"""Deterministic, editable poster renderer. Image visuals are generated separately.""" from PIL import Image, ImageDraw, ImageFont, ImageFilter, ImageOps, ImageEnhance import io, math, re, hashlib, colorsys, os from pathlib import Path W,H=1080,1350 FONT_REG=os.getenv('FONT_REGULAR','/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf') FONT_BOLD=os.getenv('FONT_BOLD','/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf') def font(px,bold=False): p=FONT_BOLD if bold else FONT_REG if not Path(p).exists(): candidates=['/usr/share/fonts/truetype/noto/NotoSans-Bold.ttf' if bold else '/usr/share/fonts/truetype/noto/NotoSans-Regular.ttf','/Library/Fonts/Arial.ttf'] p=next((x for x in candidates if Path(x).exists()),p) try:return ImageFont.truetype(p,px) except OSError:return ImageFont.load_default() def rgb(hexval, default='#6D43C2'): text=(hexval or default).lstrip('#') if not re.fullmatch(r'[0-9a-fA-F]{6}',text): text=default.lstrip('#') return tuple(bytes.fromhex(text)) def blend(a,b,t):return tuple(round(x*(1-t)+y*t) for x,y in zip(a,b)) def contrast(rgbval):return (rgbval[0]*0.299+rgbval[1]*0.587+rgbval[2]*0.114)/255 def palettes(image): """Detects dominant colors by pixels (NOT computer vision AI).""" img=image.convert('RGB').resize((140,140)).quantize(colors=8,method=Image.Quantize.MEDIANCUT).convert('RGB') colors=sorted(img.getcolors(img.width*img.height) or [],reverse=True) ordered=[] for c, color in colors: if color not in ordered and all(sum(abs(color[i]-p[i]) for i in range(3))>70 for p in ordered): ordered.append(color) non_neutral=[c for c in ordered if max(c)-min(c)>35 and 351 else (250,247,255) return ['#%02X%02X%02X'%v for v in [first,second]] def multiline(draw,txt,max_width,face,max_lines=4): words=(txt or '').strip().split() if not words:return [''] lines=[]; cur='' for w in words: trial=(cur+' '+w).strip() if draw.textbbox((0,0),trial,font=face)[2] <= max_width or not cur:cur=trial else:lines.append(cur);cur=w if cur:lines.append(cur) if len(lines)>max_lines: lines=lines[:max_lines] while draw.textbbox((0,0),lines[-1]+'…',font=face)[2]>max_width and len(lines[-1])>1:lines[-1]=lines[-1][:-1] lines[-1]+='…' return lines def draw_fit(draw,text,x,y,width,max_height,top_size,color,bold=True): for sz in range(top_size,38,-2): f=font(sz,bold) line_h=int(sz*1.32) lines=multiline(draw,text,width,f,4) if len(lines)*line_h<=max_height: for line in lines: draw.text((x,y),line,font=f,fill=color,stroke_width=0) y+=line_h return y raise ValueError('Headline terlalu panjang untuk desain. Singkatkan judul.') def render(brand, item, *, visual_bytes=None, reference_path=None, logo_path=None, mode='medium'): primary=rgb(brand.get('primary','#6D43C2')) sec=rgb(brand.get('secondary','#FFF8F2'), '#FFF8F2') dark=blend(primary,(15,18,35),0.48) # Render a one-column gradient; stretch natively (far faster for 90 posters). stripe=Image.new('RGB',(1,H)) stripe.putdata([blend(blend(sec,primary,max(0,0.48-(y/H)*0.20)),dark,(y/H)*0.78) for y in range(H)]) img=stripe.resize((W,H)) d=ImageDraw.Draw(img) # Pattern with deterministic variants, based on topic; not random content duplicated. digest=hashlib.sha256(item['title'].encode()).digest() for j in range(6): px=90+digest[j]*4; py=95+digest[j+6]*4 