app.py

import os
import duckdb
import gradio as gr
from dotenv import load_dotenv
from httpx import Client
from huggingface_hub import HfApi
from huggingface_hub.utils import logging
from llama_cpp import Llama
import pandas as pd
from transformers import pipeline

load_dotenv()

HF_TOKEN = os.getenv("HF_TOKEN")
assert HF_TOKEN is not None, "You need to set HF_TOKEN in your environment variables"


BASE_DATASETS_SERVER_URL = "https://datasets-server.huggingface.co"
API_URL = "https://m82etjwvhoptr3t5.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
	"Accept" : "application/json",
	"Authorization": f"Bearer {HF_TOKEN}",
	"Content-Type": "application/json" 
}

logger = logging.get_logger(__name__)
client = Client(headers=headers)
api = HfApi(token=HF_TOKEN)

print("About to load DuckDB-NSQL-7B model")
"""
llama = Llama(
        model_path="DuckDB-NSQL-7B-v0.1-q8_0.gguf",
        n_ctx=2048,
)
"""
pipe = pipeline("text-generation", model="motherduckdb/DuckDB-NSQL-7B-v0.1")
print("DuckDB-NSQL-7B model has been loaded")

def get_first_parquet(dataset: str):
    resp = client.get(f"{BASE_DATASETS_SERVER_URL}/parquet?dataset={dataset}")
    return resp.json()["parquet_files"][0]


def query_remote_model(text):
    payload = {
        "inputs": text,
        "parameters": {}
    }
    response = client.post(API_URL, headers=headers, json=payload)
    pred = response.json()
    return pred[0]["generated_text"]

def query_local_model_transformers(text):
    pred = pipe(text)
    print(type(pred))
    print(pred)
    return pred
    #pred = llama(text, temperature=0.1, max_tokens=500)
    #return pred["choices"][0]["text"]


def query_local_model(text):
    pred = llama(text, temperature=0.1, max_tokens=500)
    return pred["choices"][0]["text"]


def text2sql(dataset_name, query_input):
    print(f"start text2sql for {dataset_name}")
    try:
        first_parquet = get_first_parquet(dataset_name)
    except Exception as e:
        return f"❌ Dataset does not exist or is not supported {e}"
    first_parquet_url = first_parquet["url"]
    print(first_parquet_url)
    con = duckdb.connect()
    con.execute("INSTALL 'httpfs'; LOAD httpfs;")
    # could get from parquet instead?
    con.execute(f"CREATE TABLE data as SELECT * FROM '{first_parquet_url}' LIMIT 1;")
    result = con.sql("SELECT sql FROM duckdb_tables() where table_name ='data';").df()

    ddl_create = result.iloc[0,0]
    text = f"""### Instruction:
    Your task is to generate valid duckdb SQL to answer the following question.

    ### Input:
    Here is the database schema that the SQL query will run on:
    {ddl_create}
    ### Question:
    {query_input}

    ### Response (use duckdb shorthand if possible) replace table name with {first_parquet_url} in the generated sql query:
    """
    
    print(text)
    
    # sql_output =  query_remote_model(text)

    sql_output = query_local_model_transformers(text)

    try:
        query_result = con.sql(sql_output).df()
    except Exception as error:
        query_result = pd.DataFrame([{"error": f"❌ Could not execute SQL query {error=}"}])
    finally:
        con.close()
    return {
        query_output:sql_output,
        df:query_result
    }


with gr.Blocks() as demo:
    gr.Markdown("# Talk to your dataset")
    gr.Markdown("This space shows how to talk to your datasets: Get a brief description, create SQL queries, and get results.")
    gr.Markdown("Generate SQL queries'")
    dataset_name = gr.Textbox("sksayril/medicine-info", label="Dataset Name")
    query_input = gr.Textbox("How many rows there are?", label="Ask something about your data")
    btn = gr.Button("Generate SQL")
    query_output = gr.Textbox(label="Output SQL", interactive= False)    
    df = gr.DataFrame(datatype="markdown")
    btn.click(text2sql, inputs=[dataset_name, query_input], outputs=[query_output,df])
demo.launch(debug=True)