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feat: Overlay the mathematical Sun Path over the live camera feed or 3D model view (#8)

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#7 Task completed.

Co-authored-by: Sayan Datta <sayan@Sayans-MacBook-Air.local>
Reviewed-on: sagnik/Project_Velocity#8
This commit is contained in:
sayan
2026-03-21 17:01:06 +05:30
parent 6c98affe53
commit 023ba48da2
16 changed files with 3344 additions and 2939 deletions
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comfy_engine/scripts/dw_gateway_v2.py
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@@ -1,420 +1,420 @@
#!/usr/bin/env python3
"""
Dream Weaver API Gateway v2 — Dynamic Keyword → Local LLM → ComfyUI Pipeline
========================================================================
Port: 8080 (public-facing)
ComfyUI: localhost:8188 (internal)
NEW IN v2:
- POST /dream-weaver now accepts keywords[] + room_type for LLM-based prompt generation
- POST /dream-weaver/expand — expand keywords to prompt WITHOUT generating (preview)
- GET /room-types — list available room types
- Uses local Ollama model (qwen3.5:27b) for prompt expansion (no cloud API dependencies)
Environment variables:
OLLAMA_URL — Ollama server (default: http://localhost:11434)
OLLAMA_MODEL — Model name (default: qwen3.5:27b)
"""
import asyncio, json, time, uuid, io, sys, os, logging
from pathlib import Path
from typing import Optional, List
import httpx
import uvicorn
from fastapi import FastAPI, UploadFile, File, HTTPException, Form, BackgroundTasks
from fastapi.responses import JSONResponse, StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
# Add scripts dir to path so we can import prompt_expander
SCRIPTS_DIR = Path(__file__).parent / "scripts"
sys.path.insert(0, str(SCRIPTS_DIR))
try:
from prompt_expander import expand_prompt, expand_prompt_simple, ROOM_CONTEXTS, ExpandedPrompt
LLM_AVAILABLE = True
except ImportError:
LLM_AVAILABLE = False
logging.warning("prompt_expander not found — LLM expansion disabled")
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger("DreamWeaverGateway")
COMFY = "http://127.0.0.1:8188"
COMFY_ROOT = "/opt/dlami/nvme/ComfyUI"
app = FastAPI(
title="Dream Weaver API v2",
version="2.0.0",
description="Dynamic keyword-to-interior-design generation powered by LLM + ComfyUI"
)
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
# In-memory job store (swap for Redis in production)
jobs: dict = {}
# ─── Models ──────────────────────────────────────────────────────────────────
class ExpandRequest(BaseModel):
keywords: List[str]
room_type: str = "living_room"
additional_notes: str = ""
class ExpandResponse(BaseModel):
style_name: str
positive_prompt: str
negative_prompt: str
cfg: float
denoise: float
steps: int
reasoning: str
source: str
# ─── ComfyUI helpers ──────────────────────────────────────────────────────────
async def upload_to_comfy(data: bytes, filename: str) -> str:
async with httpx.AsyncClient(timeout=30) as client:
r = await client.post(f"{COMFY}/upload/image",
files={"image": (filename, data, "image/jpeg")},
data={"overwrite": "true"})
r.raise_for_status()
return r.json()["name"]
def build_workflow(img_name: str, expanded: "ExpandedPrompt") -> dict:
"""Build ComfyUI API workflow from an ExpandedPrompt result."""
