feat(api): scaffold FastAPI app + /health + shared Pydantic schemas

src/api/main.py

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+"""NeuroBridge FastAPI entrypoint.
+
+Exposes /health for liveness. Pipeline routes are mounted in Task 8.
+"""
+from __future__ import annotations
+
+from fastapi import FastAPI
+
+from src.api.schemas import HealthResponse
+
+app = FastAPI(
+    title="NeuroBridge Enterprise",
+    description="Three-modality clinical-ML pipeline surface (BBB / EEG / MRI).",
+    version="0.4.0",
+)
+
+
+@app.get("/health", response_model=HealthResponse)
+def health() -> HealthResponse:
+    """Liveness probe — used by docker-compose health checks and Streamlit."""
+    return HealthResponse(status="ok", pipelines=["bbb", "eeg", "mri"])

src/api/schemas.py

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+"""Pydantic request / response models for the NeuroBridge FastAPI surface.
+
+Each pipeline accepts its own request schema (BBBRequest / EEGRequest /
+MRIRequest) but they all return a unified PipelineResponse — the dashboard
+can render a single result card regardless of modality.
+"""
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+
+class BBBRequest(BaseModel):
+    input_path: str = Field(..., description="CSV path with a 'smiles' column")
+    output_path: str = Field(..., description="Parquet output path")
+    smiles_col: str = "smiles"
+    n_bits: int = 2048
+    radius: int = 2
+
+
+class EEGRequest(BaseModel):
+    """Field names mirror eeg_pipeline.run_pipeline kwargs exactly."""
+    input_path: str = Field(..., description="FIF or EDF file")
+    output_path: str = Field(..., description="Parquet output path")
+    epoch_duration_s: float = 2.0
+    eog_ch_name: str | None = None
+    n_components: int = 15
+    random_state: int = 97
+
+
+class MRIRequest(BaseModel):
+    input_dir: str = Field(..., description="Directory of .nii.gz files")
+    sites_csv: str = Field(..., description="CSV mapping subject_id → site")
+    output_path: str = Field(..., description="Parquet output path")
+
+
+class PipelineResponse(BaseModel):
+    """Uniform response for every pipeline route."""
+    status: str
+    output_path: str
+    rows: int
+    columns: int
+    duration_sec: float
+    mlflow_run_id: str | None = None
+
+
+class HealthResponse(BaseModel):
+    status: str
+    pipelines: list[str]

tests/api/test_main.py

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+"""Tests for the FastAPI app surface (health + schema imports)."""
+from __future__ import annotations
+
+from fastapi.testclient import TestClient
+
+from src.api.main import app
+
+
+client = TestClient(app)
+
+
+class TestHealthEndpoint:
+    def test_get_health_returns_200(self):
+        resp = client.get("/health")
+        assert resp.status_code == 200
+
+    def test_get_health_returns_status_ok(self):
+        resp = client.get("/health")
+        assert resp.json()["status"] == "ok"
+
+    def test_get_health_returns_pipeline_list(self):
+        resp = client.get("/health")
+        body = resp.json()
+        assert set(body["pipelines"]) == {"bbb", "eeg", "mri"}