import json
import logging
import os
import urllib.parse
from typing import Any

import gradio as gr
import requests
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from huggingface_hub.repocard import CardData, RepoCard


logger = logging.getLogger(__name__)
example = HuggingfaceHubSearch().example_value()


SYSTEM_PROMPT_TEMPLATE = (
    "You are a SQL query expert assistant that returns a DuckDB SQL queries "
    "based on the user's natural language query and dataset features. "
    "You might need to use DuckDB functions for lists and aggregations, "
    "given the features. Only return the SQL query, no other text. The "
    "user may ask you to make various adjustments to the query. Every "
    "time your response should only include the refined SQL query and "
    "nothing else.\n\n"
    "The table being queried is named: {table_name}.\n\n"
    "# Features\n"
    "{features}"
)


def get_iframe(hub_repo_id, sql_query=None):
    if not hub_repo_id:
        raise ValueError("Hub repo id is required")
    if sql_query:
        sql_query = urllib.parse.quote(sql_query)
        url = f"https://huggingface.co/datasets/{hub_repo_id}/embed/viewer?sql_console=true&sql={sql_query}"
    else:
        url = f"https://huggingface.co/datasets/{hub_repo_id}/embed/viewer"
    iframe = f"""
    <iframe
  src="{url}"
  frameborder="0"
  width="100%"
  height="800px"
></iframe>
"""
    return iframe


def get_table_info(hub_repo_id):
    url: str = f"https://datasets-server.huggingface.co/info?dataset={hub_repo_id}"
    response = requests.get(url)
    try:
        data = response.json()
        data = data.get("dataset_info")
        return json.dumps(data)
    except Exception as e:
        gr.Error(f"Error getting column info: {e}")


def get_table_name(
    config: str | None,
    split: str | None,
    config_choices: list[str],
    split_choices: list[str],
):
    if len(config_choices) > 0 and config is None:
        config = config_choices[0]
    if len(split_choices) > 0 and split is None:
        split = split_choices[0]

    if len(config_choices) > 1 and len(split_choices) > 1:
        base_name = f"{config}_{split}"
    elif len(config_choices) >= 1 and len(split_choices) <= 1:
        base_name = config
    else:
        base_name = split

    def replace_char(c):
        if c.isalnum():
            return c
        if c in ["-", "_", "/"]:
            return "_"
        return ""

    table_name = "".join(replace_char(c) for c in base_name)
    if table_name[0].isdigit():
        table_name = f"_{table_name}"
    return table_name.lower()


def get_system_prompt(
    card_data: dict[str, Any],
    config: str | None,
    split: str | None,
):
    config_choices = get_config_choices(card_data)
    split_choices = get_split_choices(card_data)

    table_name = get_table_name(config, split, config_choices, split_choices)
    features = card_data[config]["features"]
    return SYSTEM_PROMPT_TEMPLATE.format(
        table_name=table_name,
        features=features,
    )


def get_config_choices(card_data: dict[str, Any]) -> list[str]:
    return list(card_data.keys())


def get_split_choices(card_data: dict[str, Any]) -> list[str]:
    splits = set()
    for config in card_data.values():
        splits.update(config.get("splits", {}).keys())

    return list(splits)


def query_dataset(hub_repo_id, card_data, query, config, split, history):
    if card_data is None or len(card_data) == 0:
        return "", get_iframe(hub_repo_id), []
    card_data = json.loads(card_data)
    system_prompt = get_system_prompt(card_data, config, split)
    messages = [{"role": "system", "content": system_prompt}]
    for turn in history:
        user, assistant = turn
        messages.append(
            {
                "role": "user",
                "content": user,
            }
        )
        messages.append(
            {
                "role": "assistant",
                "content": assistant,
            }
        )
    messages.append(
        {
            "role": "user",
            "content": query,
        }
    )
    api_key = os.environ["API_KEY_TOGETHER_AI"].strip()
    response = requests.post(
        "https://api.together.xyz/v1/chat/completions",
        json=dict(
            model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
            messages=messages,
            max_tokens=1000,
        ),
        headers={"Authorization": f"Bearer {api_key}"},
    )

