app.py

import os
import io
import time
import gradio as gr
from modules.text_processing import process_text
from modules.pptx_builder import build_presentation
from modules.utils import safe_hex_to_rgb, ensure_tmpdir

APP_NAME = "Auto-PPT Generator"


def generate_pptx(long_text: str,
                  title: str,
                  theme_hex: str,
                  logo_file,
                  add_summary: bool,
                  add_tables: bool,
                  add_charts: bool,
                  use_inference_api: bool,
                  summarizer_model: str,
                  generator_model: str,
                  max_summary_words: int):
    if not long_text or not long_text.strip():
        raise gr.Error("入力テキストが空です。長文を貼り付けてください。")

    theme_rgb = safe_hex_to_rgb(theme_hex or "#3B82F6")

    # Read logo (optional)
    logo_bytes = None
    if logo_file is not None:
        try:
            if hasattr(logo_file, "read"):
                logo_bytes = logo_file.read()
            elif hasattr(logo_file, "name") and logo_file.name:
                with open(logo_file.name, "rb") as f:
                    logo_bytes = f.read()
        except Exception:
            logo_bytes = None

    # Step 1–3: NLP pipeline (summary, sections, bullets, tables, chart data)
    result = process_text(
        text=long_text,
        use_inference_api=use_inference_api,
        summarizer_model=summarizer_model,
        generator_model=generator_model,
        want_summary=add_summary,
        want_tables=add_tables,
        want_charts=add_charts,
        max_summary_words=max_summary_words,
    )

    # Step 4: Build PPTX
    ensure_tmpdir()
    timestamp = time.strftime('%Y%m%d-%H%M%S')
    out_path = f"/tmp/auto_ppt_{timestamp}.pptx"

    build_presentation(
        output_path=out_path,
        title=(title or "Auto-PPT"),
        theme_rgb=theme_rgb,
        logo_bytes=logo_bytes,
        executive_summary=result.get("summary"),
        sections=result.get("sections", []),
        bullets_by_section=result.get("bullets", {}),
        tables=result.get("tables", []),
        charts=result.get("charts", []),
    )

    # Return file path for download
    return out_path


def ui():
    with gr.Blocks(title=APP_NAME) as demo:
        gr.Markdown(f"# {APP_NAME}\n長文→要約→セクション分割→箇条書き/表/図→**PPTX出力** まで自動化")
        with gr.Row():
            with gr.Column(scale=2):
                long_text = gr.Textbox(label="長文テキスト (貼り付け)", lines=20, placeholder="ここに文章を貼り付け…")
                title = gr.Textbox(label="タイトル", value="自動生成スライド")
                theme_hex = gr.Textbox(label="ブランドカラー HEX", value="#3465A4")
                logo = gr.File(label="ロゴ (任意, PNG/JPG)")
                with gr.Row():
                    add_summary = gr.Checkbox(value=True, label="要約スライドを追加")
                    add_tables = gr.Checkbox(value=True, label="表を抽出して追加")
                    add_charts = gr.Checkbox(value=True, label="チャートを生成して追加")
            with gr.Column(scale=1):
                gr.Markdown("### モデル設定")
                use_inference_api = gr.Checkbox(value=False, label="Hugging Face Inference API を使用")
                summarizer_model = gr.Textbox(label="要約モデル (local or API)", value="sshleifer/distilbart-cnn-12-6")
                generator_model = gr.Textbox(label="生成モデル (API推奨, 任意)", value="")
                max_summary_words = gr.Slider(50, 600, value=200, step=10, label="要約の最大語数(目安)")
                generate = gr.Button("PPTXを生成", variant="primary")
                output_file = gr.File(label="ダウンロード")

        generate.click(
            fn=generate_pptx,
            inputs=[long_text, title, theme_hex, logo, add_summary, add_tables, add_charts,
                    use_inference_api, summarizer_model, generator_model, max_summary_words],
            outputs=[output_file],
        )

        gr.Markdown("""
        **Tips**
        - 日本語要約には `sonoisa/t5-base-japanese` を推奨（`text2text-generation`）。
        - Inference API を使う場合は、Space の Secrets に `HF_TOKEN` を設定してください。
        - チャートは `Label: 123` 形式の行を自動検出して棒グラフを作成します。
        """)
    return demo


if __name__ == "__main__":
    demo = ui()
    # Spaces は自動でバインドされますが、ローカル互換のため指定可能
    demo.queue().launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))