return {
"1": {"class_type": "CheckpointLoaderSimple",
"inputs": {"ckpt_name": "realvisxlV50_v50LightningBakedvae.safetensors"}},
"2": {"class_type": "LoadImage",
"inputs": {"image": img_name, "upload": "image"}},
"3": {"class_type": "CLIPTextEncode", # Positive prompt
"inputs": {"text": expanded.positive_prompt, "clip": ["1", 1]}},
"4": {"class_type": "CLIPTextEncode", # Negative prompt
"inputs": {"text": expanded.negative_prompt, "clip": ["1", 1]}},
"5": {"class_type": "VAEEncode",
"inputs": {"pixels": ["2", 0], "vae": ["1", 2]}},
"6": {"class_type": "KSampler",
"inputs": {"model": ["1", 0],
"positive": ["3", 0],
"negative": ["4", 0],
"latent_image": ["5", 0],
"seed": int(time.time()) % 999983,
"steps": expanded.steps,
"cfg": expanded.cfg,
"sampler_name": "dpmpp_2m",
"scheduler": "karras",
"denoise": expanded.denoise}},
"7": {"class_type": "VAEDecode",
"inputs": {"samples": ["6", 0], "vae": ["1", 2]}},
"8": {"class_type": "SaveImage",
"inputs": {"images": ["7", 0],
"filename_prefix": f"dw_{expanded.style_name.replace(' ', '_')[:30]}"}},
}
async def queue_prompt(workflow: dict) -> str:
async with httpx.AsyncClient(timeout=30) as client:
r = await client.post(f"{COMFY}/prompt",
json={"prompt": workflow, "client_id": str(uuid.uuid4())})
r.raise_for_status()
return r.json()["prompt_id"]
async def poll_result(prompt_id: str, timeout: int = 300):
start = time.time()
async with httpx.AsyncClient(timeout=10) as client:
while time.time() - start < timeout:
r = await client.get(f"{COMFY}/history/{prompt_id}")
if r.status_code == 200:
h = r.json().get(prompt_id, {})
if h.get("status", {}).get("status_str") == "error":
return None, h.get("status", {}).get("messages", ["unknown"])
imgs = [img for nd in h.get("outputs", {}).values()
for img in nd.get("images", [])]
if imgs:
return imgs[0], None
await asyncio.sleep(2)
return None, "timeout"
async def background_poll(job_id: str, prompt_id: str):
img, err = await poll_result(prompt_id)
if img:
jobs[job_id].update({"status": "done", "output": img, "completed": time.time()})
else:
jobs[job_id].update({"status": "error", "error": str(err)})
# ─── Endpoints ───────────────────────────────────────────────────────────────
@app.get("/health")
async def health():
comfy_ok = False
try:
async with httpx.AsyncClient(timeout=5) as c:
r = await c.get(f"{COMFY}/system_stats")
comfy_ok = r.status_code == 200
except Exception:
pass
return {
"status": "ok",
"comfyui": comfy_ok,
"gpu": "4x NVIDIA L4 (96GB VRAM)",
"model": "RealVisXL V5.0 Lightning",
"llm_expansion": LLM_AVAILABLE,
"version": "2.0.0"
}
@app.get("/room-types")
async def room_types():
"""List all supported room types with their context."""
if not LLM_AVAILABLE:
return {"room_types": ["bedroom", "living_room", "bathroom", "kitchen",
"dining_room", "home_office", "hallway", "balcony"]}
return {
"room_types": {
k: {
"description": v["description"],
"key_elements": v["key_elements"]
}
for k, v in ROOM_CONTEXTS.items()
}
}
@app.post("/dream-weaver/expand", response_model=ExpandResponse)
async def expand_endpoint(req: ExpandRequest):
"""
Preview the LLM-generated prompt WITHOUT submitting to ComfyUI.
Use this to let the user review/edit the prompt before generating.
Request body:
{
"keywords": ["blue marble", "gold veins", "renaissance", "sharp contours"],
"room_type": "bedroom",
"additional_notes": "luxury hotel feel"
}
"""
if not req.keywords:
raise HTTPException(status_code=400, detail="keywords list cannot be empty")
try:
if LLM_AVAILABLE:
result = await asyncio.to_thread(
expand_prompt,
keywords=req.keywords,
room_type=req.room_type,
additional_notes=req.additional_notes
)
else:
result = expand_prompt_simple(req.keywords, req.room_type)
except Exception as e:
logger.error(f"Prompt expansion failed: {e}")
raise HTTPException(status_code=500, detail=f"LLM expansion failed: {str(e)}")
return ExpandResponse(
style_name=result.style_name,
positive_prompt=result.positive_prompt,
negative_prompt=result.negative_prompt,
cfg=result.cfg,
denoise=result.denoise,
steps=result.steps,
reasoning=result.reasoning,
source=result.source
)
@app.post("/dream-weaver")
async def dream_weaver(
image: UploadFile = File(...),
# ── Dynamic keyword mode (new) ──
keywords: str = Form(default=""), # comma-separated: "blue marble, gold, renaissance"
room_type: str = Form(default="living_room"),
additional_notes: str = Form(default=""),
# ── Optional overrides ──
custom_positive: str = Form(default=""), # skip LLM, use this prompt directly
custom_negative: str = Form(default=""),
denoise: float = Form(default=0.0), # 0.0 = use LLM recommendation
cfg_scale: float = Form(default=0.0), # 0.0 = use LLM recommendation
):
"""
Submit a room photo for AI redesign using dynamic keyword → LLM → ComfyUI pipeline.
