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"""
GraphLLM - Modal Deployment
Serverless ML deployment with auto-scaling
"""
import modal
# Create Modal app
app = modal.App("graphllm")
# Define the container image with all dependencies
image = (
modal.Image.debian_slim(python_version="3.12")
.apt_install("tesseract-ocr", "ghostscript", "gcc", "g++")
.pip_install_from_requirements("requirements.txt")
)
# Create persistent volume for data storage
volume = modal.Volume.from_name("graphllm-data", create_if_missing=True)
# Mount FastAPI app
@app.function(
image=image,
gpu=None, # Use CPU (cheaper)
memory=4096, # 4GB RAM
timeout=600, # 10 min timeout
volumes={"/app/data": volume},
secrets=[modal.Secret.from_name("graphllm-secrets")], # GEMINI_API_KEY
)
@modal.asgi_app()
def fastapi_app():
"""
Mount the FastAPI application
"""
import sys
sys.path.insert(0, "/root")
# Import main FastAPI app
from main import app as fastapi_app
return fastapi_app
# Local testing endpoint
@app.local_entrypoint()
def main():
"""
Test the deployment locally
"""
print("GraphLLM deployed to Modal!")
print("Access your app at: https://YOUR_USERNAME--graphllm-fastapi-app.modal.run")
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