import io
import logging
import os
import shutil
import sys
import tempfile
import uuid
from pathlib import Path
from typing import List, Tuple

import duckdb
import gradio as gr
import pandas as pd
import pytest
import requests
from dotenv import load_dotenv

from src.client import LLMChain
from src.pipelines import Query2Schema

load_dotenv()


LEVEL = "INFO" if not os.getenv("ENV") == "PROD" else "WARNING"
logging.basicConfig(
    level=getattr(logging, LEVEL, logging.INFO),
    format="%(asctime)s %(levelname)s %(name)s: %(message)s",
)
logger = logging.getLogger(__name__)

if not Path("/tmp").exists():
    os.mkdir("/tmp")


def download_file(url: str, save_path: str):
    if Path(save_path).exists():
        print(f"File already exists at {save_path}. Skipping download.")
        return duckdb.connect(database=save_path)

    try:
        response = requests.get(url, stream=True)
        response.raise_for_status()

        with open(save_path, "wb") as out_file:
            shutil.copyfileobj(response.raw, out_file)
        return duckdb.connect(database=save_path)
    except Exception as e:
        logger.info(f"Error Downloding Chinook DB: {e}")
        raise


conn = download_file(
    url="https://raw.githubusercontent.com/RandomFractals/duckdb-sql-tools/main/data/chinook/duckdb/chinook.duckdb",
    save_path="database/chinook.duckdb",
)
pipe = Query2Schema(duckdb=conn, chain=LLMChain())


def get_test_databases() -> List[str]:
    """Scans the 'tests' directory for subdirectories (representing databases)."""

    return ["All", "chinook", "Northwind"]


def get_tables_names(schema_name):
    tables = conn.execute("SELECT table_name FROM information_schema.tables").fetchall()
    return [table[0] for table in tables]


def update_table_names(schema_name):
    tables = get_tables_names(schema_name)
    return gr.update(choices=tables, value=tables[0] if tables else None)


def update_column_names(table_name):
    columns = conn.execute(
        f"SELECT column_name FROM information_schema.columns WHERE table_name = '{table_name}' "
    ).fetchall()
    columns = [column[0] for column in columns]
    df = pd.DataFrame(columns, columns=["Column Names"])
    # return gr.update(
    #     choices=columns,
    #     value=columns[0] if columns else None
    # )
    return df


def get_ddl(table: str) -> str:
    result = conn.sql(
        f"SELECT sql, database_name, schema_name FROM duckdb_tables() where table_name ='{table}';"
    ).df()
    ddl_create = result.iloc[0, 0]
    parent_database = result.iloc[0, 1]
    schema_name = result.iloc[0, 2]
    full_path = f"{parent_database}.{schema_name}.{table}"
    if schema_name != "main":
        old_path = f"{schema_name}.{table}"
    else:
        old_path = table
    ddl_create = ddl_create.replace(old_path, full_path)
    return ddl_create


def run_pipeline(table: str, query_input: str) -> Tuple[str, pd.DataFrame]:
    try:
        schema = get_ddl(table=table)
    except Exception as e:
        logger.error(f"Failed to fetch DDL for table {table}: {e}")
        raise
    try:
        sql, df = pipe.try_sql_with_retries(
            user_question=query_input,
            context=schema,
        )
        sql = sql.get("sql_query") if isinstance(sql, dict) else sql
        if not sql:
            raise ValueError("SQL generation returned None")
        return sql, df
    except Exception as e:
        logger.error(f"Error generating SQL for table {table}: {e}")
        raise


def create_mesh_model(sql: str, db_name: str = "chinook") -> Tuple[str, str, str]:
    model_name = f"model_{uuid.uuid4().hex[:8]}"

    # Use catalog.schema.model_name format
    full_model_name = f"{db_name}.{model_name}"

    MODEL_HEADER = f"""MODEL (
name {full_model_name},
kind FULL
);
    """
    try:
        model_dir = Path("models/")
        model_dir.mkdir(parents=True, exist_ok=True)

        model_path = model_dir / f"{model_name}.sql"
        model_text = MODEL_HEADER + "\n" + sql.replace("chinook.main.", "")
        model_path.write_text(model_text)

        return model_text, str(model_path), full_model_name
    except Exception as e:
        logger.error(f"Error creating SQL Mesh model: {e}")
        raise


def create_pandera_schema(
    sql: str, user_instruction: str, model_name: str
) -> Tuple[str, str]:
    SCRIPT_HEADER = """
import pandas as pd
import pandera.pandas as pa
from pandera.typing import *

import pytest
from sqlmesh import Context
from datetime import date
from pathlib import Path
import shutil
import duckdb

