Spaces:
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Typhoid HDT v4.1: Automated Global Deployment
Automates Hugging Face Spaces creation and Kaggle Dataset upload.
"""
import os
import subprocess
import sys
import shutil
import json
from pathlib import Path
from huggingface_hub import HfApi, create_repo, upload_folder
BASE_DIR = Path(__file__).parent.parent
def deploy_hf():
token = os.getenv("HF_TOKEN")
if not token:
print("❌ HF_TOKEN not found in environment variables.")
return False
print("🚀 Deploying to Hugging Face Spaces...")
api = HfApi()
repo_id = "hssling/typhoid_drug_discovery_model"
try:
# Check if repo exists
try:
api.repo_info(repo_id=repo_id, repo_type="space", token=token)
print(f" Repo {repo_id} already exists. Skipping creation.")
except:
print(f" Creating repo {repo_id}...")
api.create_repo(
repo_id=repo_id,
token=token,
repo_type="space",
space_sdk="docker",
exist_ok=True
)
# Prepare README for HF (must be named README.md at root)
temp_readme = BASE_DIR / 'README_HF_TEMP.md'
shutil.copy(BASE_DIR / 'HF_README.md', temp_readme)
# Upload project files
print("Uploading project files...")
api.upload_folder(
folder_path=str(BASE_DIR),
repo_id=repo_id,
repo_type="space",
token=token,
ignore_patterns=[".git*", "__pycache__*", "venv*", "*.pdb", ".gemini*", "README.md"], # Ignore original README
path_in_repo=""
)
# Upload the HF-specific README as THE README.md in the repo
api.upload_file(
path_or_fileobj=str(temp_readme),
path_in_repo="README.md",
repo_id=repo_id,
repo_type="space",
token=token
)
# Clean up
if temp_readme.exists(): os.remove(temp_readme)
print(f"✅ Successfully deployed to: https://huggingface.co/spaces/{repo_id}")
return True
except Exception as e:
print(f"❌ HF Deployment Failed: {e}")
return False
def deploy_kaggle():
user = os.getenv("KAGGLE_USERNAME")
key = os.getenv("KAGGLE_KEY")
if not user or not key:
print("❌ KAGGLE_USERNAME or KAGGLE_KEY not found.")
return False
print("📊 Deploying to Kaggle Datasets...")
# Kaggle expects the kaggle.json in ~/.kaggle/ on Linux or %USERPROFILE%\.kaggle\ on Windows
# We will try to run the CLI directly if possible
try:
# Create a temp folder for upload
upload_dir = BASE_DIR / 'kaggle_upload'
upload_dir.mkdir(exist_ok=True)
# Copy essential tables
shutil.copy(BASE_DIR / 'outputs' / 'tables' / 'targets_ranked_v5_global.csv', upload_dir / 'global_priorities.csv')
shutil.copy(BASE_DIR / 'data' / 'gene_signature_verified.csv', upload_dir / 'gene_signature.csv')
shutil.copy(BASE_DIR / 'kaggle_metadata.json', upload_dir / 'dataset-metadata.json')
# Run Kaggle command
# Use python -m kaggle.cli to ensure the command is recognized in the environment
cmd = [sys.executable, "-m", "kaggle.cli", "datasets", "create", "-p", str(upload_dir)]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode == 0:
print(f"✅ Successfully deployed to Kaggle!")
return True
else:
# Try update instead
cmd_upd = [sys.executable, "-m", "kaggle.cli", "datasets", "version", "-p", str(upload_dir), "-m", "v4.1 update"]
result = subprocess.run(cmd_upd, capture_output=True, text=True)
if result.returncode == 0:
print(f"✅ Successfully updated Kaggle dataset version!")
return True
print(f"❌ Kaggle Deployment Failed: {result.stderr}")
return False
except Exception as e:
print(f"❌ Kaggle Deployment Error: {e}")
return False
def main():
print("="*60)
print("GLOBAL DEPLOYMENT AUTOMATION v4.1")
print("="*60)
hf_success = deploy_hf()
print("-" * 30)
kg_success = deploy_kaggle()
print("\n" + "="*60)
print("DEPLOYMENT SUMMARY")
print(f"Hugging Face: {'✅ SUCCESS' if hf_success else '❌ FAILED (Check Credentials)'}")
print(f"Kaggle: {'✅ SUCCESS' if kg_success else '❌ FAILED (Check Credentials)'}")
print("="*60)
if __name__ == "__main__":
import shutil
main()
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