api/main.py

"""
Gapura AI Analysis API
FastAPI server for regression and NLP analysis of irregularity reports
Uses real trained models from ai-model/models/
"""

from fastapi import FastAPI, HTTPException, BackgroundTasks, Request, Body
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field, field_validator
from pydantic_core import ValidationError
from typing import List, Optional, Dict, Any, Tuple
from collections import Counter
import os
import json
import logging
from datetime import datetime
import numpy as np
import pickle
import pandas as pd
import sys

sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from data.cache_service import get_cache, CacheService
from data.nlp_service import NLPModelService
from data.shap_service import get_shap_explainer
from data.anomaly_service import get_anomaly_detector

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

tags_metadata = [
    {
        "name": "Analysis",
        "description": "Core AI analysis endpoints for irregularity reports.",
    },
    {
        "name": "Health",
        "description": "System health and model status checks.",
    },
    {
        "name": "Jobs",
        "description": "Asynchronous job management.",
    },
    {
        "name": "Training",
        "description": "Model retraining and lifecycle management.",
    },
]

app = FastAPI(
    title="Gapura AI Analysis API",
    description="""
Gapura AI Analysis API provides advanced machine learning capabilities for analyzing irregularity reports.

## Features

* **Regression Analysis**: Predict resolution time (days) based on report details.
* **NLP Classification**: Determine severity (Critical, High, Medium, Low) and categorize issues.
* **Entity Extraction**: Extract key entities like Airlines, Flight Numbers, and Dates.
* **Summarization**: Generate executive summaries and key points from long reports.
* **Trend Analysis**: Analyze trends by Airline, Hub, and Category.
* **Anomaly Detection**: Identify unusual patterns in resolution times.

## Models

* **Regression**: Random Forest Regressor (v1.0.0-trained)
* **NLP**: Hybrid Transformer + Rule-based System (v4.0.0-onnx)
""",
    version="2.1.0",
    openapi_tags=tags_metadata,
    docs_url="/docs",
    redoc_url="/redoc",
)

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)
app.add_middleware(GZipMiddleware, minimum_size=500)


@app.exception_handler(ValidationError)
async def validation_exception_handler(request: Request, exc: ValidationError):
    return JSONResponse(
        status_code=422,
        content={
            "detail": "Validation error",
            "errors": exc.errors(),
            "body": exc.json(),
        },
    )


# ============== Pydantic Models ==============

from enum import Enum
from datetime import date as date_type


class ReportCategoryEnum(str, Enum):
    IRREGULARITY = "Irregularity"
    COMPLAINT = "Complaint"


class AreaEnum(str, Enum):
    APRON = "Apron Area"
    TERMINAL = "Terminal Area"
    GENERAL = "General"


class StatusEnum(str, Enum):
    OPEN = "Open"
    CLOSED = "Closed"
    IN_PROGRESS = "In Progress"


class IrregularityReport(BaseModel):
    Date_of_Event: Optional[str] = Field(None, description="Date of the event")
    Airlines: Optional[str] = Field(None, max_length=100)
    Flight_Number: Optional[str] = Field(None, max_length=20)
    Branch: Optional[str] = Field(None, max_length=10)
    HUB: Optional[str] = Field(None, max_length=20)
    Route: Optional[str] = Field(None, max_length=50)
    Report_Category: Optional[str] = Field(None, max_length=50)
    Irregularity_Complain_Category: Optional[str] = Field(None, max_length=100)
    Report: Optional[str] = Field(None, max_length=2000)
    Root_Caused: Optional[str] = Field(None, max_length=2000)
    Action_Taken: Optional[str] = Field(None, max_length=2000)
    Area: Optional[str] = Field(None, max_length=50)
    Status: Optional[str] = Field(None, max_length=50)
    Reported_By: Optional[str] = Field(None, max_length=100)
    Upload_Irregularity_Photo: Optional[str] = Field(None)

    model_config = {"extra": "allow"}


class AnalysisOptions(BaseModel):
    predictResolutionTime: bool = Field(
        default=True, description="Run regression model"
    )
    classifySeverity: bool = Field(
        default=True, description="Classify severity using NLP"
    )
    extractEntities: bool = Field(
        default=True, description="Extract entities using NER"
    )
    generateSummary: bool = Field(default=True, description="Generate text summaries")
    analyzeTrends: bool = Field(default=True, description="Analyze trends")
    bypassCache: bool = Field(
        default=False, description="Bypass cache and fetch fresh data"
    )
    includeRisk: bool = Field(default=False, description="Include risk assessment in analysis")


class AnalysisRequest(BaseModel):
    sheetId: Optional[str] = Field(None, description="Google Sheet ID")
    sheetName: Optional[str] = Field(None, description="Sheet name (NON CARGO or CGO)")
    rowRange: Optional[str] = Field(None, description="Row range (e.g., A2:Z100)")
    data: Optional[List[IrregularityReport]] = Field(
        None, description="Direct data upload"
    )
    options: AnalysisOptions = Field(default_factory=AnalysisOptions)

    @field_validator("data")
    @classmethod
    def validate_data(cls, v):
        if v is not None and len(v) == 0:
            raise ValueError("data array cannot be empty")
        return v


class ShapExplanation(BaseModel):
    baseValue: float = Field(description="Base/expected value from model")
    predictionExplained: bool = Field(
        description="Whether SHAP explanation is available"
    )
    topFactors: List[Dict[str, Any]] = Field(
        default_factory=list, description="Top contributing features"
    )
    explanation: str = Field(default="", description="Human-readable explanation")


class AnomalyResult(BaseModel):
    isAnomaly: bool = Field(description="Whether prediction is anomalous")
    anomalyScore: float = Field(description="Anomaly score (0-1)")
    anomalies: List[Dict[str, Any]] = Field(
        default_factory=list, description="List of detected anomalies"
    )


class RegressionPrediction(BaseModel):
    reportId: str
    predictedDays: float
    confidenceInterval: Tuple[float, float]
    featureImportance: Dict[str, float]
    hasUnknownCategories: bool = Field(
        default=False, description="True if unknown categories were used in prediction"
    )
    shapExplanation: Optional[ShapExplanation] = Field(
        default=None, description="SHAP-based explanation for prediction"
    )
    anomalyDetection: Optional[AnomalyResult] = Field(
        default=None, description="Anomaly detection results"
    )


class RegressionResult(BaseModel):
    predictions: List[RegressionPrediction]
    modelMetrics: Dict[str, Any]


class ClassificationResult(BaseModel):
    reportId: str
    severity: str
    severityConfidence: float
    areaType: str
    issueType: str
    issueTypeConfidence: float


class Entity(BaseModel):
    text: str
    label: str
    start: int
    end: int
    confidence: float


class EntityResult(BaseModel):
    reportId: str
    entities: List[Entity]


class SummaryResult(BaseModel):
    reportId: str
    executiveSummary: str
    keyPoints: List[str]


class SentimentResult(BaseModel):
    reportId: str
    urgencyScore: float
    sentiment: str
    keywords: List[str]


class NLPResult(BaseModel):
    classifications: List[ClassificationResult]
    entities: List[EntityResult]
    summaries: List[SummaryResult]
    sentiment: List[SentimentResult]


class TrendData(BaseModel):
    count: int
    avgResolutionDays: Optional[float]
    topIssues: List[str]


class TrendResult(BaseModel):
    byAirline: Dict[str, TrendData]
    byHub: Dict[str, TrendData]
    byCategory: Dict[str, Dict[str, Any]]
    timeSeries: List[Dict[str, Any]]


class Metadata(BaseModel):
    totalRecords: int
    processingTime: float
    modelVersions: Dict[str, str]


class AnalysisResponse(BaseModel):
    regression: Optional[RegressionResult] = None
    nlp: Optional[NLPResult] = None
    trends: Optional[TrendResult] = None
    risk: Optional[RiskAssessmentResponse] = None
    metadata: Metadata


class RiskItem(BaseModel):
    reportId: str
    severity: str
    severityConfidence: float
    predictedDays: float
    anomalyScore: float
    category: str
    hub: str
    area: str
    riskScore: float
    priority: str
    recommendedActions: List[Dict[str, Any]] = Field(default_factory=list)
    preventiveSuggestions: List[str] = Field(default_factory=list)


class RiskAssessmentResponse(BaseModel):
    items: List[RiskItem]
    topPatterns: List[Dict[str, Any]]
    metadata: Dict[str, Any]


def _severity_to_score(level: str) -> float:
    m = {"Critical": 1.0, "High": 0.8, "Medium": 0.5, "Low": 0.2}
    return m.get(level, 0.3)


def _normalize_days(d: float) -> float:
    return max(0.0, min(1.0, float(d) / 7.0))


def _priority_from_score(s: float) -> str:
    if s >= 0.75:
        return "HIGH"
    if s >= 0.45:
        return "MEDIUM"
    return "LOW"


def _extract_prevention(texts: List[str]) -> List[str]:
    kws = ["review", "prosedur", "procedure", "training", "pelatihan", "prevent", "pencegahan", "maintenance", "inspection", "inspeksi", "briefing", "supervision", "checklist", "verify", "verifikasi"]
    out = []
    seen = set()
    for t in texts:
        lt = t.lower()
        for k in kws:
            if k in lt:
                if t not in seen:
                    seen.add(t)
                    out.append(t)
    return out[:5]


