Initial commit: YouTube Shorts maker application
Features: - Video download from TikTok/Douyin using yt-dlp - Audio transcription with OpenAI Whisper - GPT-4 translation (direct/summarize/rewrite modes) - Subtitle generation with ASS format - Video trimming with frame-accurate preview - BGM integration with volume control - Intro text overlay support - Thumbnail generation with text overlay Tech stack: - Backend: FastAPI, Python 3.11+ - Frontend: React, Vite, TailwindCSS - Video processing: FFmpeg - AI: OpenAI Whisper, GPT-4 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
kihong.kim
399
backend/app/services/thumbnail.py
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399
backend/app/services/thumbnail.py
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"""
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Thumbnail Generator Service
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Generates YouTube Shorts thumbnails with:
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1. Frame extraction from video
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2. GPT-generated catchphrase
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3. Text overlay with styling
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"""
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import os
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import subprocess
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import asyncio
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from typing import Optional, Tuple, List
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from openai import OpenAI
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from PIL import Image, ImageDraw, ImageFont
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from app.config import settings
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from app.models.schemas import TranscriptSegment
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def get_openai_client() -> OpenAI:
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"""Get OpenAI client."""
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return OpenAI(api_key=settings.OPENAI_API_KEY)
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async def extract_frame(
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video_path: str,
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output_path: str,
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timestamp: float = 2.0,
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) -> Tuple[bool, str]:
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"""
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Extract a single frame from video.
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Args:
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video_path: Path to video file
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output_path: Path to save thumbnail image
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timestamp: Time in seconds to extract frame
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Returns:
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Tuple of (success, message)
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"""
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try:
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cmd = [
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"ffmpeg", "-y",
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"-ss", str(timestamp),
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"-i", video_path,
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"-vframes", "1",
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"-q:v", "2", # High quality JPEG
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output_path
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]
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process = await asyncio.create_subprocess_exec(
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*cmd,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE
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)
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_, stderr = await process.communicate()
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if process.returncode != 0:
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return False, f"FFmpeg error: {stderr.decode()[:200]}"
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if not os.path.exists(output_path):
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return False, "Frame extraction failed - no output file"
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return True, "Frame extracted successfully"
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except Exception as e:
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return False, f"Frame extraction error: {str(e)}"
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async def generate_catchphrase(
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transcript: List[TranscriptSegment],
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style: str = "homeshopping",
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) -> Tuple[bool, str, str]:
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"""
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Generate a catchy thumbnail text using GPT.
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Args:
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transcript: List of transcript segments (with translations)
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style: Style of catchphrase (homeshopping, viral, informative)
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Returns:
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Tuple of (success, message, catchphrase)
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"""
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if not settings.OPENAI_API_KEY:
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return False, "OpenAI API key not configured", ""
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try:
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client = get_openai_client()
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# Combine translated text
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if transcript and transcript[0].translated:
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full_text = " ".join([seg.translated for seg in transcript if seg.translated])
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else:
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full_text = " ".join([seg.text for seg in transcript])
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style_guides = {
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"homeshopping": """홈쇼핑 스타일의 임팩트 있는 문구를 만드세요.
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- "이거 하나면 끝!" 같은 강렬한 어필
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- 혜택/효과 강조
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- 숫자 활용 (예: "10초만에", "50% 절약")
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- 질문형도 OK (예: "아직도 힘들게?")""",
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"viral": """바이럴 쇼츠 스타일의 호기심 유발 문구를 만드세요.
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- 궁금증 유발
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- 반전/놀라움 암시
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- 이모지 1-2개 사용 가능""",
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"informative": """정보성 콘텐츠 스타일의 명확한 문구를 만드세요.
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- 핵심 정보 전달
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- 간결하고 명확하게""",
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}
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style_guide = style_guides.get(style, style_guides["homeshopping"])
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system_prompt = f"""당신은 YouTube Shorts 썸네일 문구 전문가입니다.
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{style_guide}
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규칙:
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- 반드시 15자 이내!
