Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,007 Bytes
27e0148 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
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
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)
llama = Llama(
model_path="DuckDB-NSQL-7B-v0.1-q8_0.gguf",
n_ctx=2048,
)
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(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;")
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()
con.close()
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):
"""
print(text)
# sql_output = query_remote_model(text)
sql_output = query_local_model(text)
return sql_output
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)
btn.click(text2sql, inputs=[dataset_name, query_input], outputs=query_output)
demo.launch()
|