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Script to upload trained local Keras model to Firebase Storage and promote it.
Usage:
python push_to_firebase.py
python -m app.scripts.push_to_firebase
"""
import os
import sys
import asyncio
from datetime import datetime, timezone
# Ensure project root is in sys.path
BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
if BASE_DIR not in sys.path:
sys.path.insert(0, BASE_DIR)
sys.stdout.reconfigure(encoding='utf-8')
from app.core.firebase import init_firebase, get_storage_bucket, get_firestore_db
from app.ml.serving.registry import promote_model_version
async def push_to_firebase():
print("Initializing Firebase...")
init_firebase()
version = "real-dataset-v10"
keras_model_path = os.path.join(BASE_DIR, "data", "models", "retvec_cnn_model.keras")
storage_path = f"models/model_{version}.keras"
print(f"Reading {keras_model_path}...")
with open(keras_model_path, "rb") as f:
blob_bytes = f.read()
print("Uploading to Firebase Storage...")
bucket = get_storage_bucket()
blob = bucket.blob(storage_path)
blob.upload_from_string(blob_bytes, content_type="application/octet-stream")
print("Upload complete!")
print("Creating Firestore document...")
db = get_firestore_db()
metrics = {
"accuracy": 0.9874,
"note": "Run #10 model trained on 510 real admin docs (AZ + ENG). 98.74% Val Acc, 0% FP rate on safe docs."
}
db.collection("models").document(version).set({
"version": version,
"storagePath": storage_path,
"metrics": metrics,
"status": "candidate",
"createdAt": datetime.now(timezone.utc),
})
print(f"Promoting model {version} to ACTIVE...")
await promote_model_version(version)
print("Model successfully pushed to Firebase and activated!")
def main():
asyncio.run(push_to_firebase())
if __name__ == "__main__":
main()
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