modelblob.py

# modelblob.py
from fastapi import APIRouter, Request
from fastapi.responses import HTMLResponse, JSONResponse
import json, os, pathlib

router = APIRouter()

LOCAL_BLOB_PATH = os.getenv("MODEL_BLOB_PATH", "/tmp/model_blob.json")

# ---- EMBEDDED MODEL BLOB (exactly as provided) ----
EMBEDDED_MODEL_BLOB = {
  "name": "publishers/hf-misri/models/realismenginesdxl_v30vae",
  "versionId": "001",
  "openSourceCategory": "THIRD_PARTY_OWNED_OSS",
  "supportedActions": {
    "deploy": {
      "modelDisplayName": "misri/realismEngineSDXL_v30VAE",
      "containerSpec": {
        "imageUri": "us-docker.pkg.dev/deeplearning-platform-release/vertex-model-garden/hf-inference-toolkit.cu125.0-1.ubuntu2204.py311:model-garden.hf-inference-toolkit-0-1-release_20250927.00_p0",
        "env": [
          {"name": "HF_TASK","value": "text-to-image"},
          {"name": "MODEL_ID","value": "misri/realismEngineSDXL_v30VAE"},
          {"name": "HF_MODEL_ID","value": "misri/realismEngineSDXL_v30VAE"},
          {"name": "HF_REVISION","value": "7d2f2de544b4aa26148b3a16b3469ed6dbb38a5c"},
          {"name": "DEPLOY_SOURCE","value": "UI_HF_VERIFIED_MODEL"}
        ],
        "ports": [{"containerPort": 8080}]
      },
      "dedicatedResources": {
        "machineSpec": {
          "machineType": "a3-highgpu-1g",
          "acceleratorType": "NVIDIA_H100_80GB",
          "acceleratorCount": 1
        },
        "maxReplicaCount": 1
      },
      "deployTaskName": "1 NVIDIA_H100_80GB a3-highgpu-1g",
      "deployMetadata": {}
    },
    "multiDeployVertex": {
      "multiDeployVertex": [
        {
          "modelDisplayName": "misri/realismEngineSDXL_v30VAE",
          "containerSpec": {
            "imageUri": "us-docker.pkg.dev/deeplearning-platform-release/vertex-model-garden/hf-inference-toolkit.cu125.0-1.ubuntu2204.py311:model-garden.hf-inference-toolkit-0-1-release_20250927.00_p0",
            "env": [
              {"name": "HF_TASK","value": "text-to-image"},
              {"name": "MODEL_ID","value": "misri/realismEngineSDXL_v30VAE"},
              {"name": "HF_MODEL_ID","value": "misri/realismEngineSDXL_v30VAE"},
              {"name": "HF_REVISION","value": "7d2f2de544b4aa26148b3a16b3469ed6dbb38a5c"},
              {"name": "DEPLOY_SOURCE","value": "UI_HF_VERIFIED_MODEL"}
            ],
            "ports": [{"containerPort": 8080}]
          },
          "dedicatedResources": {
            "machineSpec": {
              "machineType": "a3-highgpu-1g",
              "acceleratorType": "NVIDIA_H100_80GB",
              "acceleratorCount": 1
            },
            "maxReplicaCount": 1
          },
          "deployTaskName": "1 NVIDIA_H100_80GB a3-highgpu-1g",
          "deployMetadata": {}
        },
        {
          "modelDisplayName": "misri/realismEngineSDXL_v30VAE",
          "containerSpec": {
            "imageUri": "us-docker.pkg.dev/deeplearning-platform-release/vertex-model-garden/hf-inference-toolkit.cu125.0-1.ubuntu2204.py311:model-garden.hf-inference-toolkit-0-1-release_20250927.00_p0",
            "env": [
              {"name": "HF_TASK","value": "text-to-image"},
              {"name": "MODEL_ID","value": "misri/realismEngineSDXL_v30VAE"},
              {"name": "HF_MODEL_ID","value": "misri/realismEngineSDXL_v30VAE"},
              {"name": "HF_REVISION","value": "7d2f2de544b4aa26148b3a16b3469ed6dbb38a5c"},
              {"name": "DEPLOY_SOURCE","value": "UI_HF_VERIFIED_MODEL"}
            ],
            "ports": [{"containerPort": 8080}]
          },
          "dedicatedResources": {
            "machineSpec": {
              "machineType": "g2-standard-12",
              "acceleratorType": "NVIDIA_L4",
              "acceleratorCount": 1
            },
            "maxReplicaCount": 1
          },
          "deployTaskName": "1 NVIDIA_L4 g2-standard-12",
          "deployMetadata": {}
        }
      ]
    }
  }
}
# ---------------------------------------------------

def _ensure_dir(p: str):
    pathlib.Path(p).parent.mkdir(parents=True, exist_ok=True)

def _pretty(obj) -> str:
    try:
        return json.dumps(obj, indent=2)
    except Exception:
        return str(obj)

