report

duallens_analytics.report

Generate a downloadable Markdown report summarising the analysis.

The report includes:

• The list of analysed companies.
• A formatted financial-metrics table.
• The AI-initiative composite ranking (if generated).
• The full Q&A chat log from the session.

generate_report(companies, metrics_df, ranking_text, chat_history=None)

Compile session artefacts into a Markdown report string.

Parameters:

Name	Type	Description	Default
companies	list[str]	Ticker symbols included in the analysis.	required
metrics_df	DataFrame	Financial-metrics DataFrame rendered via :meth:~pandas.DataFrame.to_markdown.	required
ranking_text	str	Raw LLM ranking output. Pass an empty string if no ranking has been generated yet.	required
chat_history	list[dict] | None	Optional list of {"role": ..., "content": ...} dicts from the AI Q&A session.	None

Returns:

Type	Description
str	A complete Markdown document ready for download.

Source code in src/duallens_analytics/report.py

def generate_report(
    companies: list[str],
    metrics_df: pd.DataFrame,
    ranking_text: str,
    chat_history: list[dict] | None = None,
) -> str:
    """Compile session artefacts into a Markdown report string.

    Args:
        companies: Ticker symbols included in the analysis.
        metrics_df: Financial-metrics DataFrame rendered via
            :meth:`~pandas.DataFrame.to_markdown`.
        ranking_text: Raw LLM ranking output.  Pass an empty string if
            no ranking has been generated yet.
        chat_history: Optional list of ``{"role": ..., "content": ...}``
            dicts from the AI Q&A session.

    Returns:
        A complete Markdown document ready for download.
    """
    logger.info("Generating report for %s", companies)
    now = datetime.now(tz=UTC).strftime("%Y-%m-%d %H:%M UTC")
    lines: list[str] = [
        "# DualLens Analytics – Investment Report",
        f"_Generated on {now}_\n",
        "## Companies Analysed",
        ", ".join(companies),
        "",
        "## Financial Metrics",
        metrics_df.to_markdown(),
        "",
        "## AI-Initiative Ranking",
        ranking_text if ranking_text else "_Not yet generated._",
        "",
    ]

    if chat_history:
        lines.append("## Q&A Log")
        for msg in chat_history:
            role = msg.get("role", "user").capitalize()
            lines.append(f"**{role}:** {msg['content']}\n")

    lines.append("\n---\n_Report produced by DualLens Analytics v0.1.0_")
    return "\n".join(lines)