284 lines
10 KiB
Python
284 lines
10 KiB
Python
"""
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Generate detailed narration video for specific PPT slide with PDF context
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This script creates a comprehensive narration video combining:
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1. PPT slide content (text + visual)
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2. PDF financial data context
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3. AI-generated detailed explanation
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4. Smooth transitions between content sections
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Usage:
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python backend/generate_detailed_narration.py --slide 2
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"""
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import argparse
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import json
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import os
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from pathlib import Path
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from typing import Dict, Any, List
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from dotenv import load_dotenv
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# Import Gemini API
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from google import genai
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from google.genai import types
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class DetailedNarrationGenerator:
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"""Generate detailed narration video for specific slide"""
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def __init__(self, slide_index: int = 2):
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load_dotenv()
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self.project_root = Path(__file__).parent.parent
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self.slide_index = slide_index
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self.api_key = os.getenv("GEMINI_API_KEY")
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if not self.api_key:
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raise ValueError("GEMINI_API_KEY not found in .env")
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self.client = genai.Client(api_key=self.api_key)
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def load_ppt_slide(self) -> Dict[str, Any]:
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"""Load specific PPT slide data"""
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import sys
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sys.path.insert(0, str(self.project_root))
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from backend.ppt_extractor import extract_ppt_data
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ppt_path = self.project_root / "assets/0_input/ライフプラン情報.pptx"
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ppt_data = extract_ppt_data(str(ppt_path))
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if self.slide_index >= len(ppt_data["slides"]):
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raise ValueError(f"Slide {self.slide_index} not found")
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return ppt_data["slides"][self.slide_index]
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def load_pdf_context(self) -> Dict[str, Any]:
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"""Load PDF financial data as context"""
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import sys
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sys.path.insert(0, str(self.project_root))
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from backend.extractor import extract_pdf_data
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pdf_path = self.project_root / "assets/0_input/帳票（お客様向け）.pdf"
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return extract_pdf_data(str(pdf_path))
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def generate_narration_script(self, slide: Dict, pdf_data: Dict) -> List[Dict]:
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"""
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Generate detailed narration script using Gemini
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Returns:
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List of narration segments with text, duration, visual cues
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"""
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print(f"🤖 Generating detailed narration for slide {self.slide_index}...")
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prompt = f"""
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あなたはプロのファイナンシャルプランナーであり、親しみやすい動画解説者です。
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以下の情報に基づいて、詳細な解説動画用のナレーション原稿を作成してください。
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【PPTスライド内容】
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タイトル: {slide['title']}
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テキスト:
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{slide['text']}
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【PDFから抽出した追加情報】
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- 家族代表: {pdf_data.get('client_name', 'テスト太郎')} ({pdf_data.get('current_age', 47)}歳)
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- 年収: {pdf_data.get('annual_income', 703)}万円
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- 現在の資産: {pdf_data.get('current_assets', 836)}万円
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- 年間支出: {pdf_data.get('annual_expenditure', 1038)}万円
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【要件】
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1. スライドの内容を**段落ごとに分解**し、各段落を詳細に解説してください
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2. 各セグメントは以下の構造で出力してください：
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- intro: スライド全体の導入（15秒程度）
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- section_1: 家族代表の基本情報（20秒程度）
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- section_2: 配偶者情報（15秒程度）
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- section_3: お子様情報と教育プラン（20秒程度）
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- summary: まとめ（10秒程度）
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3. 各セグメントには以下を含めてください：
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- text: ナレーション文（自然な口語体、具体的な数値を含む）
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- duration: 推定時間（秒）
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- visualCue: 画面表示の指示（"ppt" / "zoom_in" / "highlight_section"）
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- highlightArea: 強調する領域（"top" / "middle" / "bottom" / "full"）
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4. 数値やデータは必ず具体的に言及し、抽象的な表現は避けてください
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5. 親しみやすく、わかりやすい言葉遣いを心がけてください
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**出力形式**: JSON配列として返してください
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[
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{{
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"id": "intro",
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"text": "それでは、ご家族の基本情報について詳しく見ていきましょう。...",
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"duration": 15,
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"visualCue": "ppt",
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"highlightArea": "full"
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}},
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...
