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File size: 2,444 Bytes
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"""
Download model during Docker build on Hugging Face Spaces
"""
import os
import requests
import gc
R2_BASE_URL = "https://r2-worker.eczemanage.workers.dev"
OUTPUT_DIR = "./derm_foundation/"
def download_model():
print("=" * 70)
print("DOWNLOADING DERM FOUNDATION MODEL (Hugging Face Spaces Build)")
print("=" * 70)
os.makedirs(OUTPUT_DIR, exist_ok=True)
files = [
"saved_model.pb",
"variables/variables.index",
"variables/variables.data-00000-of-00001"
]
for file_path in files:
print(f"\n📥 Downloading {file_path}...")
url = f"{R2_BASE_URL}/{file_path}"
local_path = os.path.join(OUTPUT_DIR, file_path)
os.makedirs(os.path.dirname(local_path), exist_ok=True)
try:
with requests.get(url, stream=True, timeout=1800) as r:
r.raise_for_status()
total_size = int(r.headers.get('content-length', 0))
downloaded = 0
chunk_count = 0
with open(local_path, 'wb') as f:
for chunk in r.iter_content(chunk_size=2*1024*1024): # 2MB chunks (HF has RAM)
if chunk:
f.write(chunk)
f.flush()
downloaded += len(chunk)
chunk_count += 1
if chunk_count % 5 == 0:
gc.collect()
if total_size > 0 and chunk_count % 10 == 0:
progress = (downloaded / total_size) * 100
mb_downloaded = downloaded / (1024*1024)
mb_total = total_size / (1024*1024)
print(f" Progress: {progress:.1f}% ({mb_downloaded:.1f}/{mb_total:.1f} MB)")
gc.collect()
print(f"✅ Successfully downloaded: {file_path}")
except Exception as e:
print(f"❌ Error downloading {file_path}: {e}")
raise
print("\n" + "=" * 70)
print("✅ MODEL DOWNLOAD COMPLETE! Ready to serve predictions.")
print("=" * 70)
if __name__ == "__main__":
download_model()
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