import os
import re
import pandas as pd
from typing import Optional
from utils.config import AppConfig
from utils.tracing import Tracer

class SQLTool:
    def __init__(self, cfg: AppConfig, tracer: Tracer):
        self.cfg = cfg
        self.tracer = tracer
        self.backend = cfg.sql_backend  # "bigquery" or "motherduck"
        if self.backend == "bigquery":
            from google.cloud import bigquery
            from google.oauth2 import service_account
            key_json = os.getenv("GCP_SERVICE_ACCOUNT_JSON")
            if not key_json:
                raise RuntimeError("Missing GCP_SERVICE_ACCOUNT_JSON secret")
            creds = service_account.Credentials.from_service_account_info(
                eval(key_json) if key_json.strip().startswith("{") else {}
            )
            self.client = bigquery.Client(credentials=creds, project=cfg.gcp_project)
        elif self.backend == "motherduck":
            import duckdb
            token = self.cfg.motherduck_token or os.getenv("MOTHERDUCK_TOKEN")
            db_name = self.cfg.motherduck_db or "default"
            self.client = duckdb.connect(f"md:/{db_name}?motherduck_token={token}")
        else:
            raise RuntimeError("Unknown SQL backend")

    def _nl_to_sql(self, message: str) -> str:
        # Minimal NL2SQL heuristic; replace with your own mapping or LLM prompt.
        # Expect users to include table names. Example: "avg revenue by month from dataset.sales"
        m = message.lower()
        if "avg" in m and " by " in m:
            return "-- Example template; edit me\nSELECT DATE_TRUNC(month, date_col) AS month, AVG(metric) AS avg_metric FROM dataset.table GROUP BY 1 ORDER BY 1;"
        # fallback: pass-through if user typed SQL explicitly
        if re.match(r"^\s*select ", m):
            return message
        return "SELECT * FROM dataset.table LIMIT 100;"

    def run(self, message: str) -> pd.DataFrame:
        sql = self._nl_to_sql(message)
        self.tracer.trace_event("sql_query", {"sql": sql, "backend": self.backend})
        if self.backend == "bigquery":
            df = self.client.query(sql).to_dataframe()
        else:
            df = self.client.execute(sql).fetch_df()
        return df