# ============== Real Model Service ==============


class ModelService:
    """Service that loads and uses real trained models"""

    def __init__(self):
        self.regression_version = "1.0.0-trained"
        self.nlp_version = "1.0.0-mock"
        self.regression_model = None
        self.regression_onnx_session = None
        self.label_encoders = {}
        self.scaler = None
        self.feature_names = []
        self.model_metrics = {}
        self.model_loaded = False
        self.nlp_service = None

        self._load_regression_model()
        self._load_nlp_service()

    def _load_nlp_service(self):
        """Load NLP service with trained models or fallback"""
        try:
            from data.nlp_service import get_nlp_service
            self.nlp_service = get_nlp_service()
            self.nlp_version = self.nlp_service.version
            logger.info(f"NLP service loaded (version: {self.nlp_version})")
        except Exception as e:
            logger.warning(f"Failed to load NLP service: {e}")

    def _load_regression_model(self):
        """Load the trained regression model from file"""
        try:
            model_path = os.path.join(
                os.path.dirname(__file__),
                "..",
                "models",
                "regression",
                "resolution_predictor_latest.pkl",
            )

            if not os.path.exists(model_path):
                logger.warning(f"Model file not found at {model_path}")
                return

            logger.info(f"Loading regression model from {model_path}")

            with open(model_path, "rb") as f:
                model_data = pickle.load(f)

            self.regression_model = model_data.get("model")
            self.label_encoders = model_data.get("label_encoders", {})
            self.scaler = model_data.get("scaler")
            self.feature_names = model_data.get("feature_names", [])
            self.model_metrics = model_data.get("metrics", {})
            self.model_loaded = True

            logger.info(f"✓ Regression model loaded successfully")
            logger.info(f"  - Features: {len(self.feature_names)}")
            logger.info(f"  - Metrics: MAE={self.model_metrics.get('test_mae', 'N/A')}")

            # Try to load ONNX model for faster inference
            onnx_path = os.path.join(
                os.path.dirname(__file__),
                "..",
                "models",
                "regression",
                "resolution_predictor.onnx",
            )
            if os.path.exists(onnx_path):
                try:
                    import onnxruntime as ort
                    sess_options = ort.SessionOptions()
                    sess_options.intra_op_num_threads = 1
                    sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
                    
                    self.regression_onnx_session = ort.InferenceSession(onnx_path, sess_options)
                    logger.info("✓ Regression ONNX model loaded successfully")
                except Exception as e:
                    logger.warning(f"Failed to load Regression ONNX model: {e}")

        except Exception as e:
            logger.error(f"Failed to load regression model: {e}")
            self.model_loaded = False

    def _extract_features(self, report: Dict) -> Optional[np.ndarray]:
        """Extract features from a single report matching training preprocessing"""
        try:
            # Parse date
            date_str = report.get("Date_of_Event", "")
            try:
                date_obj = pd.to_datetime(date_str, errors="coerce")
                if pd.isna(date_obj):
                    date_obj = datetime.now()
                day_of_week = date_obj.dayofweek
                month = date_obj.month
                is_weekend = day_of_week in [5, 6]
                week_of_year = date_obj.isocalendar().week
                day_of_year = date_obj.dayofyear
            except:
                day_of_week = 0
                month = 1
                is_weekend = False
                week_of_year = 1
                day_of_year = 1

            sin_day_of_week = np.sin(2 * np.pi * day_of_week / 7)
            cos_day_of_week = np.cos(2 * np.pi * day_of_week / 7)
            sin_month = np.sin(2 * np.pi * month / 12)
            cos_month = np.cos(2 * np.pi * month / 12)
            sin_day_of_year = np.sin(2 * np.pi * day_of_year / 365)
            cos_day_of_year = np.cos(2 * np.pi * day_of_year / 365)

            # Text features
            report_text = report.get("Report", "")
            root_cause = report.get("Root_Caused", "")
            action_taken = report.get("Action_Taken", "")

            # Categorical
            airline = report.get("Airlines", "Unknown")
            hub = report.get("HUB", "Unknown")
            branch = report.get("Branch", "Unknown")
            category = report.get("Irregularity_Complain_Category", "Unknown")
            area = report.get("Area", "Unknown")

            # Binary features
            has_photos = bool(report.get("Upload_Irregularity_Photo", ""))
            is_complaint = report.get("Report_Category", "") == "Complaint"

            # Encode categorical features
            categorical_values = {
                "airline": airline,
                "hub": hub,
                "branch": branch,
                "category": category,
                "area": area,
            }

            encoded_values = {}
            unknown_flags = {}
            for col, value in categorical_values.items():
                if col in self.label_encoders:
                    le = self.label_encoders[col]
                    value_str = str(value)
                    if value_str in le.classes_:
                        encoded_values[f"{col}_encoded"] = le.transform([value_str])[0]
                        unknown_flags[col] = False
                    else:
                        unknown_idx = (
                            le.transform(["Unknown"])[0]
                            if "Unknown" in le.classes_
                            else 0
                        )
                        encoded_values[f"{col}_encoded"] = unknown_idx
                        unknown_flags[col] = True
                        logger.warning(
                            f"Unknown {col} value: '{value_str}' - using Unknown category"
                        )
                else:
                    encoded_values[f"{col}_encoded"] = 0
                    unknown_flags[col] = True

            # Build feature vector in correct order
            feature_dict = {
                "day_of_week": day_of_week,
                "month": month,
                "is_weekend": int(is_weekend),
                "week_of_year": week_of_year,
                "sin_day_of_week": sin_day_of_week,
                "cos_day_of_week": cos_day_of_week,
                "sin_month": sin_month,
                "cos_month": cos_month,
                "sin_day_of_year": sin_day_of_year,
                "cos_day_of_year": cos_day_of_year,
                "report_length": len(report_text),
                "report_word_count": len(report_text.split()) if report_text else 0,
                "root_cause_length": len(root_cause),
                "action_taken_length": len(action_taken),
                "has_photos": int(has_photos),
                "is_complaint": int(is_complaint),
                "text_complexity": (len(report_text) * len(report_text.split()) / 100)
                if report_text
                else 0,
                "has_root_cause": int(bool(root_cause)),
                "has_action_taken": int(bool(action_taken)),
            }
            feature_dict.update(encoded_values)

            has_unknown_categories = any(unknown_flags.values())

            # Create feature array in correct order
            features = []
            for feature_name in self.feature_names:
                features.append(feature_dict.get(feature_name, 0))

            X = np.array([features])

            # Scale features
            if self.scaler:
                X = self.scaler.transform(X)

            return X, has_unknown_categories

        except Exception as e:
            logger.error(f"Feature extraction error: {e}")
            return None, True

    def _extract_features_batch(self, df: pd.DataFrame) -> Tuple[Optional[np.ndarray], np.ndarray]:
        """Extract features from a dataframe matching training preprocessing (Batch optimized)"""
        try:
            # Ensure required columns exist
            required_cols = [
                "Date_of_Event", "Report", "Root_Caused", "Action_Taken", 
                "Upload_Irregularity_Photo", "Report_Category", 
                "Airlines", "HUB", "Branch", "Irregularity_Complain_Category", "Area"
            ]
            for col in required_cols:
                if col not in df.columns:
                    df[col] = None

            # Copy to avoid modifying original
            df = df.copy()
            
            # Parse date
            df["Date_of_Event"] = pd.to_datetime(df["Date_of_Event"], errors="coerce")
            now = datetime.now()
            df["Date_of_Event"] = df["Date_of_Event"].fillna(now)
            
            df["day_of_week"] = df["Date_of_Event"].dt.dayofweek
            df["month"] = df["Date_of_Event"].dt.month
            df["is_weekend"] = df["day_of_week"].isin([5, 6]).astype(int)
            df["week_of_year"] = df["Date_of_Event"].dt.isocalendar().week.astype(int)
            df["day_of_year"] = df["Date_of_Event"].dt.dayofyear
            
            # Sin/Cos transforms
            df["sin_day_of_week"] = np.sin(2 * np.pi * df["day_of_week"] / 7)
            df["cos_day_of_week"] = np.cos(2 * np.pi * df["day_of_week"] / 7)
            df["sin_month"] = np.sin(2 * np.pi * df["month"] / 12)
            df["cos_month"] = np.cos(2 * np.pi * df["month"] / 12)
            df["sin_day_of_year"] = np.sin(2 * np.pi * df["day_of_year"] / 365)
            df["cos_day_of_year"] = np.cos(2 * np.pi * df["day_of_year"] / 365)
            
            # Text features
            df["Report"] = df["Report"].fillna("").astype(str)
            df["Root_Caused"] = df["Root_Caused"].fillna("").astype(str)
            df["Action_Taken"] = df["Action_Taken"].fillna("").astype(str)
            
            df["report_length"] = df["Report"].str.len()
            df["report_word_count"] = df["Report"].apply(lambda x: len(x.split()) if x else 0)
            df["root_cause_length"] = df["Root_Caused"].str.len()
            df["action_taken_length"] = df["Action_Taken"].str.len()
            
            df["has_photos"] = df["Upload_Irregularity_Photo"].fillna("").astype(bool).astype(int)
            df["is_complaint"] = (df["Report_Category"] == "Complaint").astype(int)
            
            df["text_complexity"] = np.where(
                df["Report"].str.len() > 0,
                (df["report_length"] * df["report_word_count"] / 100),
                0
            )
            df["has_root_cause"] = (df["Root_Caused"].str.len() > 0).astype(int)
            df["has_action_taken"] = (df["Action_Taken"].str.len() > 0).astype(int)
            
            # Categorical encoding
            categorical_cols = {
                "airline": "Airlines",
                "hub": "HUB",
                "branch": "Branch",
                "category": "Irregularity_Complain_Category",
                "area": "Area"
            }
            
            unknown_flags = np.zeros(len(df), dtype=bool)
            
            for feature_name, col_name in categorical_cols.items():
                df[col_name] = df[col_name].fillna("Unknown").astype(str)
                    
                if feature_name in self.label_encoders:
                    le = self.label_encoders[feature_name]
                    # Create mapping for fast lookup
                    mapping = {label: idx for idx, label in enumerate(le.classes_)}
                    unknown_idx = mapping.get("Unknown", 0)
                    if "Unknown" in le.classes_:
                         unknown_idx = mapping["Unknown"]
                    
