textrank](https://github.com/summanlp/textrank) which is an \
-            easy-to-use computational less expensive \
+            paragraphs with a calculated relevancy score above 85%.  \n""")
+            
+        st.write("""**Keyphrase Extraction:** The application extracts 15 \
+            keyphrases from the document, calculates a respective relevancy \
+            score, and displays the results in a summary table. The keyphrases \
+            are extracted using using [Textrank](https://github.com/summanlp/textrank)\
+            which is an easy-to-use computational less expensive \
             model leveraging combination of TFIDF and Graph networks.
             """)
         st.markdown("")