Integrate FastAI colorization with Firebase auth and Gradio UI - Replace main.py with FastAI implementation - Add Gradio interface for Space UI - Add Firebase authentication to /colorize endpoint - Add curl examples documentation - Update test.py with User-Agent headers

Detect PyTorch models and provide clear error message - Repository contains generator.pt (PyTorch) not FastAI model

List repository files to find actual model filename instead of guessing

Add fallback mechanism to manually download and load FastAI model if from_pretrained_fastai fails

Fix cache directory permissions and OMP_NUM_THREADS warnings - Set HF cache env vars before imports - Set MPLCONFIGDIR for matplotlib - Fix OMP_NUM_THREADS in Dockerfile

Switch to FastAI GAN colorization model (Hammad712/GAN-Colorization-Model)

Restore local ControlNet colorization pipeline

Point inference requests to new HF router endpoint

Update cache paths to use /tmp

Switch colorization to HF Inference API

Improve SDXL auth error logging

Align pipeline with text-guided colorization Space

Increase img2img strength to improve colorization

Fix colorize pipeline call and fallback storage directories

Improve HF cache directory fallback to use /tmp when /data not writable

Fix model loading: use /data for HF cache directory instead of /app/data

fix(runtime): use /data for uploads/results and write Firebase creds to /data; set DATA_DIR and OMP_NUM_THREADS

fix: write data under /data and move Firebase creds path to /data; mount static from resolved dirs

update Postman collection

Deploy: HF cache dir, img2img fallback, auth bypass, root route

feat(auth): accept Firebase Auth id_token (Authorization Bearer) in addition to App Check; add Postman collection and test script; default MODEL_ID to ControlNet color

Optimize CPU performance: reduce inference steps and guidance scale for faster processing

Remove unused cv2 import and pin numpy version for compatibility

Initial commit: Colorize API with Firebase App Check integration