                    # Map values
                    encoded_col = df[col_name].map(mapping)
                    
                    # Track unknowns (NaN after map means unknown)
                    is_unknown = encoded_col.isna()
                    unknown_flags |= is_unknown.values
                    
                    # Fill unknowns
                    df[f"{feature_name}_encoded"] = encoded_col.fillna(unknown_idx).astype(int)
                else:
                    df[f"{feature_name}_encoded"] = 0
                    unknown_flags[:] = True
            
            # Select features in order
            for f in self.feature_names:
                if f not in df.columns:
                    df[f] = 0
            
            X = df[self.feature_names].values
            
            # Scale
            if self.scaler:
                X = self.scaler.transform(X)
                
            return X, unknown_flags
            
        except Exception as e:
            logger.error(f"Batch feature extraction error: {e}")
            return None, np.ones(len(df), dtype=bool)

    def predict_regression(self, data: List[Dict]) -> List[RegressionPrediction]:
        """Predict resolution time using trained model"""
        predictions = []
        shap_explainer = get_shap_explainer()
        anomaly_detector = get_anomaly_detector()

        # Batch processing
        try:
            df = pd.DataFrame(data)
            X_batch, unknown_flags_batch = self._extract_features_batch(df)
            
            if X_batch is not None:
                if self.regression_onnx_session:
                    # Use ONNX model
                    input_name = self.regression_onnx_session.get_inputs()[0].name
                    predicted_batch = self.regression_onnx_session.run(None, {input_name: X_batch.astype(np.float32)})[0]
                    predicted_batch = predicted_batch.ravel() # Flatten to 1D array
                elif self.regression_model is not None:
                    # Use Pickle model
                    predicted_batch = self.regression_model.predict(X_batch)
                else:
                    predicted_batch = None
                    unknown_flags_batch = [True] * len(data)
            else:
                predicted_batch = None
                unknown_flags_batch = [True] * len(data)
        except Exception as e:
            logger.error(f"Batch prediction setup failed: {e}")
            predicted_batch = None
            unknown_flags_batch = [True] * len(data)

        for i, item in enumerate(data):
            # Use batch results
            has_unknown = unknown_flags_batch[i]
            features = X_batch[i:i+1] if X_batch is not None else None
            
            category = item.get("Irregularity_Complain_Category", "Unknown")
            hub = item.get("HUB", "Unknown")

            if predicted_batch is not None:
                predicted = predicted_batch[i]
                mae = self.model_metrics.get("test_mae", 0.5)
                lower = max(0.1, predicted - mae)
                upper = predicted + mae

                shap_exp = None
                if shap_explainer.explainer is not None and features is not None:
                    try:
                        shap_result = shap_explainer.explain_prediction(features)
                        shap_exp = ShapExplanation(
                            baseValue=shap_result.get("base_value", 0),
                            predictionExplained=shap_result.get(
                                "prediction_explained", False
                            ),
                            topFactors=shap_result.get("top_factors", [])[:5],
                            explanation=shap_result.get("explanation", ""),
                        )
                    except Exception as e:
                        logger.debug(f"SHAP explanation failed: {e}")

                anomaly_result = None
                try:
                    anomaly_data = anomaly_detector.detect_prediction_anomaly(
                        predicted, category, hub
                    )
                    anomaly_result = AnomalyResult(
                        isAnomaly=anomaly_data.get("is_anomaly", False),
                        anomalyScore=anomaly_data.get("anomaly_score", 0),
                        anomalies=anomaly_data.get("anomalies", []),
                    )
                except Exception as e:
                    logger.debug(f"Anomaly detection failed: {e}")
            else:
                base_days = {
                    "Cargo Problems": 2.5,
                    "Pax Handling": 1.8,
                    "GSE": 3.2,
                    "Operation": 2.1,
                    "Baggage Handling": 1.5,
                }.get(category, 2.0)
                predicted = base_days + np.random.normal(0, 0.3)
                lower = max(0.1, predicted - 0.5)
                upper = predicted + 0.5
                has_unknown = True
                shap_exp = None
                anomaly_result = None

            if self.model_metrics and "feature_importance" in self.model_metrics:
                importance = self.model_metrics["feature_importance"]
            else:
                importance = {
                    "category": 0.35,
                    "airline": 0.28,
                    "hub": 0.15,
                    "reportLength": 0.12,
                    "hasPhotos": 0.10,
                }

            predictions.append(
                RegressionPrediction(
                    reportId=f"row_{i}",
                    predictedDays=round(max(0.1, predicted), 2),
                    confidenceInterval=(round(lower, 2), round(upper, 2)),
                    featureImportance=importance,
                    hasUnknownCategories=has_unknown,
                    shapExplanation=shap_exp,
                    anomalyDetection=anomaly_result,
                )
            )

        return predictions

    def classify_text(self, data: List[Dict]) -> List[ClassificationResult]:
        """Classify text using trained NLP models or rule-based fallback"""
        results = []

        texts = [
            (item.get("Report") or "") + " " + (item.get("Root_Caused") or "")
            for item in data
        ]

        # Get multi-task predictions if available
        mt_results = None
        if self.nlp_service:
            mt_results = self.nlp_service.predict_multi_task(texts)
            severity_results = self.nlp_service.classify_severity(texts)
        else:
            severity_results = self._classify_severity_fallback(texts)

        for i, (item, sev_result) in enumerate(zip(data, severity_results)):
            severity = sev_result.get("severity", "Low")
            severity_conf = sev_result.get("confidence", 0.8)

            # Use multi-task predictions for area and issue type if available
            if mt_results and i < len(mt_results):
                mt_res = mt_results[i]
                area = mt_res.get("area", {}).get("label", item.get("Area", "Unknown")).replace(" Area", "")
                area_conf = mt_res.get("area", {}).get("confidence", 0.85)
                issue = mt_res.get("irregularity", {}).get("label", item.get("Irregularity_Complain_Category", "Unknown"))
                issue_conf = mt_res.get("irregularity", {}).get("confidence", 0.85)
            else:
                area = item.get("Area", "Unknown").replace(" Area", "")
                area_conf = 0.85
                issue = item.get("Irregularity_Complain_Category", "Unknown")
                issue_conf = 0.85

            results.append(
                ClassificationResult(
                    reportId=f"row_{i}",
                    severity=severity,
                    severityConfidence=severity_conf,
                    areaType=area,
                    issueType=issue,
                    issueTypeConfidence=issue_conf,
                )
            )
        return results

    def _classify_severity_fallback(self, texts: List[str]) -> List[Dict]:
        """Fallback severity classification"""
        results = []
        for text in texts:
            report = text.lower()

            if any(
                kw in report
                for kw in ["damage", "torn", "broken", "critical", "emergency"]
            ):
                severity = "High"
                severity_conf = 0.89
            elif any(kw in report for kw in ["delay", "late", "wrong", "error"]):
                severity = "Medium"
                severity_conf = 0.75
            else:
                severity = "Low"
                severity_conf = 0.82

            results.append({"severity": severity, "confidence": severity_conf})
        return results

    def extract_entities(self, data: List[Dict]) -> List[EntityResult]:
        """Extract entities from reports"""
        results = []
        for i, item in enumerate(data):
            entities = []
            report_text = item.get("Report", "") + " " + item.get("Root_Caused", "")

            # Extract airline
            airline = item.get("Airlines", "")
            if airline and airline != "Unknown":
                # Find position in text
                idx = report_text.lower().find(airline.lower())
                start = max(0, idx) if idx >= 0 else 0
                entities.append(
                    Entity(
                        text=airline,
                        label="AIRLINE",
                        start=start,
                        end=start + len(airline),
                        confidence=0.95,
                    )
                )

            # Extract flight number
            flight = item.get("Flight_Number", "")
            if flight and flight != "#N/A":
                entities.append(
                    Entity(
                        text=flight,
                        label="FLIGHT_NUMBER",
                        start=0,
                        end=len(flight),
                        confidence=0.92,
                    )
                )

            # Extract dates
            date_str = item.get("Date_of_Event", "")
            if date_str:
                entities.append(
                    Entity(
                        text=date_str,
                        label="DATE",
                        start=0,
                        end=len(date_str),
                        confidence=0.90,
                    )
                )

            results.append(EntityResult(reportId=f"row_{i}", entities=entities))
        return results

    def generate_summary(self, data: List[Dict]) -> List[SummaryResult]:
        """Generate summaries using NLP service or fallback"""
        results = []
        for i, item in enumerate(data):
            combined_text = (
                item.get("Report", "")
                + " "
                + item.get("Root_Caused", "")
                + " "
                + item.get("Action_Taken", "")
            )

            if self.nlp_service and len(combined_text) > 100:
                summary_result = self.nlp_service.summarize(combined_text)
                executive_summary = summary_result.get("executiveSummary", "")
                key_points = summary_result.get("keyPoints", [])
            else:
                category = item.get("Irregularity_Complain_Category", "Issue")
                report = item.get("Report", "")[:120]
                root_cause = item.get("Root_Caused", "")[:80]
                action = item.get("Action_Taken", "")[:80]

                executive_summary = f"{category}: {report}"
                if root_cause:
                    executive_summary += f" Root cause: {root_cause}."

                key_points = [
                    f"Category: {category}",
                    f"Status: {item.get('Status', 'Unknown')}",
                    f"Area: {item.get('Area', 'Unknown')}",
                ]

                if action:
                    key_points.append(f"Action: {action[:50]}...")

