app.py

from __future__ import annotations
from fastapi import FastAPI, UploadFile, File, Body
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import pandas as pd
from typing import Dict, Any, List

from data import append_events, read_events, aggregate
from bandit import EmpiricalBayesHierarchicalThompson
from causal import fit_uplift_binary
from utils import dumps

# Gradio を FastAPI にマウント
from dashboard import build_ui
import gradio as gr

app = FastAPI(title="AdCopy MAB Optimizer Pro", version="0.1.0")
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

ui = build_ui()
app = gr.mount_gradio_app(app, ui, path="/")

BANDIT = EmpiricalBayesHierarchicalThompson(min_explore=0.05, margin=0.0, n_draws=20000)

@app.get("/api/health")
def health():
    return {"status": "ok"}

@app.get("/api/events")
def get_events():
    df = read_events()
    return JSONResponse(content=df.to_dict(orient="records"))

@app.post("/api/ingest")
def ingest(rows: List[Dict[str, Any]] = Body(..., embed=True)):
    """
    rows: [
      {"date":"2025-09-01","medium":"FB","creative":"A1","is_control":1,"impressions":1000,"clicks":30,"conversions":5,"cost":1000.0,"features_json":"{\\"len\\":20}"},
      ...
    ]
    """
    df = pd.DataFrame(rows)
    append_events(df)
    return {"ok": True, "n": len(df)}

@app.get("/api/aggregate")
def get_agg():
    agg = aggregate()
    return JSONResponse(content=agg.to_dict(orient="records"))

@app.post("/api/optimize")
def optimize():
    agg = aggregate()
    if agg.empty:
        return {"message": "no data"}
    rec = BANDIT.recommend(agg)
    return JSONResponse(content=rec)

@app.post("/api/uplift")
def uplift():
    agg = aggregate()
    if agg.empty:
        return {"message": "no data"}
    res = fit_uplift_binary(agg)
    return JSONResponse(content=res)