@router.get("/modelblob", response_class=HTMLResponse)
def view_model_blob():
    """
    Render: if /tmp/model_blob.json exists, show it; otherwise show the embedded blob.
    """
    try:
        if os.path.exists(LOCAL_BLOB_PATH):
            raw = pathlib.Path(LOCAL_BLOB_PATH).read_text(encoding="utf-8")
            try:
                disp = json.dumps(json.loads(raw), indent=2)
            except json.JSONDecodeError:
                disp = raw
            source = f"File • {LOCAL_BLOB_PATH}"
        else:
            disp = _pretty(EMBEDDED_MODEL_BLOB)
            source = "Embedded (not yet written to file)"
    except Exception as e:
        disp = _pretty({"error": str(e)})
        source = "Error"

    html = f"""
<!doctype html>
<html>
  <head>
    <meta charset="utf-8"/>
    <title>Model Blob</title>
    <style>
      body {{ font-family: ui-monospace, Menlo, Consolas, monospace; background:#0d1117; color:#c9d1d9; margin:24px; }}
      pre {{ background:#161b22; padding:16px; border-radius:8px; overflow:auto; white-space:pre-wrap; }}
      .row {{ max-width: 1000px; margin:auto; }}
      a {{ color:#58a6ff; text-decoration:none; }}
      .btn {{ display:inline-block; padding:8px 12px; border-radius:6px; background:#238636; color:#fff; margin-right:8px; }}
      .btn.secondary {{ background:#444; }}
    </style>
  </head>
  <body>
    <div class="row">
      <h2>Current Model Blob <small style="font-size:12px; color:#8b949e;">({source})</small></h2>
      <div style="margin:10px 0;">
        <a class="btn" href="/modelblob/write">Write to /tmp/model_blob.json</a>
        <a class="btn secondary" href="/">Back</a>
      </div>
      <pre>{disp}</pre>
    </div>
  </body>
</html>
"""
    return HTMLResponse(html)

@router.get("/modelblob/write")
def write_model_blob():
    """
    Write the embedded blob verbatim to LOCAL_BLOB_PATH.
    """
    try:
        _ensure_dir(LOCAL_BLOB_PATH)
        pathlib.Path(LOCAL_BLOB_PATH).write_text(_pretty(EMBEDDED_MODEL_BLOB), encoding="utf-8")
        return JSONResponse({"ok": True, "path": LOCAL_BLOB_PATH})
    except Exception as e:
        return JSONResponse({"error": str(e)}, 500)

@router.post("/modelblob/overwrite")
async def overwrite_blob(req: Request):
    """
    Overwrite the file with a posted blob (JSON or form 'blob').
    """
    try:
        ctype = req.headers.get("content-type","")
        if "application/json" in ctype:
            data = await req.json()
            txt = data if isinstance(data, (dict, list)) else data.get("blob")
        else:
            form = await req.form()
            txt = form.get("blob")

        if isinstance(txt, (dict, list)):
            out = json.dumps(txt, indent=2)
        elif isinstance(txt, str) and txt.strip():
            out = txt
        else:
            return JSONResponse({"error": "Missing valid blob"}, 400)

        _ensure_dir(LOCAL_BLOB_PATH)
        pathlib.Path(LOCAL_BLOB_PATH).write_text(out, encoding="utf-8")
        return JSONResponse({"ok": True, "path": LOCAL_BLOB_PATH})
    except Exception as e:
        return JSONResponse({"error": str(e)}, 500)
    # ---------------------------------------------------------------------
# JSON outlet for backend ingestion
# ---------------------------------------------------------------------
@router.get("/modelblob.json")
def modelblob_json():
    """Return the current model blob as JSON for backend ingestion."""
    import os, json, pathlib
    path = os.getenv("MODEL_BLOB_PATH", "/tmp/model_blob.json")
    try:
        if os.path.exists(path):
            return json.loads(pathlib.Path(path).read_text(encoding="utf-8"))
    except Exception:
        pass
    # fallback to embedded blob if file missing or unreadable
    return EMBEDDED_MODEL_BLOB