            results.append(
                SummaryResult(
                    reportId=f"row_{i}",
                    executiveSummary=executive_summary,
                    keyPoints=key_points,
                )
            )
        return results

    def analyze_sentiment(self, data: List[Dict]) -> List[SentimentResult]:
        """Analyze sentiment/urgency using NLP service or fallback"""
        results = []

        texts = [
            item.get("Report", "") + " " + item.get("Root_Caused", "") for item in data
        ]

        if self.nlp_service:
            urgency_results = self.nlp_service.analyze_urgency(texts)
        else:
            urgency_results = self._analyze_urgency_fallback(texts)

        for i, (item, urg_result) in enumerate(zip(data, urgency_results)):
            results.append(
                SentimentResult(
                    reportId=f"row_{i}",
                    urgencyScore=urg_result.get("urgency_score", 0.0),
                    sentiment=urg_result.get("sentiment", "Neutral"),
                    keywords=urg_result.get("keywords", []),
                )
            )
        return results

    def _analyze_urgency_fallback(self, texts: List[str]) -> List[Dict]:
        """Fallback urgency analysis"""
        urgency_keywords = [
            "damage",
            "broken",
            "emergency",
            "critical",
            "urgent",
            "torn",
            "severe",
        ]

        results = []
        for text in texts:
            report = text.lower()
            keyword_matches = [kw for kw in urgency_keywords if kw in report]
            urgency_count = len(keyword_matches)
            urgency_score = min(1.0, urgency_count / 3.0)

            results.append(
                {
                    "urgency_score": round(urgency_score, 2),
                    "sentiment": "Negative" if urgency_score > 0.3 else "Neutral",
                    "keywords": keyword_matches,
                }
            )
        return results


# Initialize model service
model_service = ModelService()

# ============== API Endpoints ==============


@app.get(
    "/",
    tags=["Health"],
    summary="API Root & Status",
)
async def root():
    """Returns basic API status, version, and model availability."""
    return {
        "status": "healthy",
        "service": "Gapura AI Analysis API",
        "version": "1.0.0",
        "models": {
            "regression": "loaded" if model_service.model_loaded else "unavailable",
            "nlp": model_service.nlp_service.version if model_service.nlp_service and model_service.nlp_service.models_loaded else "unavailable",
        },
        "timestamp": datetime.now().isoformat(),
    }


@app.get(
    "/health",
    tags=["Health"],
    summary="Detailed Health Check",
)
async def health_check():
    """
    Returns detailed health status including:
    - **Models**: Version and load status of Regression and NLP models.
    - **Cache**: Redis/Local cache connectivity.
    - **Metrics**: Current model performance metrics (MAE, RMSE, R2).
    """
    cache = get_cache()
    cache_health = cache.health_check()

    return {
        "status": "healthy",
        "models": {
            "regression": {
                "version": model_service.regression_version,
                "loaded": model_service.model_loaded,
                "metrics": model_service.model_metrics
                if model_service.model_loaded
                else None,
            },
            "nlp": {
                "version": model_service.nlp_version,
                "status": "rule_based",
            },
        },
        "cache": cache_health,
        "timestamp": datetime.now().isoformat(),
    }

@app.post("/api/ai/risk/assess", response_model=RiskAssessmentResponse, tags=["Analysis"])
async def assess_risk(
    request: Optional[AnalysisRequest] = Body(None),
    sheetId: Optional[str] = None,
    sheetName: Optional[str] = None,
    rowRange: Optional[str] = None,
    bypass_cache: bool = False,
    top_k_patterns: int = 5,
):
    from data.sheets_service import GoogleSheetsService
    from data.action_service import get_action_service

    items_data: List[Dict[str, Any]] = []
    if request and request.data:
        items_data = [r.model_dump(exclude_none=True) for r in request.data]
    elif sheetId and sheetName and rowRange:
        cache = get_cache() if not bypass_cache else None
        sheets_service = GoogleSheetsService(cache=cache)
        items_data = sheets_service.fetch_sheet_data(sheetId, sheetName, rowRange, bypass_cache=bypass_cache)
    else:
        raise HTTPException(status_code=400, detail="sheetId, sheetName, and rowRange are required, or provide data in body")

    if len(items_data) == 0:
        return RiskAssessmentResponse(items=[], topPatterns=[], metadata={"count": 0})

    preds = model_service.predict_regression(items_data)
    classes = model_service.classify_text(items_data)
    try:
        action_service = get_action_service()
        eff = action_service.action_effectiveness or {}
    except Exception:
        eff = {}

    items: List[RiskItem] = []
    for i, item in enumerate(items_data):
        cat = item.get("Irregularity_Complain_Category", "Unknown") or "Unknown"
        hub = item.get("HUB", "Unknown") or "Unknown"
        area = (item.get("Area", "Unknown") or "Unknown").replace(" Area", "")
        pr = preds[i]
        cl = classes[i]
        sev = cl.severity
        sev_conf = cl.severityConfidence
        pdays = pr.predictedDays
        anom = 0.0
        if pr.anomalyDetection:
            anom = pr.anomalyDetection.anomalyScore
        sev_s = _severity_to_score(sev)
        d_s = _normalize_days(pdays)
        cat_w = 1.0 - float(eff.get(cat, 0.8))
        risk = min(1.0, 0.5 * sev_s + 0.25 * d_s + 0.15 * anom + 0.10 * cat_w)
        recs: List[Dict[str, Any]] = []
        try:
            recs_resp = action_service.recommend(
                report=item.get("Report", "") or "",
                issue_type=cat,
                severity=sev,
                area=area if area else None,
                airline=item.get("Airlines") or None,
                top_n=5,
            )
            recs = recs_resp.get("recommendations", [])
        except Exception:
            recs = []
        prev = _extract_prevention([r.get("action", "") for r in recs])
        items.append(
            RiskItem(
                reportId=f"row_{i}",
                severity=sev,
                severityConfidence=sev_conf,
                predictedDays=pdays,
                anomalyScore=anom,
                category=cat,
                hub=hub,
                area=area,
                riskScore=round(risk, 3),
                priority=_priority_from_score(risk),
                recommendedActions=recs[:5],
                preventiveSuggestions=prev,
            )
        )

    groups: Dict[str, Dict[str, Any]] = {}
    for it, raw in zip(items, items_data):
        key = f"{it.category}|{it.hub}|{it.area}"
        g = groups.get(key) or {"key": key, "category": it.category, "hub": it.hub, "area": it.area, "count": 0, "avgRisk": 0.0, "avgDays": 0.0, "highSeverityShare": 0.0}
        g["count"] += 1
        g["avgRisk"] += it.riskScore
        g["avgDays"] += it.predictedDays
        g["highSeverityShare"] += 1.0 if it.severity in ("Critical", "High") else 0.0
        groups[key] = g
    patterns = []
    for g in groups.values():
        c = g["count"]
        g["avgRisk"] = round(g["avgRisk"] / max(1, c), 3)
        g["avgDays"] = round(g["avgDays"] / max(1, c), 2)
        g["highSeverityShare"] = round(g["highSeverityShare"] / max(1, c), 3)
        patterns.append(g)
    patterns.sort(key=lambda x: (-x["avgRisk"], -x["highSeverityShare"], -x["avgDays"], -x["count"]))
    return RiskAssessmentResponse(
        items=sorted(items, key=lambda x: -x.riskScore),
        topPatterns=patterns[:top_k_patterns],
        metadata={"count": len(items)},
    )

from data.job_service import JobService, JobStatus

# Initialize job service
job_service = JobService()


def perform_analysis(data: List[Dict], options: AnalysisOptions, compact: bool) -> AnalysisResponse:
    """Core analysis logic reused by sync and async endpoints"""
    start_time = datetime.now()
    total_records = len(data)

    logger.info(f"Analyzing {total_records} records...")

    # Initialize response
    response = AnalysisResponse(
        metadata=Metadata(
            totalRecords=total_records,
            processingTime=0.0,
            modelVersions={
                "regression": model_service.regression_version,
                "nlp": model_service.nlp_version,
            },
        )
    )

    # Regression Analysis
    predictions: List[RegressionPrediction] = []
    if options.predictResolutionTime or options.includeRisk:
        logger.info(f"Running regression analysis...")
        predictions = model_service.predict_regression(data)

        # Use real metrics if available
        if model_service.model_loaded and model_service.model_metrics:
            metrics = {
                "mae": round(model_service.model_metrics.get("test_mae", 1.2), 3),
                "rmse": round(model_service.model_metrics.get("test_rmse", 1.8), 3),
                "r2": round(model_service.model_metrics.get("test_r2", 0.78), 3),
                "model_loaded": True,
                "note": "Using trained model"
                if model_service.model_loaded
                else "Using fallback",
            }
        else:
            metrics = {
                "mae": None,
                "rmse": None,
                "r2": None,
                "model_loaded": False,
                "note": "Model not available - using fallback predictions",
            }

        if options.predictResolutionTime:
            response.regression = RegressionResult(
                predictions=predictions,
                modelMetrics=metrics,
            )

    # NLP Analysis
    classifications: List[ClassificationResult] = []
    if any(
        [
            options.classifySeverity,
            options.extractEntities,
            options.generateSummary,
            options.includeRisk,
        ]
    ):
        logger.info(f"Running NLP analysis...")

        entities = []
        summaries = []
        sentiment = []

        if options.classifySeverity or options.includeRisk:
            classifications = model_service.classify_text(data)

        if options.extractEntities:
            entities = model_service.extract_entities(data)

        if options.generateSummary:
            summaries = model_service.generate_summary(data)

        sentiment = model_service.analyze_sentiment(data)

        response.nlp = NLPResult(
            classifications=classifications,
            entities=entities,
            summaries=summaries,
            sentiment=sentiment,
        )

