app.py

import gradio as gr
from huggingface_hub import InferenceClient
import os
from smolagents import (
    tool,
    CodeAgent,
    HfApiModel,
    GradioUI,
    MultiStepAgent,
    stream_to_gradio,
)
from sqlalchemy import (
    create_engine,
    MetaData,
    Table,
    Column,
    String,
    Integer,
    Float,
    insert,
    inspect,
    text,
    select,
    Engine,
)
import spaces

from dotenv import load_dotenv

load_dotenv()

# What is the average each customer paid?
#  Create a sql statement and invoke your sql_engine tool


@spaces.GPU
def dummy():
    pass


@tool
def sql_engine_tool(query: str) -> str:
    """
    Allows you to perform SQL queries on the table. Returns a string representation of the result.
    The table is named 'receipts'. Its description is as follows:
        Columns:
        - receipt_id: INTEGER
        - customer_name: VARCHAR(16)
        - price: FLOAT
        - tip: FLOAT

    Args:
        query: The query to perform. This should be correct SQL.
    """
    output = ""
    print("debug sql_engine_tool")
    print(engine)
    with engine.begin() as con:
        print(con.connection)
        print(metadata_objects.tables.keys())
        result = con.execute(
            text(
                "SELECT name FROM sqlite_master WHERE type='table' AND name='receipts'"
            )
        )
        print("tables available:", result.fetchone())

        rows = con.execute(text(query))
        for row in rows:
            output += "\n" + str(row)
    return output


def init_db(engine):

    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)
    with engine.begin() as conn:
        print("SELECT test", conn.execute(text("SELECT * FROM receipts")).fetchall())
    print("init_db debug")
    print(engine)
    print()
    return engine, metadata_obj


if __name__ == "__main__":
    engine = create_engine("sqlite:///:memory:")
    engine, metadata_objects = init_db(engine)
    model = HfApiModel(
        model_id="meta-llama/Meta-Llama-3.1-8B-Instruct",
        token=os.getenv("my_first_agents_hf_tokens"),
    )

    agent = CodeAgent(
        tools=[sql_engine_tool],
        model=model,
        max_steps=1,
        verbosity_level=1,
        use_e2b_executor=True,  # TODO Remove if I am at the stage of doing multiagents
    )
    # agent.run("What is the average each customer paid?")
    # GradioUI(agent).launch()
    print(
        "xxxxxxxxxxxxxxxxxxxxxxxxxxxxx test:", sql_engine_tool("SELECT * FROM receipts")
    )

    def enter_message(message, chat_history):
        print()
        print("enter_message debug")
        print("engine", engine)
        print("message:", message)
        print("chat_history", chat_history)
        chat_history.append({"role": "user", "content": message})
        x = stream_to_gradio(agent, message)
        print(type(x))
        print("\n\n\n", x, "\n\n\n")
        return "", x

    with gr.Blocks() as b:
        chatbot = gr.Chatbot(type="messages")
        input = gr.Textbox()
        button = gr.Button("reply")
        button.click(enter_message, [input, chatbot], [input, chatbot])
    b.launch()