from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
import pandas as pd
import os
from typing import List, Dict, Any

# Response models
class FeatureExample(BaseModel):
    category: str
    region: str
    inventory_level: float
    units_ordered: int
    demand_forecast: float

class FeaturesResponse(BaseModel):
    available_categories: List[str]
    available_regions: List[str]
    sample_data: List[FeatureExample]
    statistics: Dict[str, Any]

class SampleDataResponse(BaseModel):
    total_records: int
    sample_records: List[Dict[str, Any]]

# Create router
router = APIRouter()

def load_sample_data():
    """Load sample data from CSV file"""
    try:
        data_path = "data/sample_data.csv"
        if os.path.exists(data_path):
            df = pd.read_csv(data_path)
            return df
        else:
            # Return mock data if file doesn't exist
            return pd.DataFrame({
                'Category': ['Electronics', 'Clothing', 'Home & Garden', 'Sports'],
                'Region': ['North', 'South', 'East', 'West'],
                'Inventory Level': [150.5, 89.2, 205.7, 125.8],
                'Units Ordered': [25, 15, 40, 30],
                'Demand Forecast': [200.0, 120.5, 300.2, 180.4]
            })
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error loading data: {str(e)}")

@router.get("/get-features", response_model=FeaturesResponse)
async def get_features():
    """
    Get available feature values and sample data for prediction.
    Returns categories, regions, and example data for users.
    """
    try:
        df = load_sample_data()
        
        # Get unique values
        categories = df['Category'].unique().tolist() if 'Category' in df.columns else []
        regions = df['Region'].unique().tolist() if 'Region' in df.columns else []
        
        # Create sample examples (first 3 records)
        sample_data = []
        for _, row in df.head(3).iterrows():
            sample_data.append(FeatureExample(
                category=row.get('Category', 'Electronics'),
                region=row.get('Region', 'North'),
                inventory_level=float(row.get('Inventory Level', 100.0)),
                units_ordered=int(row.get('Units Ordered', 20)),
                demand_forecast=float(row.get('Demand Forecast', 150.0))
            ))
        
        # Calculate basic statistics
        statistics = {}
        if 'Inventory Level' in df.columns:
            statistics['inventory_level'] = {
                'min': float(df['Inventory Level'].min()),
                'max': float(df['Inventory Level'].max()),
                'mean': float(df['Inventory Level'].mean())
            }
        
        if 'Units Ordered' in df.columns:
            statistics['units_ordered'] = {
                'min': int(df['Units Ordered'].min()),
                'max': int(df['Units Ordered'].max()),
                'mean': float(df['Units Ordered'].mean())
            }
        
        if 'Demand Forecast' in df.columns:
            statistics['demand_forecast'] = {
                'min': float(df['Demand Forecast'].min()),
                'max': float(df['Demand Forecast'].max()),
                'mean': float(df['Demand Forecast'].mean())
            }
        
        return FeaturesResponse(
            available_categories=categories,
            available_regions=regions,
            sample_data=sample_data,
            statistics=statistics
        )
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting features: {str(e)}")

@router.get("/sample-data", response_model=SampleDataResponse)
async def get_sample_data(limit: int = 5):
    """
    Get raw sample data from the dataset.
    
    Args:
        limit: Number of records to return (default: 5, max: 20)
    """
    try:
        if limit > 20:
            limit = 20
        
        df = load_sample_data()
        
        # Convert to dict records
        sample_records = df.head(limit).to_dict('records')
        
        return SampleDataResponse(
            total_records=len(df),
            sample_records=sample_records
        )
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting sample data: {str(e)}")