    # Trend Analysis
    if options.analyzeTrends:
        logger.info(f"Running trend analysis...")

        by_airline = {}
        by_hub = {}
        by_category = {}

        for item in data:
            airline = item.get("Airlines", "Unknown")
            hub = item.get("HUB", "Unknown")
            category = item.get("Irregularity_Complain_Category", "Unknown")

            # Airline aggregation
            if airline not in by_airline:
                by_airline[airline] = {"count": 0, "issues": []}
            by_airline[airline]["count"] += 1
            by_airline[airline]["issues"].append(category)

            # Hub aggregation
            if hub not in by_hub:
                by_hub[hub] = {"count": 0, "issues": []}
            by_hub[hub]["count"] += 1
            by_hub[hub]["issues"].append(category)

            # Category aggregation
            if category not in by_category:
                by_category[category] = {"count": 0}
            by_category[category]["count"] += 1

        # Convert to TrendData format
        by_airline_trend = {
            k: TrendData(
                count=v["count"],
                avgResolutionDays=2.0 + np.random.random(),
                topIssues=list(set(v["issues"]))[:3],
            )
            for k, v in by_airline.items()
        }

        by_hub_trend = {
            k: TrendData(
                count=v["count"],
                avgResolutionDays=2.0 + np.random.random(),
                topIssues=list(set(v["issues"]))[:3],
            )
            for k, v in by_hub.items()
        }

        by_category_trend = {
            k: {"count": v["count"], "trend": "stable"}
            for k, v in by_category.items()
        }

        response.trends = TrendResult(
            byAirline=by_airline_trend,
            byHub=by_hub_trend,
            byCategory=by_category_trend,
            timeSeries=[],
        )

    # Risk Assessment
    if options.includeRisk:
        try:
            from data.action_service import get_action_service
            action_service = get_action_service()
            eff = action_service.action_effectiveness or {}
        except Exception:
            eff = {}
            action_service = None

        items: List[RiskItem] = []
        for i, item in enumerate(data):
            cat = item.get("Irregularity_Complain_Category", "Unknown") or "Unknown"
            hub = item.get("HUB", "Unknown") or "Unknown"
            area = (item.get("Area", "Unknown") or "Unknown").replace(" Area", "")
            pr = predictions[i] if i < len(predictions) else None
            cl = classifications[i] if i < len(classifications) else None
            sev = cl.severity if cl else "Low"
            sev_conf = cl.severityConfidence if cl else 0.6
            pdays = pr.predictedDays if pr else 0.0
            anom = pr.anomalyDetection.anomalyScore if pr and pr.anomalyDetection else 0.0
            sev_s = _severity_to_score(sev)
            d_s = _normalize_days(pdays)
            cat_w = 1.0 - float(eff.get(cat, 0.8))
            risk = min(1.0, 0.5 * sev_s + 0.25 * d_s + 0.15 * anom + 0.10 * cat_w)
            recs: List[Dict[str, Any]] = []
            if action_service:
                try:
                    recs_resp = action_service.recommend(
                        report=item.get("Report", "") or "",
                        issue_type=cat,
                        severity=sev,
                        area=area if area else None,
                        airline=item.get("Airlines") or None,
                        top_n=5,
                    )
                    recs = recs_resp.get("recommendations", [])
                except Exception:
                    recs = []
            prev = _extract_prevention([r.get("action", "") for r in recs])
            items.append(
                RiskItem(
                    reportId=f"row_{i}",
                    severity=sev,
                    severityConfidence=sev_conf,
                    predictedDays=pdays,
                    anomalyScore=anom,
                    category=cat,
                    hub=hub,
                    area=area,
                    riskScore=round(risk, 3),
                    priority=_priority_from_score(risk),
                    recommendedActions=recs[:5],
                    preventiveSuggestions=prev,
                )
            )

        groups: Dict[str, Dict[str, Any]] = {}
        for it, raw in zip(items, data):
            key = f"{it.category}|{it.hub}|{it.area}"
            g = groups.get(key) or {"key": key, "category": it.category, "hub": it.hub, "area": it.area, "count": 0, "avgRisk": 0.0, "avgDays": 0.0, "highSeverityShare": 0.0}
            g["count"] += 1
            g["avgRisk"] += it.riskScore
            g["avgDays"] += it.predictedDays
            g["highSeverityShare"] += 1.0 if it.severity in ("Critical", "High") else 0.0
            groups[key] = g
        patterns = []
        for g in groups.values():
            c = g["count"]
            g["avgRisk"] = round(g["avgRisk"] / max(1, c), 3)
            g["avgDays"] = round(g["avgDays"] / max(1, c), 2)
            g["highSeverityShare"] = round(g["highSeverityShare"] / max(1, c), 3)
            patterns.append(g)
        patterns.sort(key=lambda x: (-x["avgRisk"], -x["highSeverityShare"], -x["avgDays"], -x["count"]))
        response.risk = RiskAssessmentResponse(
            items=sorted(items, key=lambda x: -x.riskScore),
            topPatterns=patterns[:5],
            metadata={"count": len(items)},
        )

    if compact:
        if response.regression and response.regression.predictions:
            for p in response.regression.predictions:
                p.shapExplanation = None
                p.anomalyDetection = None
        if response.nlp:
            response.nlp.entities = []
            response.nlp.summaries = []

    # Calculate processing time
    processing_time = (datetime.now() - start_time).total_seconds() * 1000
    response.metadata.processingTime = round(processing_time, 2)

    logger.info(f"Analysis completed in {processing_time:.2f}ms")
    return response


@app.post(
    "/api/ai/analyze",
    response_model=AnalysisResponse,
    tags=["Analysis"],
    summary="Analyze Irregularity Reports",
    response_description="Analysis results including predictions, severity, and entities.",
)
async def analyze_reports(request: AnalysisRequest, compact: bool = False):
    """
    Perform comprehensive AI analysis on a batch of irregularity reports.

    - **Regression**: Predicts days to resolve based on category and description.
    - **NLP**: Classifies severity, extracts entities (Flight No, Airline), and summarizes text.
    - **Trends**: Aggregates data by Airline, Hub, and Category.

    The endpoint accepts a list of `IrregularityReport` objects.
    """
    try:
        # Use direct data
        if not request.data:
            raise HTTPException(
                status_code=400,
                detail="No data provided. Either sheetId or data must be specified.",
            )

        # Convert IrregularityReport objects to dicts
        data = [report.model_dump(exclude_none=True) for report in request.data]
        
        return perform_analysis(data, request.options, compact)

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Analysis error: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))


def background_analysis_task(job_id: str, data: List[Dict], options: AnalysisOptions, compact: bool):
    """Background task for analysis"""
    try:
        job_service.update_job(job_id, JobStatus.PROCESSING)
        response = perform_analysis(data, options, compact)
        job_service.update_job(job_id, JobStatus.COMPLETED, result=response.model_dump())
    except Exception as e:
        logger.error(f"Job {job_id} failed: {e}")
        job_service.update_job(job_id, JobStatus.FAILED, error=str(e))


@app.post(
    "/api/ai/analyze-async",
    response_model=Dict[str, str],
    tags=["Analysis", "Jobs"],
    summary="Start Async Analysis Job",
)
async def analyze_async(
    request: AnalysisRequest, background_tasks: BackgroundTasks, compact: bool = False
):
    """
    Start a background analysis job for large datasets.
    Returns a `jobId` immediately, which can be used to poll status via `/api/ai/jobs/{jobId}`.
    """
    if not request.data:
        raise HTTPException(status_code=400, detail="No data provided")
        
    data = [report.model_dump(exclude_none=True) for report in request.data]
    job_id = job_service.create_job()
    
    background_tasks.add_task(background_analysis_task, job_id, data, request.options, compact)
    
    return {"job_id": job_id, "status": "queued"}


@app.get(
    "/api/ai/jobs/{job_id}",
    tags=["Jobs"],
    summary="Get Job Status",
)
async def get_job_status(job_id: str):
    """
    Retrieve the status and results of a background analysis job.
    Possible statuses: `queued`, `processing`, `completed`, `failed`.
    """
    job = job_service.get_job(job_id)
    if not job:
        raise HTTPException(status_code=404, detail="Job not found")
    return job


@app.post(
    "/api/ai/predict-single",
    tags=["Analysis"],
    summary="Real-time Single Prediction",
)
async def predict_single(report: IrregularityReport):
    """
    Get immediate AI predictions for a single irregularity report.
    Useful for real-time validation or "what-if" analysis in the UI.
    """
    try:
        report_dict = report.model_dump(exclude_none=True)
        predictions = model_service.predict_regression([report_dict])
        classifications = model_service.classify_text([report_dict])
        entities = model_service.extract_entities([report_dict])
        summaries = model_service.generate_summary([report_dict])
        sentiment = model_service.analyze_sentiment([report_dict])

        return {
            "prediction": predictions[0],
            "classification": classifications[0],
            "entities": entities[0],
            "summary": summaries[0],
            "sentiment": sentiment[0],
            "modelLoaded": model_service.model_loaded,
        }

    except Exception as e:
        logger.error(f"Single prediction error: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))


@app.post(
    "/api/ai/train",
    tags=["Training"],
    summary="Trigger Model Retraining",
)
async def train_models(background_tasks: BackgroundTasks, force: bool = False):
    """
    Trigger a background task to retrain AI models.
    Checks if new data is available in Google Sheets before training, unless `force=True`.
    """
    from scripts.scheduled_training import TrainingScheduler

    def run_training_task():
        scheduler = TrainingScheduler()
        result = scheduler.run_training(force=force)
        logger.info(f"Training completed: {result}")

    background_tasks.add_task(run_training_task)

    return {
        "status": "training_queued",
        "message": "Model retraining has been started in the background",
        "force": force,
        "timestamp": datetime.now().isoformat(),
    }