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- 한 줄로 작성
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- 한글만 사용 (영어/한자 금지)
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- 출력은 문구만! (설명 없이)
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예시 출력:
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이거 하나면 끝!
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10초면 완성!
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아직도 힘들게?
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진짜 이게 돼요?"""
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response = client.chat.completions.create(
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model=settings.OPENAI_MODEL,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": f"다음 영상 내용으로 썸네일 문구를 만들어주세요:\n\n{full_text[:500]}"}
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],
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temperature=0.8,
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max_tokens=50,
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)
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catchphrase = response.choices[0].message.content.strip()
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# Clean up
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catchphrase = catchphrase.strip('"\'""''')
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# Ensure max length
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if len(catchphrase) > 20:
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catchphrase = catchphrase[:20]
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return True, "Catchphrase generated", catchphrase
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except Exception as e:
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return False, f"GPT error: {str(e)}", ""
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def add_text_overlay(
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image_path: str,
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output_path: str,
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text: str,
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font_size: int = 80,
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font_color: str = "#FFFFFF",
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stroke_color: str = "#000000",
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stroke_width: int = 4,
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position: str = "center",
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font_name: str = "NanumGothicBold",
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) -> Tuple[bool, str]:
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"""
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Add text overlay to image using PIL.
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Args:
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image_path: Input image path
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output_path: Output image path
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text: Text to overlay
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font_size: Font size in pixels
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font_color: Text color (hex)
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stroke_color: Outline color (hex)
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stroke_width: Outline thickness
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position: Text position (top, center, bottom)
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font_name: Font family name
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Returns:
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Tuple of (success, message)
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"""
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try:
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# Open image
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img = Image.open(image_path)
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draw = ImageDraw.Draw(img)
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img_width, img_height = img.size
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# Maximum text width (90% of image width)
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max_text_width = int(img_width * 0.9)
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# Try to load font
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def load_font(size):
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font_paths = [
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f"/usr/share/fonts/truetype/nanum/{font_name}.ttf",
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f"/usr/share/fonts/opentype/nanum/{font_name}.otf",
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f"/System/Library/Fonts/{font_name}.ttf",
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f"/Library/Fonts/{font_name}.ttf",
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f"~/Library/Fonts/{font_name}.ttf",
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f"/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",
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]
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for path in font_paths:
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expanded_path = os.path.expanduser(path)
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if os.path.exists(expanded_path):
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try:
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return ImageFont.truetype(expanded_path, size)
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except:
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continue
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return None
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font = load_font(font_size)
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if font is None:
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font = ImageFont.load_default()
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font_size = 40
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# Check text width and adjust if necessary
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bbox = draw.textbbox((0, 0), text, font=font)
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text_width = bbox[2] - bbox[0]
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lines = [text]
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if text_width > max_text_width:
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# Try splitting into 2 lines first
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mid = len(text) // 2
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# Find best split point near middle (at space or comma if exists)
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split_pos = mid
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for i in range(mid, max(0, mid - 5), -1):
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if text[i] in ' ,、，':
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split_pos = i + 1
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break
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for i in range(mid, min(len(text), mid + 5)):
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if text[i] in ' ,、，':
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split_pos = i + 1
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break
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# Split text into 2 lines
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line1 = text[:split_pos].strip()
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line2 = text[split_pos:].strip()
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lines = [line1, line2] if line2 else [line1]
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# Check if 2-line version fits
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max_line_width = max(
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draw.textbbox((0, 0), line, font=font)[2] - draw.textbbox((0, 0), line, font=font)[0]
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for line in lines
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)
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# If still too wide, reduce font size
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while max_line_width > max_text_width and font_size > 40:
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font_size -= 5
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font = load_font(font_size)
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if font is None:
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font = ImageFont.load_default()
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break
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max_line_width = max(
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draw.textbbox((0, 0), line, font=font)[2] - draw.textbbox((0, 0), line, font=font)[0]
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for line in lines
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)
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# Calculate total text height for multi-line
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line_height = font_size + 10
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total_height = line_height * len(lines)
