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import pandas as pd
from openai import OpenAI
import os
from google.cloud import bigquery
import numpy as np
import gradio as gr
project_id = os.getenv('project_id')
dataset_id = os.getenv('dataset_id')
table_id = os.getenv('table_id')
openai_client = OpenAI()
def fetch_table_schema(project_id, dataset_id, table_id):
bqclient = bigquery.Client(project=project_id)
table_ref = f"{project_id}.{dataset_id}.{table_id}"
table = bqclient.get_table(table_ref)
schema_dict = {}
for schema_field in table.schema:
schema_dict[schema_field.name] = schema_field.field_type
return schema_dict
def get_sql_query(description):
prompt = f'''
Generate the SQL query for the following task:\n{description}.\n
The database you need is called {dataset_id} and the table is called {table_id}.
Use the format {dataset_id}.{table_id} as the table name in the queries.
Enclose column names in backticks(`) not quotation marks.
Do not assign aliases to the columns.
Do not calculate new columns, unless specifically called to.
Return only the SQL query, nothing else.
Do not use WITHIN GROUP clause.
\nThe list of all the columns is as follows: {schema} /n
'''
try:
completion = openai_client.chat.completions.create(
model='gpt-4o',
messages = [
{"role": "system", "content": "You are an expert Data Scientist with in-depth knowledge of SQL, working on Network Telemetry Data."},
{"role": "user", "content": f'{prompt}'},
]
)
sql_query = completion.choices[0].message.content.strip().split('```sql')[1].split('```')[0]
except Exception as e:
print(f'The following error ocurred: {e}\n')
sql_query = None
return sql_query
schema = fetch_table_schema(project_id, dataset_id, table_id)
def execute_sql_query(query):
client = bigquery.Client()
try:
result = client.query(query).to_dataframe()
message = f'The query:{query} was successfully executed.'
except Exception as e:
result = None
message = f'The query:{query} could not be executed due to the following exception:\n{e}'
return result, message
def echo(text):
query = get_sql_query(text)
if query is None:
return 'No query generated', 'No query generated'
result, message = execute_sql_query(query)
return result, message
def gradio_interface(text):
result, message = echo(text)
if isinstance(result, pd.DataFrame):
return gr.Dataframe(value=result), message
else:
return result, message
def gradio_interface(text):
result, message = echo(text)
if isinstance(result, pd.DataFrame):
return gr.Dataframe(value=result), message
else:
return result, message
demo = gr.Blocks(
title="Text-to-SQL",
theme='remilia/ghostly',
)
with demo:
gr.Markdown(
'''
# <p style="text-align: center;">Text to SQL Query Engine</p>
<p style="text-align: center;">
Welcome to our Text2SQL Engine.
<br>
Enter your query in natural language and we'll convert it to SQL and return the result to you.
</p>
'''
)
with gr.Row():
with gr.Column(scale=1):
text_input = gr.Textbox(label="Enter your query")
button = gr.Button("Submit")
gr.Examples([
'Find the correlation between RTT and Jitter for each Market',
'Find the variance in Jitter for each 5G_Reliability_Category',
'Find the count of records per 5G_Reliability_Category where 5G_Reliability_Value is below the average for the category',
'Calculate the standard deviation of 5G_Reliability_Score for each Network_Engineer',
'Determine the Sector with the highest variance in 5G Reliability Value and its corresponding average Context Drop Percent'
],
inputs=[text_input]
)
with gr.Column(scale=3):
output_text = gr.Textbox(label="Output", interactive=False)
output_df = gr.Dataframe(interactive=False)
def update_output(text):
result, message = gradio_interface(text)
if result and isinstance(result, pd.DataFrame):
return result, message, gr.update(visible=True)
else:
return result, message, gr.update(visible=False)
button.click(update_output, inputs=text_input, outputs=[output_df, output_text])
demo.launch(debug=True, auth=("admin", "Text2SQL")) |