r=42+digest[j+12] outline=blend(primary,(255,255,255),0.52) d.ellipse((px-r,py-r,px+r,py+r),outline=outline,width=3) if visual_bytes: art=Image.open(io.BytesIO(visual_bytes)).convert('RGB') art=ImageOps.fit(art,(W,970),method=Image.Resampling.LANCZOS,centering=(0.5,0.36)) art=ImageEnhance.Contrast(art).enhance(0.99) img.paste(art,(0,0)) # fade image into the solid dark panel fade=Image.new('RGBA',(1,540)) fade.putdata([(*dark,int(255*(y/540)**1.3)) for y in range(540)]) overlay=fade.resize((W,540)) part=img.crop((0,430,W,970)).convert('RGBA') img.paste(Image.alpha_composite(part,overlay).convert('RGB'),(0,430)) else: # Branded art composition for the explicit offline mode. art=Image.new('RGBA',(W,770),(0,0,0,0)); q=ImageDraw.Draw(art) r=260 q.ellipse((380-r,150-r,380+r,150+r),fill=(*blend(primary,(255,255,255),0.4),130)) q.rounded_rectangle((250,160,905,690),radius=85,fill=(*blend(primary,(255,255,255),0.30),205)) q.ellipse((395,100,975,680),fill=(*blend(primary,(255,255,255),0.62),170)) q.rounded_rectangle((335,230,835,600),radius=48,outline=(255,255,255,245),width=11) art=art.filter(ImageFilter.GaussianBlur(1)) img.paste(Image.alpha_composite(img.crop((0,0,W,770)).convert('RGBA'),art).convert('RGB'),(0,0)) d=ImageDraw.Draw(img) # Backplate for content, deliberately different from image asset layer. panel_start=760 if visual_bytes else 735 d.rounded_rectangle((42,panel_start,W-42,H-42),radius=50,fill=(*dark,),outline=blend(primary,(255,255,255),0.18),width=3) d.rounded_rectangle((83,panel_start+44,290,panel_start+92),radius=18,fill=primary) cat=(item.get('category') or 'KONTEN').upper()[:18] d.text((102,panel_start+53),cat,font=font(22,True),fill='white') headline=item.get('title','Konten Baru') text_y=panel_start+117 text_y=draw_fit(d,headline,94,text_y,880,242,76,(255,255,255)) sub=item.get('subtitle','').strip() if sub: subfont=font(30) for line in multiline(d,sub,862,subfont,3): if text_y+40>H-145:break d.text((98,text_y+14),line,font=subfont,fill=(232,226,250)) text_y+=41 d.line((95,H-127,W-95,H-127),fill=blend(primary,(255,255,255),0.5),width=2) d.text((97,H-109),'SWIPE • SAVE • SHARE',font=font(20,True),fill=(230,229,239)) # Consistent brand name and original uploaded logo. brand_name=brand['name'][:34] d.rounded_rectangle((47,44,900,140),radius=26,fill=(*dark,)) bx=72 if logo_path and Path(logo_path).exists(): try: logo=Image.open(logo_path).convert('RGBA') logo.thumbnail((78,78),Image.Resampling.LANCZOS) box=Image.new('RGBA',(83,83),(255,255,255,255)); box.alpha_composite(logo,((83-logo.width)//2,(83-logo.height)//2)) img.paste(box.convert('RGB'),(58,50));bx=161 except Exception:pass d=ImageDraw.Draw(img) f=font(33,True) while d.textbbox((0,0),brand_name,font=f)[2]>W-bx-120 and f.size>18: f=font(f.size-2,True) d.text((bx,72),brand_name,font=f,fill=(255,255,255)) out=io.BytesIO();img.save(out,'JPEG',quality=93,optimize=True) return out.getvalue() def perceptual_dhash(image_bytes): """64-bit difference hash. Detects near-identical images, not semantic repeats.""" img=Image.open(io.BytesIO(image_bytes)).convert('L').resize((9,8),Image.Resampling.LANCZOS) pixels=list(img.tobytes());bits=0 for y in range(8): for x in range(8): bits=(bits<<1)|int(pixels[y*9+x]>pixels[y*9+x+1]) return bits def hash_distance(a,b): return (a^b).bit_count()