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import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from backend.api.models.response import StableDiffusionResponse
from backend.base64_image import base64_image_to_pil, pil_image_to_base64_str
from backend.device import get_device_name
from backend.models.device import DeviceInfo
from backend.models.lcmdiffusion_setting import DiffusionTask, LCMDiffusionSetting
from constants import APP_VERSION, DEVICE
from context import Context
from models.interface_types import InterfaceType
from state import get_settings
app_settings = get_settings()
app = FastAPI(
title="AEGIS Bio-Digital Lab 10 - Visual System",
description="Scientific visualization system for pathogen and molecular structure generation",
version=APP_VERSION,
license_info={
"name": "MIT",
"identifier": "MIT",
},
docs_url="/api/docs",
redoc_url="/api/redoc",
openapi_url="/api/openapi.json",
)
print(app_settings.settings.lcm_diffusion_setting)
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
context = Context(InterfaceType.API_SERVER)
@app.get("/api/")
async def root():
return {"message": "Welcome to AEGIS Bio-Digital Lab 10 - Visual System API"}
@app.get(
"/api/ping",
description="Health check endpoint for UptimeRobot monitoring",
summary="Ping endpoint",
)
async def ping():
"""Health check endpoint for monitoring services like UptimeRobot"""
return {
"status": "ok",
"service": "AEGIS Bio-Digital Lab 10 - Visual System",
"version": APP_VERSION,
"device": DEVICE,
}
@app.get(
"/api/health",
description="Detailed health check with system status",
summary="Health check",
)
async def health():
"""Detailed health check endpoint"""
return {
"status": "healthy",
"service": "AEGIS Bio-Digital Lab 10 - Visual System",
"version": APP_VERSION,
"device": DEVICE,
"device_name": get_device_name(),
"platform": platform.system(),
}
@app.get(
"/api/info",
description="Get system information",
summary="Get system information",
)
async def info():
device_info = DeviceInfo(
device_type=DEVICE,
device_name=get_device_name(),
os=platform.system(),
platform=platform.platform(),
processor=platform.processor(),
)
return device_info.model_dump()
@app.get(
"/api/config",
description="Get current configuration",
summary="Get configurations",
)
async def config():
return app_settings.settings
@app.get(
"/api/models",
description="Get available models",
summary="Get available models",
)
async def models():
return {
"lcm_lora_models": app_settings.lcm_lora_models,
"stable_diffusion": app_settings.stable_diffsuion_models,
"openvino_models": app_settings.openvino_lcm_models,
"lcm_models": app_settings.lcm_models,
}
@app.post(
"/api/generate",
description="Generate image(Text to image,Image to Image)",
summary="Generate image(Text to image,Image to Image)",
)
async def generate(diffusion_config: LCMDiffusionSetting) -> StableDiffusionResponse:
app_settings.settings.lcm_diffusion_setting = diffusion_config
if diffusion_config.diffusion_task == DiffusionTask.image_to_image:
app_settings.settings.lcm_diffusion_setting.init_image = base64_image_to_pil(
diffusion_config.init_image
)
images = context.generate_text_to_image(app_settings.settings)
if images:
images_base64 = [pil_image_to_base64_str(img) for img in images]
else:
images_base64 = []
return StableDiffusionResponse(
latency=round(context.latency, 2),
images=images_base64,
error=context.error,
)
@app.post(
"/api/window7/generate-pathogen",
description="Generate virus or bacteria visualization for Window 7 Disease Analysis",
summary="Generate pathogen visualization",
)
async def generate_pathogen_visualization(diffusion_config: LCMDiffusionSetting) -> StableDiffusionResponse:
"""
Generate virus and bacteria visualizations for AEGIS Bio-Digital Lab 10 Window 7.
Used for disease discovery and pathogen identification visualization.
