Spaces:
Sleeping
Sleeping
File size: 2,522 Bytes
bec0b04 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | """
MatDeepLearn MCP Service - HuggingFace Space Entry Point
This file provides a FastAPI application for health checks and service info.
The actual MCP service is started via start_mcp.py.
"""
from fastapi import FastAPI
from fastapi.responses import JSONResponse
import os
import sys
# Add project to path
project_root = os.path.dirname(os.path.abspath(__file__))
if project_root not in sys.path:
sys.path.insert(0, project_root)
app = FastAPI(
title="MatDeepLearn MCP Service",
description="Graph Neural Networks for Materials Property Prediction",
version="1.0.0"
)
@app.get("/")
async def root():
"""Root endpoint with service information."""
return {
"status": "ok",
"service": "MatDeepLearn MCP Service",
"description": "Graph Neural Networks for Materials Property Prediction",
"transport": os.environ.get("MCP_TRANSPORT", "stdio"),
"available_models": [
"CGCNN_demo", "MPNN_demo", "SchNet_demo",
"MEGNet_demo", "GCN_demo", "SOAP_demo", "SM_demo"
]
}
@app.get("/health")
async def health():
"""Health check endpoint."""
try:
import torch
gpu_available = torch.cuda.is_available()
except:
gpu_available = False
return {
"status": "healthy",
"gpu_available": gpu_available
}
@app.get("/info")
async def info():
"""Detailed service information."""
try:
import torch
torch_version = torch.__version__
gpu_available = torch.cuda.is_available()
gpu_count = torch.cuda.device_count() if gpu_available else 0
except:
torch_version = "N/A"
gpu_available = False
gpu_count = 0
return {
"service": "MatDeepLearn MCP Service",
"version": "1.0.0",
"torch_version": torch_version,
"gpu_available": gpu_available,
"gpu_count": gpu_count,
"mcp_tools": [
"check_environment",
"list_available_models",
"get_model_config",
"process_structure_data",
"train_model",
"predict_properties",
"cross_validation",
"analyze_structure",
"compare_models",
"get_dataset_info"
]
}
if __name__ == "__main__":
import uvicorn
port = int(os.environ.get("PORT", "7860"))
uvicorn.run(app, host="0.0.0.0", port=port)
|