app.py

import os
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
"""
hf_token = os.getenv("user_token")
client = InferenceClient("Qwen/Qwen2.5-Coder-3B-Instruct", token=hf_token)


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="""Your task is to convert a question into a SQL query, given a Postgres database schema.
Adhere to these rules:
- Use Table Aliases to prevent ambiguity.
- When creating a ratio, always cast the numerator as float.
- Only use tables and fields explicitly defined in the provided schema. Do not include or reference any tables or fields not listed.
- If the required query cannot be constructed using the provided schema, return the string `NOT FOUND`.

### Input:
Generate a SQL query that answers the question `{question}`.
This query will run on a database whose schema is represented in this string:
CREATE TABLE products (
product_id INTEGER PRIMARY KEY, -- Unique ID for each product
name VARCHAR(50), -- Name of the product
price DECIMAL(10,2), -- Price of each unit of the product
quantity INTEGER  -- Current quantity in stock
);

CREATE TABLE customers (
customer_id INTEGER PRIMARY KEY, -- Unique ID for each customer
name VARCHAR(50), -- Name of the customer
address VARCHAR(100) -- Mailing address of the customer
);

CREATE TABLE salespeople (
salesperson_id INTEGER PRIMARY KEY, -- Unique ID for each salesperson
name VARCHAR(50), -- Name of the salesperson
region VARCHAR(50) -- Geographic sales region
);

CREATE TABLE sales (
sale_id INTEGER PRIMARY KEY, -- Unique ID for each sale
product_id INTEGER, -- ID of product sold
customer_id INTEGER,  -- ID of customer who made purchase
salesperson_id INTEGER, -- ID of salesperson who made the sale
sale_date DATE, -- Date the sale occurred
quantity INTEGER -- Quantity of product sold
);

CREATE TABLE product_suppliers (
supplier_id INTEGER PRIMARY KEY, -- Unique ID for each supplier
product_id INTEGER, -- Product ID supplied
supply_price DECIMAL(10,2) -- Unit price charged by supplier
);

-- sales.product_id can be joined with products.product_id
-- sales.customer_id can be joined with customers.customer_id
-- sales.salesperson_id can be joined with salespeople.salesperson_id
-- product_suppliers.product_id can be joined with products.product_id

### Response:
```sql""", 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__":
    demo.launch()