import gradio as gr
from huggingface_hub import InferenceClient

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    from sqlalchemy import (
        create_engine,
        MetaData,
        Table,
        Column,
        String,
        Integer,
        Float,
        insert,
        inspect,
        text,
    )
    
    engine = create_engine("sqlite:///:memory:")
    metadata_obj = MetaData()
    
    def insert_rows_into_table(rows, table, engine=engine):
        for row in rows:
            stmt = insert(table).values(**row)
            with engine.begin() as connection:
                connection.execute(stmt)
    
    table_name = "receipts"
    receipts = Table(
        table_name,
        metadata_obj,
        Column("receipt_id", Integer, primary_key=True),
        Column("customer_name", String(16), primary_key=True),
        Column("price", Float),
        Column("tip", Float),
    )
    metadata_obj.create_all(engine)
    
    rows = [
        {"receipt_id": 1, "customer_name": "Alan Payne", "price": 12.06, "tip": 1.20},
        {"receipt_id": 2, "customer_name": "Alex Mason", "price": 23.86, "tip": 0.24},
        {"receipt_id": 3, "customer_name": "Woodrow Wilson", "price": 53.43, "tip": 5.43},
        {"receipt_id": 4, "customer_name": "Margaret James", "price": 21.11, "tip": 1.00},
    ]
    insert_rows_into_table(rows, receipts)
    demo.launch()