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from gradio_huggingfacehub_search import HuggingfaceHubSearch
from huggingface_hub import HfApi
import matplotlib.pyplot as plt
from typing import Tuple, Optional
import pandas as pd
import gradio as gr
import duckdb
import requests
import llama_cpp
import instructor
import spaces
import enum
from pydantic import BaseModel, Field
BASE_DATASETS_SERVER_URL = "https://datasets-server.huggingface.co"
view_name = "dataset_view"
hf_api = HfApi()
conn = duckdb.connect()
llama = llama_cpp.Llama(
model_path="Hermes-2-Pro-Llama-3-8B-Q8_0.gguf",
n_gpu_layers=-1,
chat_format="chatml",
n_ctx=2048,
verbose=False,
temperature=0.1,
)
create = instructor.patch(
create=llama.create_chat_completion_openai_v1,
mode=instructor.Mode.JSON_SCHEMA,
)
class OutputTypes(str, enum.Enum):
TABLE = "table"
BARCHART = "barchart"
LINECHART = "linechart"
class SQLResponse(BaseModel):
sql: str
visualization_type: Optional[OutputTypes] = Field(
None, description="The type of visualization to display"
)
data_key: Optional[str] = Field(
None,
description="The column name from the sql query that contains the data for chart responses",
)
label_key: Optional[str] = Field(
None,
description="The column name from the sql query that contains the labels for chart responses",
)
def get_dataset_ddl(dataset_id: str) -> str:
response = requests.get(f"{BASE_DATASETS_SERVER_URL}/parquet?dataset={dataset_id}")
response.raise_for_status() # Check if the request was successful
first_parquet = response.json().get("parquet_files", [])[0]
first_parquet_url = first_parquet.get("url")
if not first_parquet_url:
raise ValueError("No valid URL found for the first parquet file.")
conn.execute(
f"CREATE OR REPLACE VIEW {view_name} as SELECT * FROM read_parquet('{first_parquet_url}');"
)
dataset_ddl = conn.execute(f"PRAGMA table_info('{view_name}');").fetchall()
column_data_types = ",\n\t".join(
[f"{column[1]} {column[2]}" for column in dataset_ddl]
)
sql_ddl = """
CREATE TABLE {} (
{}
);
""".format(
view_name, column_data_types
)
return sql_ddl
@spaces.GPU
def generate_query(dataset_id: str, query: str) -> str:
ddl = get_dataset_ddl(dataset_id)
system_prompt = f"""
You are an expert SQL assistant with access to the following PostgreSQL Table:
```sql
{ddl}
```
Please assist the user by writing a SQL query that answers the user's question.
Use Label Key as the column name for the x-axis and Data Key as the column name for the y-axis for chart responses. The
label key and data key must be present in the SQL output.
"""
print("Calling LLM with system prompt: ", system_prompt)
resp: SQLResponse = create(
model="Hermes-2-Pro-Llama-3-8B",
messages=[
{"role": "system", "content": system_prompt},
{
"role": "user",
"content": query,
},
],
response_model=SQLResponse,
)
print("Received Response: ", resp)
return resp
def query_dataset(dataset_id: str, query: str) -> Tuple[pd.DataFrame, str, plt.Figure]:
response: SQLResponse = generate_query(dataset_id, query)
print("Querying Parquet...")
df = conn.execute(response.sql).fetchdf()
plot = None
# handle incorrect data and label keys better
if response.label_key and response.label_key not in df.columns:
response.label_key = None
if response.data_key and response.data_key not in df.columns:
response.data_key = None
if response.visualization_type == OutputTypes.LINECHART:
plot = df.plot(
kind="line", x=response.label_key, y=response.data_key
).get_figure()
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
elif response.visualization_type == OutputTypes.BARCHART:
plot = df.plot(
kind="bar", x=response.label_key, y=response.data_key
).get_figure()
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
markdown_output = f"""```sql\n{response.sql}\n```"""
return df, markdown_output, plot
with gr.Blocks() as demo:
gr.Markdown("# Query your HF Datasets with Natural Language ππ")
dataset_id = HuggingfaceHubSearch(
label="Hub Dataset ID",
placeholder="Find your favorite dataset...",
search_type="dataset",
value="gretelai/synthetic_text_to_sql",
)
user_query = gr.Textbox("", label="Ask anything...")
examples = [
["Show me a preview of the data"],
["Show me something interesting"],
["What is the largest length of sql query context?"],
["show me counts by sql_query_type in a bar chart"],
]
gr.Examples(examples=examples, inputs=[user_query], outputs=[])
btn = gr.Button("Ask πͺ")
sql_query = gr.Markdown(label="Output SQL Query")
df = gr.DataFrame()
plot = gr.Plot()
btn.click(
query_dataset,
inputs=[dataset_id, user_query],
outputs=[df, sql_query, plot],
)
if __name__ == "__main__":
demo.launch()
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