    """

    MESH_STR = f"""
@pytest.fixture(scope="session")
def mesh_context():

    context = Context(paths=".", gateway="duckdb", load=True)
    yield context

@pytest.fixture
def today_str():
    return date.today().isoformat()

def test_back_fill(mesh_context, today_str):
    mesh_context.plan(skip_backfill=False, auto_apply=True)
    mesh_context.run(start=today_str, end=today_str)

    # df = mesh_context.fetchdf("SELECT * FROM {model_name} LIMIT 10")
    # assert not df.empty
    """
    try:
        schema = pipe.generate_pandera_schema(
            sql_query=sql, user_instruction=user_instruction
        )
        test_schema = f"""
        
def test_schema(mesh_context, today_str):    
    df = mesh_context.evaluate(
        "{model_name}",
        start=today_str,
        end=today_str,
        execution_time=today_str,
    )
    {schema.split()[1].split("(")[0].strip()}.validate(df)
    """
        print(schema)

        with tempfile.NamedTemporaryFile(
            mode="w",
            prefix="test_",
            suffix=".py",
            delete=False,
            encoding="utf-8",
        ) as f:
            f.write(SCRIPT_HEADER)
            f.write("\n\n")
            f.write(schema)
            f.write("\n\n")
            f.write(MESH_STR)
            f.write("\n\n")
            f.write(test_schema)

        file_path = Path(f.name)

        return schema, str(file_path)
    except Exception as e:
        logger.error(f"Error creating Pandera schema: {e}")
        raise


def create_test_file(
    table_name: str, db_name: str, sql_instruction: str, user_instruction: str
) -> Tuple[str, str, pd.DataFrame, str, str]:
    try:
        sql, df = run_pipeline(table=table_name, query_input=sql_instruction)
        model_text, model_file, model_name = create_mesh_model(sql=sql, db_name=db_name)
        schema, test_file = create_pandera_schema(
            sql=sql,
            user_instruction=user_instruction,
            model_name=model_name,
        )
        return test_file, model_file, df, model_text, schema
    except Exception as e:
        logger.error(f"Error creating test file for table {table_name}: {e}")
        raise


def run_tests(
    table_name: str, db_name: str, sql_instruction: str, user_instruction: str
):
    test_file, model_file, df, model_text, schema = create_test_file(
        table_name=table_name,
        db_name=db_name,
        sql_instruction=sql_instruction,
        user_instruction=user_instruction,
    )

    capture_out = io.StringIO()
    capture_err = io.StringIO()

    old_out = sys.stdout
    old_err = sys.stderr

    sys.stdout = capture_out
    sys.stderr = capture_err

    try:
        retcode = pytest.main(
            [
                test_file,
                "-s",
                "--tb=short",
                "--disable-warnings",
                "-o",
                "cache_dir=/tmp",
            ]
        )
    except Exception as e:
        sys.stdout = old_out
        sys.stderr = old_err
        return f"Error running tests: {str(e)}", ""

    sys.stdout = old_out
    sys.stderr = old_err

    output = capture_out.getvalue() + "\n" + capture_err.getvalue()

    for f in [test_file, model_file]:
        try:
            os.remove(f)
        except FileNotFoundError:
            pass

    return output, df, model_text, schema


custom_css = """
/* --- Overall container --- */
.gradio-container {
    background-color: #f0f4f8; /* light background */
    font-family: 'Arial', sans-serif;
}

/* --- Logo --- */
.logo {
    max-width: 200px;
    margin: 20px auto;
    display: block;
}

/* --- Buttons --- */
.gr-button {
    background-color: #4a90e2 !important;  /* primary color */
    font-size: 14px;                        /* fixed font size */
    padding: 6px 12px !important;           /* fixed padding */
    height: 36px !important;                /* fixed height */
    min-width: 120px !important;            /* fixed width */
}
.gr-button:hover {
    background-color: #3a7bc8 !important;
}