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# Calculate starting y position
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if position == "top":
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start_y = img_height // 6
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elif position == "bottom":
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start_y = img_height - img_height // 4 - total_height
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else: # center
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start_y = (img_height - total_height) // 2
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# Convert hex colors to RGB
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def hex_to_rgb(hex_color):
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hex_color = hex_color.lstrip('#')
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return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
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text_rgb = hex_to_rgb(font_color)
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stroke_rgb = hex_to_rgb(stroke_color)
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# Draw each line
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for i, line in enumerate(lines):
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bbox = draw.textbbox((0, 0), line, font=font)
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line_width = bbox[2] - bbox[0]
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# Account for left bearing (bbox[0]) to prevent first character cut-off
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# Some fonts/characters have non-zero left offset
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x = (img_width - line_width) // 2 - bbox[0]
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y = start_y + i * line_height
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# Draw text with stroke (outline)
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for dx in range(-stroke_width, stroke_width + 1):
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for dy in range(-stroke_width, stroke_width + 1):
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if dx != 0 or dy != 0:
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draw.text((x + dx, y + dy), line, font=font, fill=stroke_rgb)
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# Draw main text
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draw.text((x, y), line, font=font, fill=text_rgb)
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# Save
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img.save(output_path, "JPEG", quality=95)
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return True, "Text overlay added"
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except Exception as e:
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return False, f"Text overlay error: {str(e)}"
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async def generate_thumbnail(
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job_id: str,
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video_path: str,
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transcript: List[TranscriptSegment],
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timestamp: float = 2.0,
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style: str = "homeshopping",
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custom_text: Optional[str] = None,
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font_size: int = 80,
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position: str = "center",
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) -> Tuple[bool, str, Optional[str]]:
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"""
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Generate a complete thumbnail with text overlay.
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Args:
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job_id: Job ID for naming
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video_path: Path to video file
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transcript: Transcript segments
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timestamp: Time to extract frame
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style: Catchphrase style
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custom_text: Custom text (skip GPT generation)
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font_size: Font size
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position: Text position
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Returns:
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Tuple of (success, message, thumbnail_path)
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"""
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# Paths
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frame_path = os.path.join(settings.PROCESSED_DIR, f"{job_id}_frame.jpg")
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thumbnail_path = os.path.join(settings.PROCESSED_DIR, f"{job_id}_thumbnail.jpg")
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# Step 1: Extract frame
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success, msg = await extract_frame(video_path, frame_path, timestamp)
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if not success:
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return False, msg, None
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# Step 2: Generate or use custom text
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if custom_text:
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catchphrase = custom_text
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else:
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success, msg, catchphrase = await generate_catchphrase(transcript, style)
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if not success:
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# Fallback: use first translation
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catchphrase = transcript[0].translated if transcript and transcript[0].translated else "확인해보세요!"
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# Step 3: Add text overlay
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success, msg = add_text_overlay(
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frame_path,
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thumbnail_path,
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catchphrase,
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font_size=font_size,
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position=position,
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)
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if not success:
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return False, msg, None
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# Cleanup frame
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if os.path.exists(frame_path):
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os.remove(frame_path)
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return True, f"Thumbnail generated: {catchphrase}", thumbnail_path
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async def get_video_timestamps(video_path: str, count: int = 5) -> List[float]:
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"""
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Get evenly distributed timestamps from video for thumbnail selection.
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Args:
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video_path: Path to video
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count: Number of timestamps to return
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Returns:
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List of timestamps in seconds
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"""
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try:
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cmd = [
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"ffprobe", "-v", "error",
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"-show_entries", "format=duration",
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"-of", "default=noprint_wrappers=1:nokey=1",
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video_path
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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duration = float(result.stdout.strip())
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# Generate evenly distributed timestamps (skip first and last 10%)
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start = duration * 0.1
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end = duration * 0.9
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step = (end - start) / (count - 1) if count > 1 else 0
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timestamps = [start + i * step for i in range(count)]
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return timestamps
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except Exception:
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return [1.0, 3.0, 5.0, 7.0, 10.0] # Fallback
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