Two modes:
1. KEYWORD MODE (recommended): Provide keywords + room_type, LLM generates prompt
2. DIRECT MODE: Provide custom_positive + custom_negative to bypass LLM
Returns job_id for async polling.
"""
job_id = str(uuid.uuid4())
jobs[job_id] = {"status": "uploading", "created": time.time()}
try:
# Upload image to ComfyUI
data = await image.read()
filename = f"dw_{job_id[:8]}_{image.filename or 'room.jpg'}"
comfy_name = await upload_to_comfy(data, filename)
jobs[job_id]["status"] = "expanding_prompt"
# ── Determine prompt ──────────────────────────────────────────────
if custom_positive:
# Direct mode — user provided prompts explicitly
from dataclasses import dataclass
@dataclass
class DirectPrompt:
style_name: str = "custom"
positive_prompt: str = custom_positive
negative_prompt: str = custom_negative or (
"(worst quality, low quality, illustration, 3d render, painting, cartoon, sketch), "
"blurry, distorted, deformed, extra windows, unrealistic lighting, structural changes"
)
cfg: float = cfg_scale or 7.5
denoise: float = denoise or 0.72
steps: int = 30
reasoning: str = "Direct user input"
source: str = "direct"
expanded = DirectPrompt()
elif keywords:
# Keyword mode — expand via LLM
kw_list = [k.strip() for k in keywords.split(",") if k.strip()]
if LLM_AVAILABLE:
expanded = await asyncio.to_thread(
expand_prompt,
keywords=kw_list,
room_type=room_type,
additional_notes=additional_notes
)
else:
expanded = expand_prompt_simple(kw_list, room_type)
# Apply manual overrides if provided
if denoise > 0:
expanded.denoise = denoise
if cfg_scale > 0:
expanded.cfg = cfg_scale
else:
raise HTTPException(status_code=400,
detail="Provide either 'keywords' or 'custom_positive'")
jobs[job_id].update({
"status": "queued",
"style": expanded.style_name,
"prompt_source": expanded.source,
"positive_prompt": expanded.positive_prompt,
"negative_prompt": expanded.negative_prompt,
"room_type": room_type,
})
# Submit workflow
wf = build_workflow(comfy_name, expanded)
prompt_id = await queue_prompt(wf)
jobs[job_id].update({"status": "processing", "prompt_id": prompt_id})
# Start background polling
asyncio.create_task(background_poll(job_id, prompt_id))
return {
"job_id": job_id,
"status": "processing",
"style": expanded.style_name,
"prompt_preview": expanded.positive_prompt[:120] + "...",
"reasoning": expanded.reasoning,
"poll_url": f"/dream-weaver/status/{job_id}",
"result_url": f"/dream-weaver/result/{job_id}"
}
except HTTPException:
raise
except Exception as e:
jobs[job_id] = {"status": "error", "error": str(e)}
logger.error(f"Generation failed: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/dream-weaver/status/{job_id}")
async def status(job_id: str):
job = jobs.get(job_id)
if not job:
raise HTTPException(status_code=404, detail="Job not found")
result = {k: v for k, v in job.items() if k != "output"}
result["ready"] = job.get("status") == "done"
if result["ready"]:
result["result_url"] = f"/dream-weaver/result/{job_id}"
return result
@app.get("/dream-weaver/result/{job_id}")
async def result(job_id: str):
job = jobs.get(job_id)
if not job or job.get("status") != "done":
raise HTTPException(status_code=404, detail="Result not ready")
img = job["output"]
url = (f"{COMFY}/view?filename={img['filename']}"
f"&subfolder={img.get('subfolder','')}&type={img.get('type','output')}")
async with httpx.AsyncClient(timeout=30) as c:
r = await c.get(url)
return StreamingResponse(
io.BytesIO(r.content),
media_type="image/png",
headers={"Content-Disposition": f"attachment; filename=dreamweaver_{job_id[:8]}.png"}
)
@app.post("/dream-weaver/sync")
async def dream_weaver_sync(
image: UploadFile = File(...),
keywords: str = Form(default=""),
room_type: str = Form(default="living_room"),
additional_notes: str = Form(default=""),
custom_positive: str = Form(default=""),
custom_negative: str = Form(default=""),
):
"""
Blocking version — waits up to 120s and returns image bytes directly.