/* --- Logs Textbox --- */
#logs textarea {
    overflow-y: scroll;   
    resize: none;         
    height: 400px;        
    width: 100%;          
    font-family: monospace; 
    font-size: 13px;
    line-height: 1.4;
}

/* Optional: small spacing between rows */
.gr-row {
    gap: 10px;
}
"""

with gr.Blocks(
    theme=gr.themes.Soft(primary_hue="purple", secondary_hue="indigo"), css=custom_css
) as demo:
    gr.Image("logo.png", label=None, show_label=False, container=False, height=100)

    gr.Markdown(
        """
    <div style='text-align: center;'>
    <strong style='font-size: 36px;'>SQL Test Suite</strong>
    <br>
    <span style='font-size: 20px;'>Automated testing and schema validation for SQL models with LLM.</span>
    </div>
    """
    )

    with gr.Row():
        with gr.Column(scale=1):
            schema_dropdown = gr.Dropdown(
                choices=["chinook", "northwind"],
                value="chinook",
                label="Select Schema",
                interactive=True,
            )
            tables_dropdown = gr.Dropdown(
                choices=[], label="Available Tables", value=None, interactive=True
            )
            # columns_dropdown = gr.Dropdown(choices=[], label="Available Columns", value=None, interactive=True)
            columns_df = gr.DataFrame(label="Columns", value=[], interactive=False)
            # with gr.Row():
            #     generate_result = gr.Button("Run Tests", variant="primary")

        with gr.Column(scale=3):
            with gr.Row():
                sql_instruction = gr.Textbox(
                    lines=3,
                    label="Business Metric Query (Plain English)",
                    placeholder=(
                        "Describe the business question you want to answer.\n"
                        "Example: 'Show me the average sales per month.'\n"
                        "Example: 'Total revenue by product category for last year.'"
                    ),
                )
            with gr.Row():
                user_instruction = gr.Textbox(
                    lines=5,
                    label="Define Data Quality Level",
                    placeholder=(
                        "Describe the validation rule and how strict it should be.\n"
                        "Example: Validate that the incident_zip column contains valid 5-digit ZIP codes.\n"
                    ),
                )
            with gr.Row():
                with gr.Column(scale=7):
                    pass
                with gr.Column(scale=1):
                    run_tests_btn = gr.Button("▶ Run Tests", variant="primary")

            with gr.Row():
                with gr.Column():
                    with gr.Tabs():
                        with gr.Tab("Test Logs"):
                            with gr.Row():
                                with gr.Column():
                                    test_logs = gr.Textbox(
                                        label="Test Logs",
                                        lines=20,
                                        max_lines=20,
                                        interactive=False,
                                        elem_id="logs",
                                    )

                        with gr.Tab("SQL Model"):
                            with gr.Row():
                                with gr.Column():
                                    sql_model = gr.Textbox(
                                        label="SQL Model",
                                        lines=20,
                                        max_lines=20,
                                        interactive=False,
                                        elem_id="sql_model",
                                    )

                        with gr.Tab("Schema"):
                            with gr.Row():
                                with gr.Column():
                                    result_schema = gr.Textbox(
                                        label="Validation Schema",
                                        lines=20,
                                        max_lines=20,
                                        interactive=False,
                                    )

                        with gr.Tab("Data"):
                            with gr.Row():
                                with gr.Column():
                                    result_data = gr.DataFrame(
                                        label="Query Result",
                                        value=[],
                                        interactive=False,
                                    )

        schema_dropdown.change(
            update_table_names, inputs=schema_dropdown, outputs=tables_dropdown
        )
        tables_dropdown.change(
            update_column_names, inputs=tables_dropdown, outputs=columns_df
        )
        demo.load(
            fn=update_table_names, inputs=schema_dropdown, outputs=tables_dropdown
        )
        run_tests_btn.click(
            run_tests,
            inputs=[
                tables_dropdown,
                schema_dropdown,
                sql_instruction,
                user_instruction,
            ],
            outputs=[test_logs, result_data, sql_model, result_schema],
        )

if __name__ == "__main__":
    demo.launch(debug=True)