@app.get(
    "/api/ai/train/status",
    tags=["Training"],
    summary="Get Training Status",
)
async def training_status():
    """
    Get the status of the latest training job and training history.
    """
    from scripts.scheduled_training import TrainingScheduler

    scheduler = TrainingScheduler()
    status = scheduler.get_status()

    return {
        "status": "success",
        "data": status,
        "timestamp": datetime.now().isoformat(),
    }


@app.get("/api/ai/model-info")
async def model_info():
    """Get current model information"""
    return {
        "regression": {
            "version": model_service.regression_version,
            "type": "GradientBoostingRegressor",
            "status": "loaded" if model_service.model_loaded else "unavailable",
            "last_trained": "2025-01-15",
            "metrics": model_service.model_metrics
            if model_service.model_loaded
            else None,
        },
        "nlp": {
            "version": model_service.nlp_version,
            "type": "Rule-based + Keyword extraction",
            "status": "active",
            "tasks": ["classification", "ner", "summarization", "sentiment"],
            "note": "Full ML NLP models coming soon",
        },
    }


@app.post("/api/ai/cache/invalidate")
async def invalidate_cache(sheet_name: Optional[str] = None):
    """Invalidate cache for sheets data"""
    cache = get_cache()

    if sheet_name:
        pattern = f"sheets:*{sheet_name}*"
        deleted = cache.delete_pattern(pattern)
        return {
            "status": "success",
            "message": f"Invalidated cache for sheet: {sheet_name}",
            "keys_deleted": deleted,
        }
    else:
        deleted = cache.delete_pattern("sheets:*")
        return {
            "status": "success",
            "message": "Invalidated all sheets cache",
            "keys_deleted": deleted,
        }


@app.get("/api/ai/cache/status")
async def cache_status():
    """Get cache status and statistics"""
    cache = get_cache()
    return cache.health_check()


class AnalyzeAllResponse(BaseModel):
    status: str
    metadata: Dict[str, Any]
    sheets: Dict[str, Any]
    results: List[Dict[str, Any]]
    summary: Dict[str, Any]
    timestamp: str


@app.get("/api/ai/analyze-all", response_model=AnalyzeAllResponse)
async def analyze_all_sheets(
    bypass_cache: bool = False,
    include_regression: bool = True,
    include_nlp: bool = True,
    include_trends: bool = True,
    max_rows_per_sheet: int = 10000,
    compact: bool = False,
):
    """
    Analyze ALL rows from all Google Sheets

    Fetches data from both NON CARGO and CGO sheets, analyzes each row,
    and returns comprehensive results.

    Args:
        bypass_cache: Skip cache and fetch fresh data
        include_regression: Include regression predictions
        include_nlp: Include NLP analysis (severity, entities, summary)
        include_trends: Include trend analysis
        max_rows_per_sheet: Maximum rows to process per sheet
    """
    start_time = datetime.now()

    try:
        from data.sheets_service import GoogleSheetsService

        cache = get_cache() if not bypass_cache else None
        sheets_service = GoogleSheetsService(cache=cache)

        spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
        if not spreadsheet_id:
            raise HTTPException(
                status_code=500, detail="GOOGLE_SHEET_ID not configured"
            )

        all_data = []
        sheet_info = {}

        sheets_to_fetch = [
            {"name": "NON CARGO", "range": f"A1:AA{max_rows_per_sheet + 1}"},
            {"name": "CGO", "range": f"A1:Z{max_rows_per_sheet + 1}"},
        ]

        for sheet in sheets_to_fetch:
            try:
                sheet_name = sheet["name"]
                range_str = sheet["range"]

                logger.info(f"Fetching {sheet_name}...")
                data = sheets_service.fetch_sheet_data(
                    spreadsheet_id, sheet_name, range_str, bypass_cache=bypass_cache
                )

                for row in data:
                    row["_source_sheet"] = sheet_name
                    all_data.append(row)

                sheet_info[sheet_name] = {
                    "rows_fetched": len(data),
                    "status": "success",
                }

            except Exception as e:
                logger.error(f"Failed to fetch {sheet['name']}: {e}")
                sheet_info[sheet["name"]] = {
                    "rows_fetched": 0,
                    "status": "error",
                    "error": str(e),
                }

        total_records = len(all_data)

        if total_records == 0:
            raise HTTPException(status_code=404, detail="No data found in any sheet")

        logger.info(f"Analyzing {total_records} total records...")

        results = []
        batch_size = 100

        for i in range(0, total_records, batch_size):
            batch = all_data[i : i + batch_size]

            if include_regression:
                regression_preds = model_service.predict_regression(batch)
            else:
                regression_preds = [None] * len(batch)

            if include_nlp:
                classifications = model_service.classify_text(batch)
                entities = model_service.extract_entities(batch)
                summaries = model_service.generate_summary(batch)
                sentiments = model_service.analyze_sentiment(batch)
            else:
                classifications = [None] * len(batch)
                entities = [None] * len(batch)
                summaries = [None] * len(batch)
                sentiments = [None] * len(batch)

            for j, row in enumerate(batch):
                result = {
                    "rowId": row.get("_row_id", f"row_{i + j}"),
                    "sourceSheet": row.get("_source_sheet", "Unknown"),
                    "originalData": {
                        "date": row.get("Date_of_Event"),
                        "airline": row.get("Airlines"),
                        "flightNumber": row.get("Flight_Number"),
                        "branch": row.get("Branch"),
                        "hub": row.get("HUB"),
                        "route": row.get("Route"),
                        "category": row.get("Report_Category"),
                        "issueType": row.get("Irregularity_Complain_Category"),
                        "report": row.get("Report"),
                        "status": row.get("Status"),
                    },
                }

                if regression_preds[j]:
                    pred = {
                        "predictedDays": regression_preds[j].predictedDays,
                        "confidenceInterval": regression_preds[j].confidenceInterval,
                        "hasUnknownCategories": regression_preds[j].hasUnknownCategories,
                    }
                    if not compact:
                        pred["shapExplanation"] = (
                            regression_preds[j].shapExplanation.model_dump()
                            if regression_preds[j].shapExplanation
                            else None
                        )
                        pred["anomalyDetection"] = (
                            regression_preds[j].anomalyDetection.model_dump()
                            if regression_preds[j].anomalyDetection
                            else None
                        )
                    result["prediction"] = pred

                if classifications[j]:
                    result["classification"] = classifications[j].model_dump()

                if entities[j] and not compact:
                    result["entities"] = entities[j].model_dump()

                if summaries[j] and not compact:
                    result["summary"] = summaries[j].model_dump()

                if sentiments[j] and not compact:
                    result["sentiment"] = sentiments[j].model_dump()

                results.append(result)

        summary = {
            "totalRecords": total_records,
            "sheetsProcessed": len(
                [s for s in sheet_info.values() if s["status"] == "success"]
            ),
            "regressionEnabled": include_regression,
            "nlpEnabled": include_nlp,
        }

        if include_nlp and results:
            severity_counts = {}
            for r in results:
                sev = r.get("classification", {}).get("severity", "Unknown")
                severity_counts[sev] = severity_counts.get(sev, 0) + 1
            summary["severityDistribution"] = severity_counts

        if include_regression and results:
            predictions = [
                r["prediction"]["predictedDays"] for r in results if r.get("prediction")
            ]
            if predictions:
                summary["predictionStats"] = {
                    "min": round(min(predictions), 2),
                    "max": round(max(predictions), 2),
                    "mean": round(sum(predictions) / len(predictions), 2),
                }

        processing_time = (datetime.now() - start_time).total_seconds()

        return AnalyzeAllResponse(
            status="success",
            metadata={
                "totalRecords": total_records,
                "processingTimeSeconds": round(processing_time, 2),
                "recordsPerSecond": round(total_records / processing_time, 2)
                if processing_time > 0
                else 0,
                "modelVersions": {
                    "regression": model_service.regression_version,
                    "nlp": model_service.nlp_version,
                },
            },
            sheets=sheet_info,
            results=results,
            summary=summary,
            timestamp=datetime.now().isoformat(),
        )

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Analyze all error: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))


# ============== Risk Scoring Endpoints ==============


@app.get("/api/ai/risk/summary")
async def risk_summary():
    """Get overall risk summary for all entities"""
    from data.risk_service import get_risk_service

    risk_service = get_risk_service()
    return risk_service.get_risk_summary()


@app.get("/api/ai/risk/airlines")
async def airline_risks():
    """Get risk scores for all airlines"""
    from data.risk_service import get_risk_service

    risk_service = get_risk_service()
    return risk_service.get_all_airline_risks()


@app.get("/api/ai/risk/airlines/{airline_name}")
async def airline_risk(airline_name: str):
    """Get risk score for a specific airline"""
    from data.risk_service import get_risk_service

    risk_service = get_risk_service()
    risk_data = risk_service.get_airline_risk(airline_name)

    if not risk_data:
        raise HTTPException(
            status_code=404, detail=f"Airline '{airline_name}' not found"
        )

    recommendations = risk_service.get_risk_recommendations("airline", airline_name)

    return {
        "airline": airline_name,
        "risk_data": risk_data,
        "recommendations": recommendations,
    }


@app.get("/api/ai/risk/branches")
async def branch_risks():
    """Get risk scores for all branches"""
    from data.risk_service import get_risk_service

    risk_service = get_risk_service()
    return risk_service.get_all_branch_risks()


