langchain/callbacks/mlflow_callback.py/MlflowCallbackHandler/_create_session_analysis_df
class MlflowCallbackHandler:    def _create_session_analysis_df(self) -> Any:
        """Create a dataframe with all the information from the session."""
        pd = import_pandas()
        on_llm_start_records_df = pd.DataFrame(self.records["on_llm_start_records"])
        on_llm_end_records_df = pd.DataFrame(self.records["on_llm_end_records"])

        llm_input_prompts_df = (
            on_llm_start_records_df[["step", "prompt", "name"]]
            .dropna(axis=1)
            .rename({"step": "prompt_step"}, axis=1)
        )
        complexity_metrics_columns = []
        visualizations_columns = []

        complexity_metrics_columns = [
            "flesch_reading_ease",
            "flesch_kincaid_grade",
            "smog_index",
            "coleman_liau_index",
            "automated_readability_index",
            "dale_chall_readability_score",
            "difficult_words",
            "linsear_write_formula",
            "gunning_fog",
            # "text_standard",
            "fernandez_huerta",
            "szigriszt_pazos",
            "gutierrez_polini",
            "crawford",
            "gulpease_index",
            "osman",
        ]

        visualizations_columns = ["dependency_tree", "entities"]

        llm_outputs_df = (
            on_llm_end_records_df[
                [
                    "step",
                    "text",
                    "token_usage_total_tokens",
                    "token_usage_prompt_tokens",
                    "token_usage_completion_tokens",
                ]
                + complexity_metrics_columns
                + visualizations_columns
            ]
            .dropna(axis=1)
            .rename({"step": "output_step", "text": "output"}, axis=1)
        )
        session_analysis_df = pd.concat([llm_input_prompts_df, llm_outputs_df], axis=1)
        session_analysis_df["chat_html"] = session_analysis_df[
            ["prompt", "output"]
        ].apply(
            lambda row: construct_html_from_prompt_and_generation(
                row["prompt"], row["output"]
            ),
            axis=1,
        )
        return session_analysis_df