"""
app_settings.settings.lcm_diffusion_setting = diffusion_config
if diffusion_config.diffusion_task == DiffusionTask.image_to_image:
app_settings.settings.lcm_diffusion_setting.init_image = base64_image_to_pil(
diffusion_config.init_image
)
images = context.generate_text_to_image(app_settings.settings)
if images:
images_base64 = [pil_image_to_base64_str(img) for img in images]
else:
images_base64 = []
return StableDiffusionResponse(
latency=round(context.latency, 2),
images=images_base64,
error=context.error,
)
@app.post(
"/api/window7/generate-disease-visualization",
description="Generate disease and infection visualization for Window 7",
summary="Generate disease visualization",
)
async def generate_disease_visualization(diffusion_config: LCMDiffusionSetting) -> StableDiffusionResponse:
"""
Generate disease and infection visualizations for AEGIS Bio-Digital Lab 10 Window 7.
Used for visualizing disease progression, symptoms, and affected areas.
"""
app_settings.settings.lcm_diffusion_setting = diffusion_config
if diffusion_config.diffusion_task == DiffusionTask.image_to_image:
app_settings.settings.lcm_diffusion_setting.init_image = base64_image_to_pil(
diffusion_config.init_image
)
images = context.generate_text_to_image(app_settings.settings)
if images:
images_base64 = [pil_image_to_base64_str(img) for img in images]
else:
images_base64 = []
return StableDiffusionResponse(
latency=round(context.latency, 2),
images=images_base64,
error=context.error,
)
@app.post(
"/api/window9/generate-molecule",
description="Generate molecular structure visualization for Window 9",
summary="Generate molecule visualization",
)
async def generate_molecule_visualization(diffusion_config: LCMDiffusionSetting) -> StableDiffusionResponse:
"""
Generate molecular structure visualizations for AEGIS Bio-Digital Lab 10 Window 9.
Used for drug development visualization after SMILES processing.
"""
app_settings.settings.lcm_diffusion_setting = diffusion_config
# Ensure we're doing text-to-image for molecular structures
if diffusion_config.diffusion_task == DiffusionTask.image_to_image:
app_settings.settings.lcm_diffusion_setting.init_image = base64_image_to_pil(
diffusion_config.init_image
)
images = context.generate_text_to_image(app_settings.settings)
if images:
images_base64 = [pil_image_to_base64_str(img) for img in images]
else:
images_base64 = []
return StableDiffusionResponse(
latency=round(context.latency, 2),
images=images_base64,
error=context.error,
)
@app.post(
"/api/window9/generate-drug-visualization",
description="Generate drug compound visualization for Window 9",
summary="Generate drug visualization",
)
async def generate_drug_visualization(diffusion_config: LCMDiffusionSetting) -> StableDiffusionResponse:
"""
Generate drug compound visualizations for AEGIS Bio-Digital Lab 10 Window 9.
Used for visualizing drug candidates and their properties.
"""
app_settings.settings.lcm_diffusion_setting = diffusion_config
if diffusion_config.diffusion_task == DiffusionTask.image_to_image:
app_settings.settings.lcm_diffusion_setting.init_image = base64_image_to_pil(
diffusion_config.init_image
)
images = context.generate_text_to_image(app_settings.settings)
if images:
images_base64 = [pil_image_to_base64_str(img) for img in images]
else:
images_base64 = []
return StableDiffusionResponse(
latency=round(context.latency, 2),
images=images_base64,
error=context.error,
)
@app.post(
"/api/aegis/generate-scientific",
description="Generate scientific visualization for AEGIS Bio-Digital Lab 10",
summary="Generate scientific visualization",
)
async def generate_scientific_visualization(diffusion_config: LCMDiffusionSetting) -> StableDiffusionResponse:
"""
General scientific visualization endpoint for AEGIS Bio-Digital Lab 10.
Can be used across all windows for generating scientific imagery.
"""
app_settings.settings.lcm_diffusion_setting = diffusion_config
if diffusion_config.diffusion_task == DiffusionTask.image_to_image:
app_settings.settings.lcm_diffusion_setting.init_image = base64_image_to_pil(
diffusion_config.init_image
)
images = context.generate_text_to_image(app_settings.settings)
if images:
images_base64 = [pil_image_to_base64_str(img) for img in images]
else:
images_base64 = []
return StableDiffusionResponse(
latency=round(context.latency, 2),
images=images_base64,
error=context.error,
)
def start_web_server(port: int = 8000):
uvicorn.run(
app,
host="0.0.0.0",
port=port,
)
|