@app.get("/api/ai/risk/hubs")
async def hub_risks():
    """Get risk scores for all hubs"""
    from data.risk_service import get_risk_service

    risk_service = get_risk_service()
    return risk_service.get_all_hub_risks()


@app.post("/api/ai/risk/calculate")
async def calculate_risk_scores(bypass_cache: bool = False):
    """Calculate risk scores from current Google Sheets data"""
    from data.risk_service import get_risk_service
    from data.sheets_service import GoogleSheetsService

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    # Fetch all data
    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA2000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z2000", bypass_cache=bypass_cache
    )
    all_data = non_cargo + cargo

    risk_service = get_risk_service()
    risk_data = risk_service.calculate_all_risk_scores(all_data)

    return {
        "status": "success",
        "records_processed": len(all_data),
        "risk_summary": risk_service.get_risk_summary(),
    }


# ============== Subcategory Classification Endpoints ==============


@app.post("/api/ai/subcategory")
async def classify_subcategory(
    report: str,
    area: Optional[str] = None,
    issue_type: Optional[str] = None,
    root_cause: Optional[str] = None,
):
    """Classify report into subcategory"""
    from data.subcategory_service import get_subcategory_classifier

    classifier = get_subcategory_classifier()
    result = classifier.classify(report, area, issue_type, root_cause)

    return result


@app.get("/api/ai/subcategory/categories")
async def get_subcategories(area: Optional[str] = None):
    """Get list of available subcategories"""
    from data.subcategory_service import get_subcategory_classifier

    classifier = get_subcategory_classifier()
    return classifier.get_available_categories(area)


# ============== Action Recommendation Endpoints ==============


@app.post("/api/ai/action/recommend")
async def recommend_actions(
    report: str,
    issue_type: str,
    severity: str = "Medium",
    area: Optional[str] = None,
    airline: Optional[str] = None,
    top_n: int = 5,
):
    """Get action recommendations for an issue"""
    from data.action_service import get_action_service

    action_service = get_action_service()
    recommendations = action_service.recommend(
        report=report,
        issue_type=issue_type,
        severity=severity,
        area=area,
        airline=airline,
        top_n=top_n,
    )

    return recommendations


@app.post("/api/ai/action/train")
async def train_action_recommender(
    bypass_cache: bool = False, background_tasks: BackgroundTasks = None
):
    """Train action recommender from historical data"""
    from data.action_service import get_action_service
    from data.sheets_service import GoogleSheetsService
    from data.similarity_service import get_similarity_service

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA2000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z2000", bypass_cache=bypass_cache
    )
    all_data = non_cargo + cargo

    similarity_service = get_similarity_service()
    similarity_service.build_index(all_data)

    action_service = get_action_service()
    action_service.train_from_data(all_data)

    return {
        "status": "success",
        "records_processed": len(all_data),
    }


# ============== Advanced NER Endpoints ==============


@app.post("/api/ai/ner/extract")
async def extract_entities(text: str):
    """Extract entities from text"""
    from data.advanced_ner_service import get_advanced_ner

    ner = get_advanced_ner()
    entities = ner.extract(text)
    summary = ner.extract_summary(text)

    return {
        "entities": entities,
        "summary": summary,
    }


# ============== Similarity Endpoints ==============


@app.post("/api/ai/similar")
async def find_similar_reports(
    text: str,
    top_k: int = 5,
    threshold: float = 0.3,
):
    """Find similar reports"""
    from data.similarity_service import get_similarity_service

    similarity_service = get_similarity_service()
    similar = similarity_service.find_similar(text, top_k, threshold)

    return {
        "query_preview": text[:100],
        "similar_reports": similar,
    }


@app.post("/api/ai/similar/build-index")
async def build_similarity_index(bypass_cache: bool = False):
    """Build similarity index from Google Sheets data"""
    from data.similarity_service import get_similarity_service
    from data.sheets_service import GoogleSheetsService

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA2000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z2000", bypass_cache=bypass_cache
    )
    all_data = non_cargo + cargo

    similarity_service = get_similarity_service()
    similarity_service.build_index(all_data)

    return {
        "status": "success",
        "records_indexed": len(all_data),
    }


# ============== Forecasting Endpoints ==============


@app.get("/api/ai/forecast/issues")
async def forecast_issues(periods: int = 4):
    """Forecast issue volume for next periods"""
    from data.forecast_service import get_forecast_service

    forecast_service = get_forecast_service()
    forecast = forecast_service.forecast_issues(periods)

    return forecast


@app.get("/api/ai/forecast/trends")
async def predict_trends():
    """Predict category trends"""
    from data.forecast_service import get_forecast_service

    forecast_service = get_forecast_service()
    trends = forecast_service.predict_category_trends()

    return trends


@app.get("/api/ai/forecast/seasonal")
async def get_seasonal_patterns():
    """Get seasonal patterns"""
    from data.forecast_service import get_forecast_service

    forecast_service = get_forecast_service()
    patterns = forecast_service.get_seasonal_patterns()

    return patterns


@app.post("/api/ai/forecast/build")
async def build_forecast_data(bypass_cache: bool = False):
    """Build forecast historical data from Google Sheets"""
    from data.forecast_service import get_forecast_service
    from data.sheets_service import GoogleSheetsService

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA2000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z2000", bypass_cache=bypass_cache
    )
    all_data = non_cargo + cargo

    forecast_service = get_forecast_service()
    forecast_service.build_historical_data(all_data)

    return {
        "status": "success",
        "records_processed": len(all_data),
        "forecast_summary": forecast_service.get_forecast_summary(),
    }


# ============== Report Generation Endpoints ==============


@app.post("/api/ai/report/generate")
async def generate_report(
    row_id: str,
    bypass_cache: bool = False,
):
    """Generate formal incident report"""
    from data.report_generator_service import get_report_generator
    from data.sheets_service import GoogleSheetsService
    from data.risk_service import get_risk_service

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    # Fetch all data and find the record
    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA2000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z2000", bypass_cache=bypass_cache
    )
    all_data = non_cargo + cargo

    record = None
    for r in all_data:
        if r.get("_row_id") == row_id:
            record = r
            break

    if not record:
        raise HTTPException(status_code=404, detail=f"Record '{row_id}' not found")

    # Generate analysis
    report_text = record.get("Report", "") + " " + record.get("Root_Caused", "")
    analysis = {
        "severity": model_service._classify_severity_fallback([report_text])[0].get(
            "severity", "Medium"
        ),
        "issueType": record.get("Irregularity_Complain_Category", ""),
    }

    # Get risk data
    risk_service = get_risk_service()
    airline = record.get("Airlines", "")
    risk_data = risk_service.get_airline_risk(airline)

    # Generate report
    report_gen = get_report_generator()
    formal_report = report_gen.generate_incident_report(record, analysis, risk_data)
    exec_summary = report_gen.generate_executive_summary(record, analysis)
    json_report = report_gen.generate_json_report(record, analysis, risk_data)

    return {
        "row_id": row_id,
        "formal_report": formal_report,
        "executive_summary": exec_summary,
        "structured_report": json_report,
    }


# ============== Dashboard Endpoints ==============


@app.get("/api/ai/dashboard/summary")
async def dashboard_summary(bypass_cache: bool = False):
    """Get comprehensive dashboard summary"""
    from data.risk_service import get_risk_service
    from data.forecast_service import get_forecast_service
    from data.sheets_service import GoogleSheetsService

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    # Fetch data
    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA2000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z2000", bypass_cache=bypass_cache
    )
    all_data = non_cargo + cargo

    # Get risk summary
    risk_service = get_risk_service()
    risk_summary = risk_service.get_risk_summary()

    # Get forecast summary
    forecast_service = get_forecast_service()
    forecast_summary = forecast_service.get_forecast_summary()

    # Calculate statistics
    severity_dist = Counter()
    category_dist = Counter()
    airline_dist = Counter()

    for record in all_data:
        report_text = record.get("Report", "") + " " + record.get("Root_Caused", "")
        sev = model_service._classify_severity_fallback([report_text])[0].get(
            "severity", "Low"
        )
        severity_dist[sev] += 1
        category_dist[record.get("Irregularity_Complain_Category", "Unknown")] += 1
        airline_dist[record.get("Airlines", "Unknown")] += 1

    return {
        "total_records": len(all_data),
        "sheets": {
            "non_cargo": len(non_cargo),
            "cargo": len(cargo),
        },
        "severity_distribution": dict(severity_dist),
        "category_distribution": dict(category_dist.most_common(10)),
        "top_airlines": dict(airline_dist.most_common(10)),
        "risk_summary": risk_summary,
        "forecast_summary": forecast_summary,
        "model_status": {
            "regression": model_service.model_loaded,
            "nlp": model_service.nlp_service is not None,
        },
        "last_updated": datetime.now().isoformat(),
    }


# ============== Seasonality Endpoints ==============


@app.get("/api/ai/seasonality/summary")
async def seasonality_summary(category_type: Optional[str] = None):
    """
    Get seasonality summary and patterns

    Args:
        category_type: "landside_airside", "cgo", or None for both
    """
    from data.seasonality_service import get_seasonality_service

    service = get_seasonality_service()
    return service.get_seasonality_summary(category_type)


@app.get("/api/ai/seasonality/forecast")
async def seasonality_forecast(
    category_type: Optional[str] = None,
    periods: int = 4,
    granularity: str = "weekly",
):
    """
    Forecast issue volumes

    Args:
        category_type: "landside_airside", "cgo", or None for both
        periods: Number of periods to forecast
        granularity: "daily", "weekly", or "monthly"
    """
    from data.seasonality_service import get_seasonality_service

    service = get_seasonality_service()
    return service.forecast(category_type, periods, granularity)