    if response.status_code != 200:
        logger.warning(response.text)

    try:
        response.raise_for_status()
    except Exception as e:
        gr.Error(f"Could not query LLM for suggestion: {e}")

    response_dict = response.json()
    duck_query = response_dict["choices"][0]["message"]["content"]
    duck_query = _sanitize_duck_query(duck_query)
    history.append((query, duck_query))
    return duck_query, get_iframe(hub_repo_id, duck_query), history


def _sanitize_duck_query(duck_query: str) -> str:
    # Sometimes the LLM wraps the query like this:
    # ```sql
    # select * from x;
    # ```
    # This removes that wrapping if present.
    if "```" not in duck_query:
        return duck_query
    start_idx = duck_query.index("```") + len("```")
    end_idx = duck_query.rindex("```")
    duck_query = duck_query[start_idx:end_idx]
    if duck_query.startswith("sql\n"):
        duck_query = duck_query.replace("sql\n", "", 1)
    return duck_query


with gr.Blocks() as demo:
    gr.Markdown("""# 🐥 🦙 🤗 Text To SQL Hub Datasets 🤗 🦙 🐥

                This is a basic text to SQL tool that allows you to query datasets on Huggingface Hub.
                It is built with [DuckDB](https://duckdb.org/), [Huggingface's Inference API](https://huggingface.co/docs/api-inference/index), and [LLama 3.1 70B](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct).
                Also, it uses the [dataset-server API](https://redocly.github.io/redoc/?url=https://datasets-server.huggingface.co/openapi.json#operation/isValidDataset).
                """)
    with gr.Row():
        search_in = HuggingfaceHubSearch(
            label="Search Huggingface Hub",
            placeholder="Search for models on Huggingface",
            search_type="dataset",
            sumbit_on_select=True,
        )
    with gr.Row():
        show_btn = gr.Button("Show Dataset")
    with gr.Row():
        sql_out = gr.Code(
            label="DuckDB SQL Query",
            interactive=True,
            language="sql",
            lines=1,
            visible=False,
        )
    with gr.Row():
        card_data = gr.Code(label="Card data", language="json", visible=False)

    @gr.render(inputs=[card_data])
    def show_config_split_choices(data):
        try:
            data = json.loads(data.strip())
            config_choices = get_config_choices(data)
            split_choices = get_split_choices(data)
        except Exception:
            config_choices = []
            split_choices = []

        initial_config = config_choices[0] if len(config_choices) > 0 else None
        initial_split = split_choices[0] if len(split_choices) > 0 else None
        with gr.Row():
            with gr.Column():
                config_selection = gr.Dropdown(
                    label="Config Name", choices=config_choices, value=initial_config
                )
            with gr.Column():
                split_selection = gr.Dropdown(
                    label="Split Name", choices=split_choices, value=initial_split
                )

        with gr.Accordion("Query Suggestion History.", open=False) as accordion:
            chatbot = gr.Chatbot(height=200, layout="bubble")
        with gr.Row():
            query = gr.Textbox(
                label="Query Description",
                placeholder="Enter a natural language query to generate SQL",
            )
        with gr.Row():
            with gr.Column():
                query_btn = gr.Button("Get Suggested Query")
            with gr.Column():
                clear = gr.ClearButton([query, chatbot], value="Reset Query History")
        with gr.Row():
            search_out = gr.HTML(label="Search Results")
        gr.on(
            [show_btn.click, search_in.submit],
            fn=get_iframe,
            inputs=[search_in],
            outputs=[search_out],
        ).then(
            fn=get_table_info,
            inputs=[search_in],
            outputs=[card_data],
        )
        gr.on(
            [query_btn.click, query.submit],
            fn=query_dataset,
            inputs=[
                search_in,
                card_data,
                query,
                config_selection,
                split_selection,
                chatbot,
            ],
            outputs=[sql_out, search_out, chatbot],
        )
        gr.on([query_btn.click], fn=lambda: gr.update(open=True), outputs=[accordion])


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