Use for testing. Prefer async /dream-weaver for production.
"""
data = await image.read()
filename = f"sync_{uuid.uuid4().hex[:8]}_{image.filename or 'room.jpg'}"
comfy_name = await upload_to_comfy(data, filename)
if custom_positive:
from dataclasses import dataclass
@dataclass
class _P:
style_name = "custom"
positive_prompt = custom_positive
negative_prompt = custom_negative or "(worst quality, low quality), blurry, structural changes"
cfg = 7.5; denoise = 0.72; steps = 30
reasoning = ""; source = "direct"
expanded = _P()
elif keywords:
kw_list = [k.strip() for k in keywords.split(",") if k.strip()]
expanded = (expand_prompt(kw_list, room_type, additional_notes)
if LLM_AVAILABLE else expand_prompt_simple(kw_list, room_type))
else:
raise HTTPException(status_code=400, detail="Provide keywords or custom_positive")
wf = build_workflow(comfy_name, expanded)
prompt_id = await queue_prompt(wf)
img, err = await poll_result(prompt_id, timeout=120)
if err:
raise HTTPException(status_code=500, detail=str(err))
url = (f"{COMFY}/view?filename={img['filename']}"
f"&subfolder={img.get('subfolder','')}&type={img.get('type','output')}")
async with httpx.AsyncClient(timeout=30) as c:
r = await c.get(url)
return StreamingResponse(io.BytesIO(r.content), media_type="image/png",
headers={"X-Style": expanded.style_name,
"X-Prompt-Source": expanded.source})
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", "8082")), log_level="info")
#!/usr/bin/env python3
"""
Dream Weaver API Gateway v2 — Dynamic Keyword → Local LLM → ComfyUI Pipeline
========================================================================
Port: 8080 (public-facing)
ComfyUI: localhost:8188 (internal)
NEW IN v2:
- POST /dream-weaver now accepts keywords[] + room_type for LLM-based prompt generation
- POST /dream-weaver/expand — expand keywords to prompt WITHOUT generating (preview)
- GET /room-types — list available room types
- Uses local Ollama model (qwen3.5:27b) for prompt expansion (no cloud API dependencies)
Environment variables:
OLLAMA_URL — Ollama server (default: http://localhost:11434)
OLLAMA_MODEL — Model name (default: qwen3.5:27b)
"""
import asyncio, json, time, uuid, io, sys, os, logging
from pathlib import Path
from typing import Optional, List
import httpx
import uvicorn
from fastapi import FastAPI, UploadFile, File, HTTPException, Form, BackgroundTasks
from fastapi.responses import JSONResponse, StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
# Add scripts dir to path so we can import prompt_expander
SCRIPTS_DIR = Path(__file__).parent / "scripts"
sys.path.insert(0, str(SCRIPTS_DIR))
try:
from prompt_expander import expand_prompt, expand_prompt_simple, ROOM_CONTEXTS, ExpandedPrompt
LLM_AVAILABLE = True
except ImportError:
LLM_AVAILABLE = False
logging.warning("prompt_expander not found — LLM expansion disabled")
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger("DreamWeaverGateway")
COMFY = "http://127.0.0.1:8188"
COMFY_ROOT = "/opt/dlami/nvme/ComfyUI"
app = FastAPI(
title="Dream Weaver API v2",
version="2.0.0",
description="Dynamic keyword-to-interior-design generation powered by LLM + ComfyUI"
)
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
# In-memory job store (swap for Redis in production)
jobs: dict = {}
# ─── Models ──────────────────────────────────────────────────────────────────
class ExpandRequest(BaseModel):
keywords: List[str]
room_type: str = "living_room"
additional_notes: str = ""
class ExpandResponse(BaseModel):
style_name: str
positive_prompt: str
negative_prompt: str
cfg: float
denoise: float
steps: int
reasoning: str
source: str
# ─── ComfyUI helpers ──────────────────────────────────────────────────────────
async def upload_to_comfy(data: bytes, filename: str) -> str:
async with httpx.AsyncClient(timeout=30) as client:
r = await client.post(f"{COMFY}/upload/image",
files={"image": (filename, data, "image/jpeg")},
data={"overwrite": "true"})
r.raise_for_status()
return r.json()["name"]
def build_workflow(img_name: str, expanded: "ExpandedPrompt") -> dict:
"""Build ComfyUI API workflow from an ExpandedPrompt result."""
