tissue_mcp.py

"""
Model Context Protocol (MCP) for TISSUE

TISSUE (Transcript Imputation with Spatial Single-cell Uncertainty Estimation) provides methods for spatial gene expression prediction and uncertainty quantification in spatial transcriptomics data. It enables uncertainty-aware analysis including multiple imputation, cell filtering, and weighted PCA for improved downstream analysis.

This MCP Server contains the tools extracted from the following tutorials:
1. tissue
    - predict_spatial_gene_expression: Predict spatial gene expression using paired spatial and scRNA-seq data
    - calibrate_uncertainties_and_prediction_intervals: Use TISSUE to calibrate uncertainties and obtain prediction intervals
    - multiple_imputation_hypothesis_testing: Hypothesis testing with TISSUE multiple imputation framework
    - tissue_cell_filtering_for_supervised_learning: TISSUE cell filtering for supervised learning applications
    - tissue_cell_filtering_for_pca: TISSUE cell filtering for PCA, clustering and visualization
    - tissue_weighted_pca: TISSUE-WPCA (weighted principal component analysis)
"""

import sys
from pathlib import Path
from fastmcp import FastMCP

# Import the MCP tools from the tools folder
from tools.tissue_readme import tissue_mcp

from starlette.requests import Request
from starlette.responses import PlainTextResponse, JSONResponse
import os
from fastapi.staticfiles import StaticFiles
import uuid

# Define the MCP server
mcp = FastMCP(name = "TISSUE")

# Mount the tools
mcp.mount(tissue_mcp)

# Use absolute directory for uploads
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
UPLOAD_DIR = os.path.join(BASE_DIR, "/data/upload")
os.makedirs(UPLOAD_DIR, exist_ok=True)

@mcp.custom_route("/health", methods=["GET"])
async def health_check(request: Request) -> PlainTextResponse:
    return PlainTextResponse("OK")


@mcp.custom_route("/", methods=["GET"])
async def index(request: Request) -> PlainTextResponse:
    return PlainTextResponse("MCP is on https://Paper2Agent-tissue-mcp.hf.space/mcp")

# Upload route
@mcp.custom_route("/upload", methods=["POST"])
async def upload(request: Request):
    form = await request.form()
    up = form.get("file")
    if up is None:
        return JSONResponse({"error": "missing form field 'file'"}, status_code=400)

    # Generate a safe filename
    orig = getattr(up, "filename", "") or ""
    ext = os.path.splitext(orig)[1]
    name = f"{uuid.uuid4().hex}{ext}"
    dst = os.path.join(UPLOAD_DIR, name)
    
    # up is a Starlette UploadFile-like object
    with open(dst, "wb") as out:
        out.write(await up.read())

    # Return only the absolute local path
    abs_path = os.path.abspath(dst)
    return JSONResponse({"path": abs_path})

app = mcp.http_app(path="/mcp")
# Saved uploaded input files
app.mount("/files", StaticFiles(directory=UPLOAD_DIR), name="files")
# Saved output files
app.mount("/outputs", StaticFiles(directory="/data/tmp_outputs"), name="outputs")

# Run the MCP server
if __name__ == "__main__":
  mcp.run(transport="http", host="127.0.0.1", port=8003)