rebuild with changes

build/lib/lyzr/base/prompts/analysis_steps_pt.txt

@@ -17,7 +17,7 @@ B. The value of "steps" should be a list of dictionaries. Each dictionary should
     2. Task: The task to be performed. The task can be one of the following: "clean_data", "transform", "math_operation", "analysis"
     3. Type: The type of task to be performed.
         3a. For task "clean_data", following types are available: "convert_to_datetime", "convert_to_numeric", "convert_to_categorical"
-        3b. For task "transform", following types are available: "one_hot_encode", "ordinal_encode", "scale", "extract_time_period"
+        3b. For task "transform", following types are available: "one_hot_encode", "ordinal_encode", "scale", "extract_time_period", "select_indices"
         3c. For task "math_operation", following types are available: "add", "subtract", "multiply", "divide"
         3d. For task "analysis", following types are available: "sortvalues", "filter", "mean", "sum", "cumsum", "groupby", "correlation", "regression", "classification", "clustering", "forecast"
     4. Args: The arguments required to perform the task. The arguments should be in the form of a dictionary.

build/lib/lyzr/base/prompts/plotting_steps_with_analysis_pt.txt

@@ -15,7 +15,7 @@ A. The value of "preprocess" should be a dictionary with keys "df_name" and "ste
     2. Task: The task to be performed. The task can be one of the following: "clean_data", "transform", "math_operation", "analysis"
     3. Type: The type of task to be performed.
         3a. For task "clean_data", following types are available: "convert_to_datetime", "convert_to_numeric", "convert_to_categorical"
-        3b. For task "transform", following types are available: "one_hot_encode", "ordinal_encode", "scale", "extract_time_period"
+        3b. For task "transform", following types are available: "one_hot_encode", "ordinal_encode", "scale", "extract_time_period", "select_indices"
         3c. For task "math_operation", following types are available: "add", "subtract", "multiply", "divide"
         3d. For task "analysis", following types are available: "sortvalues", "filter", "mean", "sum", "cumsum", "groupby", "correlation", "regression", "classification", "clustering", "forecast"
     4. Args: The arguments required to perform the task. The arguments should be in the form of a dictionary.

build/lib/lyzr/data_analyzr/analyzr.py

@@ -360,10 +360,11 @@ def visualisation(
             self.logger.info("Fetching dataframes from database to make visualization.")
             self.df_dict = self.database_connector.fetch_dataframes_dict()
             plot_df = self.df_dict
-        df_keys = list(self.df_dict.keys())
-        for key in df_keys:
-            k_new = key.lower().replace(" ", "_")
-            self.df_dict[k_new] = self.df_dict.pop(key)
+        if plot_df is not None:
+            df_keys = list(plot_df.keys())
+            for key in df_keys:
+                k_new = key.lower().replace(" ", "_")
+                plot_df[k_new] = plot_df.pop(key)
 
         self.visualisation_output = None
         self.start_time = time.time()

build/lib/lyzr/data_analyzr/db_connector.py

@@ -475,6 +475,14 @@ def create_database(self, db_path: str, df_dict: dict[pd.DataFrame]):
         try:
             self.conn = sqlite3.connect(self.db_path, check_same_thread=False)
             for name, df in df_dict.items():
+                df = df.rename(
+                    columns=dict(
+                        zip(
+                            df.columns,
+                            [col.replace(" ", "_").lower() for col in df.columns],
+                        )
+                    )
+                )
                 df.to_sql(name, con=self.conn, index=False, if_exists="replace")
             return self.conn
         except sqlite3.Error as e: