import pandas as pd
import io
import re
import yaml
from typing import List, Optional
from fastapi import FastAPI, File, UploadFile, HTTPException, Query
from fastapi.responses import JSONResponse
import uvicorn
from fastapi.middleware.cors import CORSMiddleware

app = FastAPI()

# Carregar configuração
with open("column_config.yaml") as f:
    COLUMN_CONFIG = yaml.safe_load(f)

# Função para detectar tipos de colunas
def detect_column_type(dtype):
    if pd.api.types.is_datetime64_any_dtype(dtype):
        return "datetime"
    elif pd.api.types.is_numeric_dtype(dtype):
        return "number"
    return "text"

# Normalização de colunas
def normalize_column_names(column_names: List[str]) -> List[str]:
    normalized = []
    for raw_col in column_names:
        sanitized = re.sub(r'[\W]+', '_', raw_col.strip()).lower().strip('_')
        for config_col, config in COLUMN_CONFIG['columns'].items():
            synonyms = [
                re.sub(r'[\W]+', '_', s.strip()).lower().strip('_')
                for s in [config_col] + config.get('synonyms', [])
            ]
            if sanitized in synonyms:
                normalized.append(config_col)
                break
        else:
            normalized.append(sanitized)
    return normalized

# Limpeza de dados aprimorada
def clean_data(df: pd.DataFrame) -> pd.DataFrame:
    df.columns = normalize_column_names(df.columns)

    # Tratamento de valores ausentes
    for col in df.columns:
        if col in COLUMN_CONFIG['columns']:
            col_type = COLUMN_CONFIG['columns'][col].get('type', 'text')
            if col_type == 'datetime':
                df[col] = pd.to_datetime(df[col], errors='coerce')
            elif col_type == 'numeric':
                df[col] = pd.to_numeric(df[col], errors='coerce')
            elif col_type == 'categorical':
                allowed = COLUMN_CONFIG['columns'][col].get('allowed', [])
                df[col] = df[col].where(df[col].isin(allowed), None)

    # Tratamento de formatos inconsistentes
    for col in df.columns:
        if col in COLUMN_CONFIG['columns']:
            col_type = COLUMN_CONFIG['columns'][col].get('type', 'text')
            if col_type == 'datetime':
                fmt = COLUMN_CONFIG['columns'][col].get('format')
                df[col] = pd.to_datetime(df[col], errors='coerce', format=fmt)
                df[col] = df[col].dt.strftime('%Y-%m-%dT%H:%M:%SZ')
            elif col_type == 'numeric':
                df[col] = pd.to_numeric(df[col], errors='coerce').astype(float)
            elif col_type == 'categorical':
                allowed = COLUMN_CONFIG['columns'][col].get('allowed', [])
                df[col] = df[col].where(df[col].isin(allowed))

    # Tratamento de outliers
    for col in df.columns:
        if col in COLUMN_CONFIG['columns']:
            col_type = COLUMN_CONFIG['columns'][col].get('type', 'text')
            if col_type == 'numeric':
                q1 = df[col].quantile(0.25)
                q3 = df[col].quantile(0.75)
                iqr = q3 - q1
                lower_bound = q1 - 1.5 * iqr
                upper_bound = q3 + 1.5 * iqr
                df[col] = df[col].clip(lower=lower_bound, upper=upper_bound)

    # Tratamento de registros duplicados
    df.drop_duplicates(inplace=True)

    # Tratamento de tipos de dados mistos
    for col in df.columns:
        if col in COLUMN_CONFIG['columns']:
            col_type = COLUMN_CONFIG['columns'][col].get('type', 'text')
            if col_type == 'numeric':
                df[col] = pd.to_numeric(df[col], errors='coerce')
            elif col_type == 'datetime':
                df[col] = pd.to_datetime(df[col], errors='coerce')

    # Tratamento de dados ruídos
    for col in df.columns:
        if col in COLUMN_CONFIG['columns']:
            col_type = COLUMN_CONFIG['columns'][col].get('type', 'text')
            if col_type == 'text':
                df[col] = df[col].str.strip().str.lower()

    return df.replace({pd.NA: None})

# Função para processar o arquivo e retornar dados limpos
def process_file(file: UploadFile, sheet_name: Optional[str] = None) -> pd.DataFrame:
    try:
        content = file.file.read()
        extension = file.filename.split('.')[-1]
        if extension == 'csv':
            df = pd.read_csv(io.BytesIO(content))
        elif extension == 'xlsx':
            if sheet_name is None:
                sheet_name = 0  # Default to the first sheet
            df = pd.read_excel(io.BytesIO(content), sheet_name=sheet_name)
        else:
            raise HTTPException(400, "Formato de arquivo não suportado")
        return df, clean_data(df)
    except Exception as e:
        raise HTTPException(500, f"Erro ao processar o arquivo: {str(e)}")

# Endpoint para upload e processamento de arquivos
@app.post("/process-file")
async def process_file_endpoint(file: UploadFile = File(...), sheet_name: Optional[str] = Query(None)):
    try:
        raw_df, df = process_file(file, sheet_name)

        columns = [{
            "name": col,
            "type": detect_column_type(df[col].dtype)
        } for col in df.columns]

        rows = []
        for idx, row in df.iterrows():
            cells = {}
            for col, val in row.items():
                cells[col] = {
                    "value": val,
                    "displayValue": str(val),
                    "columnId": col
                }
            rows.append({"id": str(idx), "cells": cells})

        return JSONResponse(
            content={
                "data": {
                    "columns": columns,
                    "rows": rows
                },
                "metadata": {
                    "totalRows": len(df),
                    "processedAt": pd.Timestamp.now().isoformat()
                }
            })
    except Exception as e:
        raise HTTPException(500, f"Erro: {str(e)}")

# Configuração de CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

if __name__ == "__main__":
    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)