Upload api/main.py
Browse files- api/main.py +157 -0
api/main.py
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"""FastAPI backend for InteriorFusion inference service."""
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import os
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import tempfile
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from pathlib import Path
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from typing import List, Optional
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import torch
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from fastapi import FastAPI, File, Form, UploadFile
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import FileResponse, JSONResponse
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from PIL import Image
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import io
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import base64
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from interiorfusion.pipelines import InteriorFusionPipeline, InteriorFusionOutput
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app = FastAPI(
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title="InteriorFusion API",
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description="Single image to 3D interior scene generation",
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version="0.1.0",
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)
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# CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Global pipeline instance
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_pipeline: Optional[InteriorFusionPipeline] = None
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def get_pipeline() -> InteriorFusionPipeline:
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global _pipeline
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if _pipeline is None:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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_pipeline = InteriorFusionPipeline(
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model_size="L",
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device=device,
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dtype=torch.float16,
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)
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return _pipeline
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@app.post("/generate")
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async def generate_3d_scene(
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image: UploadFile = File(...),
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room_type: Optional[str] = Form(None),
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style: Optional[str] = Form(None),
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formats: str = Form("glb,ply"),
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model_size: str = Form("L"),
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):
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"""
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Generate a 3D interior scene from a single image.
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Returns download links for the generated 3D files.
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"""
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# Read image
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contents = await image.read()
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img = Image.open(io.BytesIO(contents)).convert("RGB")
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# Parse formats
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output_formats = [f.strip() for f in formats.split(",")]
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# Run pipeline
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pipeline = get_pipeline()
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output = pipeline(
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image=img,
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room_type_hint=room_type,
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style_hint=style,
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)
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# Export
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output_dir = tempfile.mkdtemp()
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output.export_all(output_dir)
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# Collect file paths
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files = {}
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for fmt in output_formats:
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path = Path(output_dir) / f"scene.{fmt}"
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if path.exists():
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files[fmt] = str(path)
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return JSONResponse({
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"success": True,
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"room_type": output.room_type,
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"style": output.style,
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"processing_time": output.processing_time,
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"num_objects": len(output.object_meshes),
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"files": files,
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})
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@app.post("/edit")
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async def edit_scene(
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scene_glb: UploadFile = File(...),
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edit_action: str = Form(...), # "move", "replace", "remove", "add"
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object_id: Optional[int] = Form(None),
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new_image: Optional[UploadFile] = File(None),
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position: Optional[str] = Form(None), # JSON array [x, y, z]
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):
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"""
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Edit an existing scene.
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Actions:
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- move: Move an existing object
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- replace: Replace an object with a new one
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- remove: Remove an object
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- add: Add a new object
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"""
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import json
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pipeline = get_pipeline()
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# Parse position
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pos = None
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if position:
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pos = json.loads(position)
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# Build edit dict
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edit = {"action": edit_action}
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if object_id is not None:
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edit["object_id"] = object_id
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if pos:
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edit["position"] = pos
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if new_image:
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contents = await new_image.read()
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edit["new_image"] = Image.open(io.BytesIO(contents)).convert("RGB")
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# For simplicity, return not-implemented
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return JSONResponse({
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"success": False,
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"message": "Scene editing API coming soon",
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})
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@app.get("/health")
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async def health_check():
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"""Health check endpoint."""
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return {"status": "ok", "version": "0.1.0"}
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@app.get("/download/{filename}")
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async def download_file(filename: str):
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"""Download a generated file."""
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# In production, use proper file storage (S3, etc.)
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# For now, placeholder
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return JSONResponse({"message": f"Download {filename} from storage"})
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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