def build_report_summary()

in src/dfcx_scrapi/tools/evaluations.py [0:0]

• 30 lines of code
• 11 McCabe index (conditional complexity)

    def build_report_summary(self, df: pd.DataFrame) -> pd.DataFrame:
        # Check for agent_id or get from dataframe
        if not self.agent_id:
            self.agent_id = self.get_agent_id_from_results(df)

        # Get Generative Settings for report data
        a = Agents(language_code=self.language_code)
        agent = a.get_agent(self.agent_id)
        gen_settings = a.get_generative_settings(
            self.agent_id, language_code=self.language_code)
        model_name = self.get_model_name(gen_settings)

        current_datetime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        metrics_info = {}
        if "similarity" in df.columns:
            metrics_info["similarity"] = df["similarity"].mean()
        if "tool_name_match" in df.columns:
            metrics_info["tool_match"] = df["tool_name_match"].mean()

        eval_results_summary = pd.DataFrame({
            'timestamp': [current_datetime],
            'total_conversations': [len(df['eval_id'].unique())],
            'model_name': [model_name],
            'agent_name': agent.display_name,
            'agent_id': [self.agent_id],
            'notes': [""]
        })

        # insert metrics for report
        insert_index = eval_results_summary.columns.get_loc(
            "total_conversations") + 1
        for metric, value in metrics_info.items():
            if (isinstance(value, float) and np.isnan(value)):
                value = "-"
            eval_results_summary.insert(insert_index, metric, [value])
            insert_index += 1

        return eval_results_summary