return {
"1": {"class_type": "CheckpointLoaderSimple",
"inputs": {"ckpt_name": "realvisxlV50_v50LightningBakedvae.safetensors"}},
"2": {"class_type": "LoadImage",
"inputs": {"image": img_name, "upload": "image"}},
"3": {"class_type": "CLIPTextEncode", # Positive prompt
"inputs": {"text": expanded.positive_prompt, "clip": ["1", 1]}},
"4": {"class_type": "CLIPTextEncode", # Negative prompt
"inputs": {"text": expanded.negative_prompt, "clip": ["1", 1]}},
"5": {"class_type": "VAEEncode",
"inputs": {"pixels": ["2", 0], "vae": ["1", 2]}},
"6": {"class_type": "KSampler",
"inputs": {"model": ["1", 0],
"positive": ["3", 0],
"negative": ["4", 0],
"latent_image": ["5", 0],
"seed": int(time.time()) % 999983,
"steps": expanded.steps,
"cfg": expanded.cfg,
"sampler_name": "dpmpp_2m",
"scheduler": "karras",
"denoise": expanded.denoise}},
"7": {"class_type": "VAEDecode",
"inputs": {"samples": ["6", 0], "vae": ["1", 2]}},
"8": {"class_type": "SaveImage",
"inputs": {"images": ["7", 0],
"filename_prefix": f"dw_{expanded.style_name.replace(' ', '_')[:30]}"}},
}
async def queue_prompt(workflow: dict) -> str:
async with httpx.AsyncClient(timeout=30) as client:
r = await client.post(f"{COMFY}/prompt",
json={"prompt": workflow, "client_id": str(uuid.uuid4())})
r.raise_for_status()
return r.json()["prompt_id"]
async def poll_result(prompt_id: str, timeout: int = 300):
start = time.time()
async with httpx.AsyncClient(timeout=10) as client:
while time.time() - start < timeout:
r = await client.get(f"{COMFY}/history/{prompt_id}")
if r.status_code == 200:
h = r.json().get(prompt_id, {})
if h.get("status", {}).get("status_str") == "error":
return None, h.get("status", {}).get("messages", ["unknown"])
imgs = [img for nd in h.get("outputs", {}).values()
for img in nd.get("images", [])]
if imgs:
return imgs[0], None
await asyncio.sleep(2)
return None, "timeout"
async def background_poll(job_id: str, prompt_id: str):
img, err = await poll_result(prompt_id)
if img:
jobs[job_id].update({"status": "done", "output": img, "completed": time.time()})
else:
jobs[job_id].update({"status": "error", "error": str(err)})
# ─── Endpoints ───────────────────────────────────────────────────────────────
@app.get("/health")
async def health():
comfy_ok = False
try:
async with httpx.AsyncClient(timeout=5) as c:
r = await c.get(f"{COMFY}/system_stats")
comfy_ok = r.status_code == 200
except Exception:
pass
return {
"status": "ok",
"comfyui": comfy_ok,
"gpu": "4x NVIDIA L4 (96GB VRAM)",
"model": "RealVisXL V5.0 Lightning",
"llm_expansion": LLM_AVAILABLE,
"version": "2.0.0"
}
@app.get("/room-types")
async def room_types():
"""List all supported room types with their context."""