@app.get("/api/ai/seasonality/peaks")
async def seasonality_peaks(
    category_type: Optional[str] = None, threshold: float = 1.2
):
    """
    Identify peak periods

    Args:
        category_type: "landside_airside", "cgo", or None for both
        threshold: Multiplier above average (1.2 = 20% above)
    """
    from data.seasonality_service import get_seasonality_service

    service = get_seasonality_service()
    return service.get_peak_periods(category_type, threshold)


@app.post("/api/ai/seasonality/build")
async def build_seasonality_patterns(bypass_cache: bool = False):
    """Build seasonality patterns from Google Sheets data"""
    from data.seasonality_service import get_seasonality_service
    from data.sheets_service import GoogleSheetsService

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA5000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z5000", bypass_cache=bypass_cache
    )

    for row in non_cargo:
        row["_sheet_name"] = "NON CARGO"
    for row in cargo:
        row["_sheet_name"] = "CGO"

    all_data = non_cargo + cargo

    service = get_seasonality_service()
    result = service.build_patterns(all_data)

    return {
        "status": "success",
        "records_processed": len(all_data),
        "patterns": result,
    }


# ============== Root Cause Endpoints ==============


@app.post("/api/ai/root-cause/classify")
async def classify_root_cause(
    root_cause: str,
    report: Optional[str] = None,
    area: Optional[str] = None,
    category: Optional[str] = None,
):
    """
    Classify a root cause text into categories

    Categories: Equipment Failure, Staff Competency, Process/Procedure,
    Communication, External Factors, Documentation, Training Gap, Resource/Manpower
    """
    from data.root_cause_service import get_root_cause_service

    service = get_root_cause_service()
    context = {"area": area, "category": category}
    result = service.classify(root_cause, report or "", context)

    return result


@app.post("/api/ai/root-cause/classify-batch")
async def classify_root_cause_batch(bypass_cache: bool = False):
    """Classify root causes for all records"""
    from data.root_cause_service import get_root_cause_service
    from data.sheets_service import GoogleSheetsService

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA5000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z5000", bypass_cache=bypass_cache
    )
    all_data = non_cargo + cargo

    service = get_root_cause_service()
    results = service.classify_batch(all_data)

    return {
        "status": "success",
        "records_processed": len(all_data),
        "classifications": results[:100],
        "total_classified": len(
            [r for r in results if r["primary_category"] != "Unknown"]
        ),
    }


@app.get("/api/ai/root-cause/categories")
async def get_root_cause_categories():
    """Get all available root cause categories"""
    from data.root_cause_service import get_root_cause_service

    service = get_root_cause_service()
    return service.get_categories()


@app.get("/api/ai/root-cause/stats")
async def get_root_cause_stats(source: Optional[str] = None, bypass_cache: bool = False):
    """
    Get root cause statistics from data

    Args:
        source: "NON CARGO", "CGO", or None for both
        bypass_cache: Skip cache and fetch fresh data
    """
    from data.root_cause_service import get_root_cause_service
    from data.sheets_service import GoogleSheetsService

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    all_data = []
    
    # Conditional fetching based on source to reduce I/O and processing
    if not source or source.upper() == "NON CARGO":
        non_cargo = sheets_service.fetch_sheet_data(
            spreadsheet_id, "NON CARGO", "A1:AA5000", bypass_cache=bypass_cache
        )
        all_data.extend(non_cargo)
    
    if not source or source.upper() == "CGO":
        cargo = sheets_service.fetch_sheet_data(
            spreadsheet_id, "CGO", "A1:Z5000", bypass_cache=bypass_cache
        )
        all_data.extend(cargo)

    service = get_root_cause_service()
    stats = service.get_statistics(all_data)

    return stats


@app.post("/api/ai/root-cause/train")
async def train_root_cause_classifier(background_tasks: BackgroundTasks, bypass_cache: bool = False):
    """Train root cause classifier from historical data"""
    from data.root_cause_service import get_root_cause_service
    from data.sheets_service import GoogleSheetsService

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA5000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z5000", bypass_cache=bypass_cache
    )
    all_data = non_cargo + cargo

    service = get_root_cause_service()
    
    # Offload the intensive training process to the background
    background_tasks.add_task(service.train_from_data, all_data)

    return {
        "status": "training_started",
        "records_fetched": len(all_data),
        "message": "Classification training is now running in the background. The model will be automatically updated once complete."
    }


# ============== Category Summarization Endpoints ==============


class CategorySummaryResponse(BaseModel):
    status: str
    category_type: str
    summary: Dict[str, Any]
    timestamp: str


@app.get("/api/ai/summarize", response_model=CategorySummaryResponse)
async def summarize_by_category(category: str = "all", bypass_cache: bool = False):
    """
    Get summarized insights for Non-cargo and/or CGO categories

    Query Parameters:
        category: "non_cargo", "cgo", or "all" (default: "all")
        bypass_cache: Skip cache and fetch fresh data (default: false)

    Returns aggregated summary including:
        - Severity distribution
        - Top categories, airlines, hubs, branches
        - Key insights and recommendations
        - Common issues
        - Monthly trends
    """
    from data.category_summarization_service import get_category_summarization_service
    from data.sheets_service import GoogleSheetsService

    valid_categories = ["non_cargo", "cgo", "all"]
    if category.lower() not in valid_categories:
        raise HTTPException(
            status_code=400,
            detail=f"Invalid category. Must be one of: {valid_categories}",
        )

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA5000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z5000", bypass_cache=bypass_cache
    )

    for row in non_cargo:
        row["_sheet_name"] = "NON CARGO"
    for row in cargo:
        row["_sheet_name"] = "CGO"

    all_data = non_cargo + cargo

    summarization_service = get_category_summarization_service()
    summary = summarization_service.summarize_category(all_data, category.lower())

    return CategorySummaryResponse(
        status="success",
        category_type=category.lower(),
        summary=summary,
        timestamp=datetime.now().isoformat(),
    )


@app.get("/api/ai/summarize/non-cargo")
async def summarize_non_cargo(bypass_cache: bool = False):
    """Quick endpoint for Non-cargo summary"""
    return await summarize_by_category(category="non_cargo", bypass_cache=bypass_cache)


@app.get("/api/ai/summarize/cgo")
async def summarize_cgo(bypass_cache: bool = False):
    """Quick endpoint for CGO (Cargo) summary"""
    return await summarize_by_category(category="cgo", bypass_cache=bypass_cache)


@app.get("/api/ai/summarize/compare")
async def compare_categories(bypass_cache: bool = False):
    """Compare Non-cargo and CGO categories side by side"""
    return await summarize_by_category(category="all", bypass_cache=bypass_cache)


# ============== Branch Analytics Endpoints ==============


@app.get("/api/ai/branch/summary")
async def branch_analytics_summary(category_type: Optional[str] = None):
    """
    Get branch analytics summary

    Args:
        category_type: "landside_airside", "cgo", or None for both
    """
    from data.branch_analytics_service import get_branch_analytics_service

    service = get_branch_analytics_service()
    return service.get_summary(category_type)


@app.get("/api/ai/branch/{branch_name}")
async def get_branch_metrics(branch_name: str, category_type: Optional[str] = None):
    """
    Get metrics for a specific branch

    Args:
        branch_name: Branch name
        category_type: "landside_airside", "cgo", or None for combined
    """
    from data.branch_analytics_service import get_branch_analytics_service

    service = get_branch_analytics_service()
    data = service.get_branch(branch_name, category_type)

    if not data:
        raise HTTPException(status_code=404, detail=f"Branch '{branch_name}' not found")

    return data


@app.get("/api/ai/branch/ranking")
async def branch_ranking(
    category_type: Optional[str] = None,
    sort_by: str = "risk_score",
    limit: int = 20,
):
    """
    Get branch ranking

    Args:
        category_type: "landside_airside", "cgo", or None for both
        sort_by: Field to sort by (risk_score, total_issues, critical_high_count)
        limit: Maximum branches to return
    """
    from data.branch_analytics_service import get_branch_analytics_service

    service = get_branch_analytics_service()
    return service.get_ranking(category_type, sort_by, limit)


@app.get("/api/ai/branch/comparison")
async def branch_comparison():
    """Compare all branches across category types"""
    from data.branch_analytics_service import get_branch_analytics_service

    service = get_branch_analytics_service()
    return service.get_comparison()


@app.post("/api/ai/branch/calculate")
async def calculate_branch_metrics(bypass_cache: bool = False):
    """Calculate branch metrics from Google Sheets data"""
    from data.branch_analytics_service import get_branch_analytics_service
    from data.sheets_service import GoogleSheetsService

    cache = get_cache() if not bypass_cache else None
    sheets_service = GoogleSheetsService(cache=cache)

    spreadsheet_id = os.getenv("GOOGLE_SHEET_ID")
    if not spreadsheet_id:
        raise HTTPException(status_code=500, detail="GOOGLE_SHEET_ID not configured")

    non_cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "NON CARGO", "A1:AA5000", bypass_cache=bypass_cache
    )
    cargo = sheets_service.fetch_sheet_data(
        spreadsheet_id, "CGO", "A1:Z5000", bypass_cache=bypass_cache
    )

    for row in non_cargo:
        row["_sheet_name"] = "NON CARGO"
    for row in cargo:
        row["_sheet_name"] = "CGO"

    all_data = non_cargo + cargo

    service = get_branch_analytics_service()
    result = service.calculate_branch_metrics(all_data)

    return {
        "status": "success",
        "records_processed": len(all_data),
        "metrics": result,
    }


# ============== Main ==============

if __name__ == "__main__":
    import uvicorn

    port = int(os.getenv("API_PORT", 8000))
    uvicorn.run(app, host="0.0.0.0", port=port)