CSVAgent / app.py
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import os
import gradio as gr
from sqlalchemy import text
from smolagents import tool, CodeAgent, HfApiModel
import spaces
import pandas as pd
from database import (
engine,
create_dynamic_table,
clear_database,
insert_rows_into_table,
get_table_schema
)
def process_sql_file(file_path):
"""
Process an SQL file and execute its contents.
"""
try:
# Read the SQL file
with open(file_path, 'r') as file:
sql_content = file.read()
# Split into individual statements
statements = sql_content.split(';')
# Clear existing database
clear_database()
# Execute each statement
with engine.begin() as conn:
for statement in statements:
if statement.strip(): # Skip empty statements
conn.execute(text(statement))
return True, "SQL file successfully executed! Click 'Continue' to proceed to query interface..."
except Exception as e:
return False, f"Error processing SQL file: {str(e)}"
def process_csv_file(file_path):
"""
Process a CSV file and load it into the database.
"""
try:
# Read the CSV file
df = pd.read_csv(file_path)
if len(df.columns) == 0:
return False, "Error: File contains no columns"
# Clear existing database and create new table
clear_database()
table = create_dynamic_table(df)
# Convert DataFrame to list of dictionaries and insert
records = df.to_dict('records')
insert_rows_into_table(records, table)
return True, "CSV file successfully loaded! Click 'Continue' to proceed to query interface..."
except Exception as e:
return False, f"Error processing CSV file: {str(e)}"
def process_uploaded_file(file):
"""
Process the uploaded file (either SQL or CSV).
"""
try:
if file is None:
return False, "Please upload a file."
# Get file extension
file_ext = os.path.splitext(file)[1].lower()
if file_ext == '.sql':
return process_sql_file(file)
elif file_ext == '.csv':
return process_csv_file(file)
else:
return False, "Error: Unsupported file type. Please upload either a .sql or .csv file."
except Exception as e:
return False, f"Error processing file: {str(e)}"
def get_data_table():
"""
Fetches all data from the current table and returns it as a Pandas DataFrame.
"""
try:
# Get list of tables
with engine.connect() as con:
tables = con.execute(text(
"SELECT name FROM sqlite_master WHERE type='table' AND name NOT LIKE 'sqlite_%'"
)).fetchall()
if not tables:
return pd.DataFrame()
# Use the first table found
table_name = tables[0][0]
with engine.connect() as con:
result = con.execute(text(f"SELECT * FROM {table_name}"))
rows = result.fetchall()
if not rows:
return pd.DataFrame()
columns = result.keys()
df = pd.DataFrame(rows, columns=columns)
return df
except Exception as e:
return pd.DataFrame({"Error": [str(e)]})
@tool
def sql_engine(query: str) -> str:
"""
Executes an SQL query and returns formatted results.
Args:
query: The SQL query string to execute on the database. Must be a valid SELECT query.
Returns:
str: The formatted query results as a string.
"""
try:
with engine.connect() as con:
rows = con.execute(text(query)).fetchall()
if not rows:
return "No results found."
if len(rows) == 1 and len(rows[0]) == 1:
return str(rows[0][0])
return "\n".join([", ".join(map(str, row)) for row in rows])
except Exception as e:
return f"Error: {str(e)}"
agent = CodeAgent(
tools=[sql_engine],
model=HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct"),
)
def query_sql(user_query: str) -> str:
"""
Converts natural language input to an SQL query using CodeAgent.
"""
schema = get_table_schema()
if not schema:
return "Error: No data table exists. Please upload a file first."
schema_info = (
f"The database has the following schema:\n"
f"{schema}\n"
"Generate a valid SQL SELECT query using ONLY these column names.\n"
"DO NOT explain your reasoning, and DO NOT return anything other than the SQL query itself."
)
generated_sql = agent.run(f"{schema_info} Convert this request into SQL: {user_query}")
if not isinstance(generated_sql, str):
return f"{generated_sql}"
if not generated_sql.strip().lower().startswith(("select", "show", "pragma")):
return "Error: Only SELECT queries are allowed."
result = sql_engine(generated_sql)
try:
float_result = float(result)
return f"{float_result:.2f}"
except ValueError:
return result
# Create the Gradio interface
with gr.Blocks() as demo:
current_state = gr.State("upload")
# Upload Interface Components
upload_group = gr.Group(visible=True)
with upload_group:
gr.Markdown("""
# Data Query Interface
Upload your data file to begin.
### Supported File Types:
- SQL (.sql): SQL file containing CREATE TABLE and INSERT statements
- CSV (.csv): CSV file with headers that will be automatically converted to a table
### CSV Requirements:
- Must include headers
- First column will be used as the primary key
- Column types will be automatically detected
### SQL Requirements:
- Must contain valid SQL statements
- Statements must be separated by semicolons
- Should include CREATE TABLE and data insertion statements
""")
file_input = gr.File(
label="Upload Data File",
file_types=[".csv", ".sql"],
type="filepath"
)
upload_status = gr.Textbox(label="Status", interactive=False)
continue_btn = gr.Button("Continue", visible=False)
# Query Interface Components
query_group = gr.Group(visible=False)
with query_group:
gr.Markdown("""
## Data Query Interface
Enter your questions about the data in natural language.
The AI will convert your questions into SQL queries.
""")
with gr.Row():
with gr.Column(scale=1):
user_input = gr.Textbox(label="Ask a question about the data")
query_output = gr.Textbox(label="Result")
with gr.Column(scale=2):
gr.Markdown("### Current Data")
data_table = gr.Dataframe(
value=get_data_table(),
label="Data Table",
interactive=False
)
schema_display = gr.Markdown(value="Loading schema...")
with gr.Row():
refresh_table_btn = gr.Button("Refresh Table")
refresh_schema_btn = gr.Button("Refresh Schema")
back_btn = gr.Button("Upload New File")
def handle_upload(file):
success, message = process_uploaded_file(file)
return message, gr.Button.update(visible=success)
def update_schema():
schema = get_table_schema()
if schema:
return f"### Current Schema:\n```\n{schema}\n```"
return "No data loaded"
def switch_to_query():
return {
upload_group: gr.Group.update(visible=False),
query_group: gr.Group.update(visible=True),
schema_display: update_schema()
}
def switch_to_upload():
return {
upload_group: gr.Group.update(visible=True),
query_group: gr.Group.update(visible=False),
continue_btn: gr.Button.update(visible=False),
upload_status: gr.Textbox.update(value="")
}
# Event handlers
file_input.upload(
fn=handle_upload,
inputs=[file_input],
outputs=[upload_status, continue_btn]
)
continue_btn.click(
fn=switch_to_query,
outputs=[upload_group, query_group, schema_display]
)
back_btn.click(
fn=switch_to_upload,
outputs=[upload_group, query_group, continue_btn, upload_status]
)
user_input.change(
fn=query_sql,
inputs=[user_input],
outputs=[query_output]
)
refresh_table_btn.click(
fn=get_data_table,
outputs=[data_table]
)
refresh_schema_btn.click(
fn=update_schema,
outputs=[schema_display]
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860, share=True)