if not LLM_AVAILABLE:
return {"room_types": ["bedroom", "living_room", "bathroom", "kitchen",
"dining_room", "home_office", "hallway", "balcony"]}
return {
"room_types": {
k: {
"description": v["description"],
"key_elements": v["key_elements"]
}
for k, v in ROOM_CONTEXTS.items()
}
}
@app.post("/dream-weaver/expand", response_model=ExpandResponse)
async def expand_endpoint(req: ExpandRequest):
"""
Preview the LLM-generated prompt WITHOUT submitting to ComfyUI.
Use this to let the user review/edit the prompt before generating.
Request body:
{
"keywords": ["blue marble", "gold veins", "renaissance", "sharp contours"],
"room_type": "bedroom",
"additional_notes": "luxury hotel feel"
}
"""
if not req.keywords:
raise HTTPException(status_code=400, detail="keywords list cannot be empty")
try:
if LLM_AVAILABLE:
result = await asyncio.to_thread(
expand_prompt,
keywords=req.keywords,
room_type=req.room_type,
additional_notes=req.additional_notes
)
else:
result = expand_prompt_simple(req.keywords, req.room_type)
except Exception as e:
logger.error(f"Prompt expansion failed: {e}")
raise HTTPException(status_code=500, detail=f"LLM expansion failed: {str(e)}")
return ExpandResponse(
style_name=result.style_name,
positive_prompt=result.positive_prompt,
negative_prompt=result.negative_prompt,
cfg=result.cfg,
denoise=result.denoise,
steps=result.steps,
reasoning=result.reasoning,
source=result.source
)
@app.post("/dream-weaver")
async def dream_weaver(
image: UploadFile = File(...),
# ── Dynamic keyword mode (new) ──
keywords: str = Form(default=""), # comma-separated: "blue marble, gold, renaissance"
room_type: str = Form(default="living_room"),
additional_notes: str = Form(default=""),
# ── Optional overrides ──
custom_positive: str = Form(default=""), # skip LLM, use this prompt directly
custom_negative: str = Form(default=""),
denoise: float = Form(default=0.0), # 0.0 = use LLM recommendation
cfg_scale: float = Form(default=0.0), # 0.0 = use LLM recommendation
):
"""
Submit a room photo for AI redesign using dynamic keyword → LLM → ComfyUI pipeline.
Two modes:
1. KEYWORD MODE (recommended): Provide keywords + room_type, LLM generates prompt
2. DIRECT MODE: Provide custom_positive + custom_negative to bypass LLM
Returns job_id for async polling.
"""
job_id = str(uuid.uuid4())
jobs[job_id] = {"status": "uploading", "created": time.time()}
try:
# Upload image to ComfyUI
data = await image.read()
filename = f"dw_{job_id[:8]}_{image.filename or 'room.jpg'}"
comfy_name = await upload_to_comfy(data, filename)
jobs[job_id]["status"] = "expanding_prompt"
# ── Determine prompt ──────────────────────────────────────────────
if custom_positive:
# Direct mode — user provided prompts explicitly
from dataclasses import dataclass
@dataclass
class DirectPrompt:
style_name: str = "custom"
positive_prompt: str = custom_positive
negative_prompt: str = custom_negative or (
"(worst quality, low quality, illustration, 3d render, painting, cartoon, sketch), "
"blurry, distorted, deformed, extra windows, unrealistic lighting, structural changes"
)
cfg: float = cfg_scale or 7.5
denoise: float = denoise or 0.72
steps: int = 30
reasoning: str = "Direct user input"
source: str = "direct"
expanded = DirectPrompt()
elif keywords:
# Keyword mode — expand via LLM
kw_list = [k.strip() for k in keywords.split(",") if k.strip()]
if LLM_AVAILABLE:
expanded = await asyncio.to_thread(
expand_prompt,
keywords=kw_list,
room_type=room_type,
additional_notes=additional_notes
)
else:
expanded = expand_prompt_simple(kw_list, room_type)
# Apply manual overrides if provided
if denoise > 0:
expanded.denoise = denoise
if cfg_scale > 0:
expanded.cfg = cfg_scale
else:
raise HTTPException(status_code=400,
