from fastapi import FastAPI, UploadFile, File, HTTPException
import pandas as pd
import os

from backend.core.orchestrator import Orchestrator
from backend.api.schemas import PredictRequest

app = FastAPI(
    title="ModelSmith AI",
    description="Automated ML platform for tabular data",
    version="1.0.0"
)

orchestrator = Orchestrator()

MODEL_PATH = "exports/models/trained_model.pkl"


# -----------------------------
# Analyze Dataset
# -----------------------------
@app.post("/analyze")
async def analyze_dataset(
    file: UploadFile = File(...),
    target_column: str = "target"
):
    try:
        df = pd.read_csv(file.file)

        result = orchestrator.run(df, target_column)

        dataset_info = result.get("dataset_info", {})
        strategy = result.get("strategy", {})

        return {
            "columns": list(df.columns),
            "dataTypes": dataset_info.get("data_types", {}),
            "risks": dataset_info.get("risks", []),
            "problemType": result.get("problem_type"),
            "confidence": strategy.get("confidence", 0),
            "strategy": strategy
        }

    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))


# -----------------------------
# Train Model
# -----------------------------
@app.post("/train")
async def train_model(
    file: UploadFile = File(...),
    target_column: str = "target"
):
    try:
        df = pd.read_csv(file.file)

        result = orchestrator.run(df, target_column, train=True)

        return {
            "strategy": result.get("strategy"),
            "metrics": result.get("metrics"),
            "model_path": MODEL_PATH,
            "model_id": "trained_model"
        }

    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))


# -----------------------------
# Explain Model
# -----------------------------
@app.post("/explain")
async def explain_model(
    file: UploadFile = File(...),
    target_column: str = "target"
):
    try:
        df = pd.read_csv(file.file)

        result = orchestrator.run(df, target_column, train=True)

        return {
            "strategy_explanation": result.get("strategy_explanation"),
            "metrics": result.get("metrics"),
            "feature_importance": result.get("feature_importance")
        }

    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))


# -----------------------------
# Predict
# -----------------------------
@app.post("/predict")
async def predict(request: PredictRequest):
    try:
        # 1. Check model exists
        if not os.path.exists(MODEL_PATH):
            raise HTTPException(
                status_code=400,
                detail="No trained model found. Train a model first."
            )

        # 2. Load model
        model = orchestrator.model_io.load(MODEL_PATH)

        # 3. Convert input to DataFrame
        df = pd.DataFrame(request.instances)

        if df.empty:
            raise HTTPException(
                status_code=400,
                detail="Prediction data is empty."
            )

        # 4. Validate feature columns
        if hasattr(model, "feature_names_in_"):
            expected_features = list(model.feature_names_in_)
            received_features = list(df.columns)

            missing = set(expected_features) - set(received_features)
            extra = set(received_features) - set(expected_features)

            if missing:
                raise HTTPException(
                    status_code=400,
                    detail=f"Missing required features: {sorted(missing)}"
                )

            # Drop extra columns if any
            df = df[expected_features]

        # 5. Predict
        predictions = model.predict(df)

        return {
            "predictions": predictions.tolist()
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=400, detail=str(e))


# -----------------------------
# Root & Health
# -----------------------------
@app.get("/")
def root():
    return {
        "message": "ModelSmith AI API",
        "status": "running"
    }


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