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,
                  add_summary: bool,
                  add_charts: bool,
                  use_inference_api: bool,
                  summarize_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_rab = safe_hex_to_rgb(theme_hex or "#3B82F6")

    # Read logo (optional)
    logo_bytes = None
    if logo_file is not None:
        logo_bytes = logo_file.read()
    
    # Step 1-3: NLP pipeline(summary, sections, bullets, tables , chart data)
    results = process_text(
        text=long_text,
        use_inference_api=use_inference_api,
        summarize_model=summarize_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_rab,
        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="ここに長文テキストを貼り付けてください...")
                title = gr.Textbox(label="タイトル", value="自動生成スライド")
                theme_hex = gr.ColorPicker(label="テーマカラー", value="#3B82F6")
                logo = gr.File(label="ロゴ画像(任意)")
                with gr.Row():
                    add_summary = gr.Checkbox(label="要約スライドを追加", value=True)
                    add_tables = gr.Checkbox(label="表を検出して追加", value=True)
                    add_charts = gr.Checkbox(label="チャートを生成して追加", value=True)
            with gr.Column(scale=1):
                use_inference_api = gr.Checkbox(label="Hugging Face Inference APIを使用", value=False)
                summarize_api = gr.Textbox(label="要約モデル名(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(label="要約の最大単語数", 50, 600, value=200, step=10)
                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, add_summary, add_charts,add_tables,
                use_inference_api, summarize_api, generator_model, max_summary_words, summarizer_model,
            ],
            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 Secret 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__":
    app = ui()
    app.launch()