detail="Provide either 'keywords' or 'custom_positive'")
jobs[job_id].update({
"status": "queued",
"style": expanded.style_name,
"prompt_source": expanded.source,
"positive_prompt": expanded.positive_prompt,
"negative_prompt": expanded.negative_prompt,
"room_type": room_type,
})
# Submit workflow
wf = build_workflow(comfy_name, expanded)
prompt_id = await queue_prompt(wf)
jobs[job_id].update({"status": "processing", "prompt_id": prompt_id})
# Start background polling
asyncio.create_task(background_poll(job_id, prompt_id))
return {
"job_id": job_id,
"status": "processing",
"style": expanded.style_name,
"prompt_preview": expanded.positive_prompt[:120] + "...",
"reasoning": expanded.reasoning,
"poll_url": f"/dream-weaver/status/{job_id}",
"result_url": f"/dream-weaver/result/{job_id}"
}
except HTTPException:
raise
except Exception as e:
jobs[job_id] = {"status": "error", "error": str(e)}
logger.error(f"Generation failed: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/dream-weaver/status/{job_id}")
async def status(job_id: str):
job = jobs.get(job_id)
if not job:
raise HTTPException(status_code=404, detail="Job not found")
result = {k: v for k, v in job.items() if k != "output"}
result["ready"] = job.get("status") == "done"
if result["ready"]:
result["result_url"] = f"/dream-weaver/result/{job_id}"
return result
@app.get("/dream-weaver/result/{job_id}")
async def result(job_id: str):
job = jobs.get(job_id)
if not job or job.get("status") != "done":
raise HTTPException(status_code=404, detail="Result not ready")
img = job["output"]
url = (f"{COMFY}/view?filename={img['filename']}"
f"&subfolder={img.get('subfolder','')}&type={img.get('type','output')}")
async with httpx.AsyncClient(timeout=30) as c:
r = await c.get(url)
return StreamingResponse(
io.BytesIO(r.content),
media_type="image/png",
headers={"Content-Disposition": f"attachment; filename=dreamweaver_{job_id[:8]}.png"}
)
@app.post("/dream-weaver/sync")
async def dream_weaver_sync(
image: UploadFile = File(...),
keywords: str = Form(default=""),
room_type: str = Form(default="living_room"),
additional_notes: str = Form(default=""),
custom_positive: str = Form(default=""),
custom_negative: str = Form(default=""),
):
"""
Blocking version — waits up to 120s and returns image bytes directly.
Use for testing. Prefer async /dream-weaver for production.
"""
data = await image.read()
filename = f"sync_{uuid.uuid4().hex[:8]}_{image.filename or 'room.jpg'}"
comfy_name = await upload_to_comfy(data, filename)
if custom_positive:
from dataclasses import dataclass
@dataclass
class _P:
style_name = "custom"
positive_prompt = custom_positive
negative_prompt = custom_negative or "(worst quality, low quality), blurry, structural changes"
cfg = 7.5; denoise = 0.72; steps = 30
reasoning = ""; source = "direct"
expanded = _P()
elif keywords:
kw_list = [k.strip() for k in keywords.split(",") if k.strip()]
expanded = (expand_prompt(kw_list, room_type, additional_notes)
if LLM_AVAILABLE else expand_prompt_simple(kw_list, room_type))
else:
raise HTTPException(status_code=400, detail="Provide keywords or custom_positive")
wf = build_workflow(comfy_name, expanded)
prompt_id = await queue_prompt(wf)
img, err = await poll_result(prompt_id, timeout=120)
if err:
raise HTTPException(status_code=500, detail=str(err))
url = (f"{COMFY}/view?filename={img['filename']}"
f"&subfolder={img.get('subfolder','')}&type={img.get('type','output')}")
async with httpx.AsyncClient(timeout=30) as c:
r = await c.get(url)
return StreamingResponse(io.BytesIO(r.content), media_type="image/png",
headers={"X-Style": expanded.style_name,
"X-Prompt-Source": expanded.source})
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", "8082")), log_level="info")