fixed documentation formatting

Author: anngoroshi

test2text/pages/documentation.py

@@ -7,72 +7,72 @@ def show_documentation():
 
                     ## About the Application
                 
-                        **Test2Text** is a tool for computing requirement's coverage by tests and generating relevant reports.  
-                        The application provides a convenient interface for analysis the relationships between test cases and requirements.
+                    **Test2Text** is a tool for computing requirement's coverage by tests and generating relevant reports.  
+                    The application provides a convenient interface for analysis the relationships between test cases and requirements.
                 """)
     st.divider()
     st.markdown("""
                     ## HOW TO USE
                     
                     ### Upload data
-                        Click :gray-badge[:material/database_upload: Annotations] or :gray-badge[:material/database_upload: Requirements] to upload annotations and requirements from CSV files to the app's database. 
+                    Click :gray-badge[:material/database_upload: Annotations] or :gray-badge[:material/database_upload: Requirements] to upload annotations and requirements from CSV files to the app's database. 
                     
                     ### Renew data
-                        Click :gray-badge[:material/cached: Controls] to transform missed and new texts into numeral vectors (embeddings).  
-                        Update distances by embeddings for intelligent matching of requirements and annotations.
+                    Click :gray-badge[:material/cached: Controls] to transform missed and new texts into numeral vectors (embeddings).  
+                    Update distances by embeddings for intelligent matching of requirements and annotations.
                     
                     ### Generate reports
-                        Click :gray-badge[:material/publish: Requirement's Report] or :gray-badge[:material/publish: Test cases Report] to make a report.  
-                        Use filters to select desired information. Analyze selected requirements or test cases by showed and plotted distances
+                    Click :gray-badge[:material/publish: Requirement's Report] or :gray-badge[:material/publish: Test cases Report] to make a report.  
+                    Use filters to select desired information. Analyze selected requirements or test cases by showed and plotted distances
                     
                     ### Visualize saved data 
-                        Click :gray-badge[:material/dataset: Visualize vectors] to plot distances between vector representations of all requirements and annotations.
+                    Click :gray-badge[:material/dataset: Visualize vectors] to plot distances between vector representations of all requirements and annotations.
                 
                 """)
     st.divider()
     st.markdown("""    
                     ### Methodology
-                        The application use a pre-trained transformer model from the [sentence-transformers library](https://huggingface.co/sentence-transformers), specifically [nomic-ai/nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1), a model trained to produce high-quality vector embeddings for text.  
-                        The model returns, for each input text, a high-dimensional NumPy array (vector) of floating point numbers (the embedding).  
-                        This arrays give us a possibility to calculate Euclidian distances between test cases annotations and requirements to view how similar or dissimilar the two texts.  
+                    The application use a pre-trained transformer model from the [sentence-transformers library](https://huggingface.co/sentence-transformers), specifically [nomic-ai/nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1), a model trained to produce high-quality vector embeddings for text.  
+                    The model returns, for each input text, a high-dimensional NumPy array (vector) of floating point numbers (the embedding).  
+                    This arrays give us a possibility to calculate Euclidian distances between test cases annotations and requirements to view how similar or dissimilar the two texts.  
                    """)
 
     st.markdown("""     
                     #### Euclidean (L2) Distance Formula
-                        The Euclidean (L2) distance is a measure of the straight-line distance between two points (or vectors) in a multidimensional space.  
-                        It is widely used to compute the similarity or dissimilarity between two vector representations, such as text embeddings.
+                    The Euclidean (L2) distance is a measure of the straight-line distance between two points (or vectors) in a multidimensional space.  
+                    It is widely used to compute the similarity or dissimilarity between two vector representations, such as text embeddings.
                     """)
     st.markdown("""
-                        Suppose we have two vectors:
-                    """)
-    st.latex(r"""
-                        [ \mathbf{a} = [a_1, a_2, ..., a_n] ],
+                    Suppose we have two vectors:
                 """)
     st.latex(r"""
-                        [ \mathbf{b} = [b_1, b_2, ..., b_n] ]
-                """)
+                    [ \mathbf{a} = [a_1, a_2, ..., a_n] ],
+            """)
+    st.latex(r"""
+                    [ \mathbf{b} = [b_1, b_2, ..., b_n] ]
+            """)
 
     st.markdown("""
-    The L2 distance between **a** and **b** is calculated as:
-    """)
+                    The L2 distance between **a** and **b** is calculated as:
+                """)
 
     st.latex(r"""
-    [ L_2(\mathbf{a}, \mathbf{b}) = \sqrt{(a_1 - b_1)^2 + (a_2 - b_2)^2 + \cdots + (a_n - b_n)^2} ]
+                    [ L_2(\mathbf{a}, \mathbf{b}) = \sqrt{(a_1 - b_1)^2 + (a_2 - b_2)^2 + \cdots + (a_n - b_n)^2} ]
     """)
 
     st.markdown("""
-    Or, more compactly:
+                    Or, more compactly:
     """)
 
     st.latex(r"""
-    [ L_2(\mathbf{a}, \mathbf{b}) = \sqrt{\sum_{i=1}^n (a_i - b_i)^2} ]
+                    [ L_2(\mathbf{a}, \mathbf{b}) = \sqrt{\sum_{i=1}^n (a_i - b_i)^2} ]
     """)
 
     st.markdown(""" 
-    - A **smaller L2 distance** means the vectors are more similar.  
-    - A **larger L2 distance** indicates greater dissimilarity.  
+                    - A **smaller L2 distance** means the vectors are more similar.  
+                    - A **larger L2 distance** indicates greater dissimilarity.  
     """)
 
     st.markdown("""
-    This formula is commonly used for comparing the semantic similarity of embeddings generated from text using models like Sentence Transformers.
+                    This formula is commonly used for comparing the semantic similarity of embeddings generated from text using models like Sentence Transformers.
     """)