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]
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"""
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response = self.client.models.generate_content(
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model="gemini-2.0-flash",
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contents=prompt,
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config=types.GenerateContentConfig(
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temperature=0.7,
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response_mime_type="application/json"
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)
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)
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result = json.loads(response.text)
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print(f"✓ Generated {len(result)} narration segments")
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return result
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def create_smooth_transitions(self, segments: List[Dict]) -> List[Dict]:
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"""Add transition effects between segments"""
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enhanced_segments = []
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for i, seg in enumerate(segments):
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# Enhance segment with visual metadata
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enhanced_seg = {
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**seg,
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"type": "narration",
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"visualType": "ppt",
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"visualAssetIndex": self.slide_index,
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"brollKeyword": f"Japanese family planning {seg.get('highlightArea', 'full')}",
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}
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enhanced_segments.append(enhanced_seg)
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return enhanced_segments
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def generate_audio(self, segments: List[Dict]) -> List[Dict]:
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"""Generate TTS audio for each segment"""
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import sys
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sys.path.insert(0, str(self.project_root))
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from backend.audio_generator import AudioGenerator
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from backend.video_processor.utils import get_video_duration
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print("🎙️ Generating audio for narration segments...")
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audio_gen = AudioGenerator()
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output_dir = self.project_root / "public" / f"slide_{self.slide_index}_narration"
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output_dir.mkdir(parents=True, exist_ok=True)
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for i, seg in enumerate(segments):
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text = seg["text"]
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audio_filename = f"narration_{seg['id']}.mp3"
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audio_path = output_dir / audio_filename
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print(f" [{i+1}] Generating: {text[:50]}...")
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# Use OpenAI TTS API directly
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try:
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response = audio_gen.client.audio.speech.create(
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model="tts-1",
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voice="nova",
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input=text
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)
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response.stream_to_file(str(audio_path))
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# Get actual duration
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actual_duration = get_video_duration(str(audio_path))
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seg["duration"] = actual_duration
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seg["audioPath"] = f"public/slide_{self.slide_index}_narration/{audio_filename}"
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except Exception as e:
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print(f" ⚠️ Failed: {e}")
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continue
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print(f"✓ Generated audio for {len(segments)} segments")
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return segments
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def create_render_props(self, segments: List[Dict], slide: Dict, pdf_data: Dict) -> Dict:
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"""Create render_props.json for the detailed narration video"""
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# Load original props for charts
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original_props_path = self.project_root / "assets/1_processed/render_props.json"
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if original_props_path.exists():
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with open(original_props_path, "r", encoding="utf-8") as f:
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original_props = json.load(f)
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else:
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original_props = {"charts": {}, "assets": {"slides": []}}
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# Construct new props
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new_props = {
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"meta": {
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"title": f"【詳細解説】{slide['title']} - {pdf_data.get('client_name', 'お客様')}様",
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"currencyUnit": "万円",
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"duration": sum(s.get("duration", 0) for s in segments),
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"clientName": pdf_data.get("client_name", "お客様")
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},
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"charts": original_props.get("charts", {}),
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"script": segments,
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"assets": {
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**original_props.get("assets", {}),
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"targetSlide": slide
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}
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}
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# Save to file
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output_path = self.project_root / f"assets/1_processed/render_props_slide_{self.slide_index}_detailed.json"
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(new_props, f, ensure_ascii=False, indent=2)
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print(f"✓ Saved render props to {output_path.name}")
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return new_props
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def run(self) -> str:
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"""Execute full pipeline"""
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print(f"🚀 Generating detailed narration video for slide {self.slide_index}...")
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print()
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# Load data
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slide = self.load_ppt_slide()
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pdf_data = self.load_pdf_context()
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print(f"✓ Loaded slide: {slide['title']}")
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print(f"✓ Loaded PDF context for: {pdf_data.get('client_name')}")
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print()
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# Generate narration
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segments = self.generate_narration_script(slide, pdf_data)
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print()
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# Add transitions
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segments = self.create_smooth_transitions(segments)
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print(f"✓ Added smooth transitions")
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print()
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# Generate audio
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segments = self.generate_audio(segments)
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print()
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# Create render props
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props = self.create_render_props(segments, slide, pdf_data)
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print()
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output_file = f"assets/2_output/slide_{self.slide_index}_detailed.mp4"
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print(f"✅ Pipeline complete! Use the following command to render:")
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print(f" npx remotion render MainVideo {output_file} \\")
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print(f" --props=\"assets/1_processed/render_props_slide_{self.slide_index}_detailed.json\"")
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return output_file
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def main():
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parser = argparse.ArgumentParser(
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description="Generate detailed narration video for specific PPT slide"
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)
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parser.add_argument("--slide", type=int, default=2, help="Slide index (default: 2)")
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args = parser.parse_args()
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try:
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generator = DetailedNarrationGenerator(slide_index=args.slide)
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generator.run()
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except Exception as e:
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print(f"\n❌ Generation failed: {e}")
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import traceback
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traceback.print_exc()
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return 1
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return 0
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if __name__ == "__main__":
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exit(main())
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