Update my_model/tabs/dataset_analysis.py

my_model/tabs/dataset_analysis.py

@@ -38,8 +38,7 @@ class OKVQADatasetAnalyzer:
                                'train_test'indicating whether to load training data, testing data, or both.
 
         The constructor initializes the paths, selects the dataset based on the choice, and loads the initial data by
-        calling the `load_data` method.
-        It also prepares structures for categorizing questions and storing the results.
+        calling the `load_data` method. It also prepares structures for categorizing questions and storing the results.
         """
         
         self.train_file_path = train_file_path
@@ -160,6 +159,7 @@ class OKVQADatasetAnalyzer:
         # Display the chart in Streamlit
         st.altair_chart(chart, use_container_width=True)
 
+    
     def plot_bar_chart(self, df: pd.DataFrame, category_col: str, value_col: str, chart_title: str) -> None:
         """
         Plots an interactive bar chart using Altair and Streamlit.
@@ -217,7 +217,6 @@ class OKVQADatasetAnalyzer:
         st.altair_chart(chart, use_container_width=True)
 
 
-
     def export_to_csv(self, qs_filename: str, question_types_filename: str) -> None:
         """
         Exports the categorized questions and their counts to two separate CSV files.
@@ -246,8 +245,6 @@ class OKVQADatasetAnalyzer:
                 writer.writerow([q_type, count])
 
 
-
-
 def run_dataset_analyzer() -> None:
     """
     Executes the dataset analysis process and displays the results using Streamlit.
@@ -326,7 +323,4 @@ def run_dataset_analyzer() -> None:
         with st.expander("Show Dataset Samples"):
             n = random.randint(1,len(train_data)-10)
             # Displaying 10 random samples.
-            st.write(train_data[n:n+10])
-
-
-
+            st.write(train_data[n:n+10])