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modal_app.py — Phase 9: Modal.com Deployment (Modal v1.5+)
------------------------------------------------------------
Deploy: python -m modal deploy modal_app.py
Test: python -m modal serve modal_app.py
"""
import modal
# ── Source files to include (explicit list — no large dirs) ───────────────────
SOURCE_FILES = [
"app.py",
"predictor.py",
"lang_detector.py",
]
# ── Build container image ─────────────────────────────────────────────────────
image = (
modal.Image.debian_slim(python_version="3.10")
.pip_install(
"fastapi==0.110.0",
"uvicorn==0.27.0",
"pydantic==2.6.0",
"torch==2.2.0",
"transformers==4.40.0",
"huggingface_hub==0.22.0",
"numpy==1.26.0",
"pandas==2.2.0",
"scikit-learn==1.4.0",
"accelerate==0.29.0",
)
# Copy only the essential Python source files (not models/, urdu_env/, etc.)
.add_local_file("app.py", remote_path="/app/app.py")
.add_local_file("predictor.py", remote_path="/app/predictor.py")
.add_local_file("lang_detector.py",remote_path="/app/lang_detector.py")
# Copy frontend assets
.add_local_dir("templates", remote_path="/app/templates")
.add_local_dir("static", remote_path="/app/static")
)
# ── Modal app ─────────────────────────────────────────────────────────────────
app = modal.App("urdu-sentiment-engine", image=image)
@app.function(
min_containers=1, # Keep one warm to avoid cold starts
memory=4096, # 4GB RAM for two XLM-RoBERTa models (~2.2GB)
cpu=2.0,
timeout=300, # Allow 5min on cold start for model download from HF Hub
)
@modal.asgi_app()
def fastapi_app():
import sys
sys.path.insert(0, "/app")
from app import app as _app
return _app
|