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# app.py
import os
import pandas as pd
import gradio as gr
from tools.sql_tool import SQLTool
from tools.ts_preprocess import build_timeseries
# --- Environment & defaults ---
DUCKDB_PATH = os.getenv("DUCKDB_PATH", "alm.duckdb")
DEFAULT_SCHEMA = os.getenv("SQL_DEFAULT_SCHEMA", "main")
DEFAULT_TABLE = os.getenv("SQL_DEFAULT_TABLE", "masterdataset_v")
sql_tool = SQLTool(DUCKDB_PATH)
INTRO = f"""
### ALM LLM — Demo
Connected to **DuckDB** at `{DUCKDB_PATH}` using table **{DEFAULT_SCHEMA}.{DEFAULT_TABLE}**.
**Try:**
- *"show me the top 10 fds by portfolio value"*
- *"top 10 assets by portfolio value"*
- *"sum portfolio value by currency"*
"""
# --- Handlers ---
def run_nl(nl_query: str):
if not nl_query or not nl_query.strip():
return (
pd.DataFrame(),
"",
"Please enter a query.",
pd.DataFrame(),
pd.DataFrame(),
)
try:
df, sql, why = sql_tool.query_from_nl(nl_query)
except Exception as e:
return pd.DataFrame(), "", f"Error: {e}", pd.DataFrame(), pd.DataFrame()
# Try building projections and liquidity gap
try:
cf, gap = build_timeseries(df)
except Exception:
cf, gap = pd.DataFrame(), pd.DataFrame()
return df, sql.strip(), why, cf, gap
def run_sql(sql_text: str):
if not sql_text or not sql_text.strip():
return pd.DataFrame(), "Please paste a SQL statement.", pd.DataFrame(), pd.DataFrame()
try:
df = sql_tool.run_sql(sql_text)
except Exception as e:
return pd.DataFrame(), f"Error: {e}", pd.DataFrame(), pd.DataFrame()
try:
cf, gap = build_timeseries(df)
except Exception:
cf, gap = pd.DataFrame(), pd.DataFrame()
return df, "OK", cf, gap
# --- UI ---
with gr.Blocks(title="ALM LLM") as demo:
gr.Markdown(INTRO)
with gr.Tab("Ask in Natural Language"):
nl = gr.Textbox(
label="Ask a question",
placeholder="e.g., show me the top 10 fds by portfolio value",
lines=2,
)
btn = gr.Button("Run")
sql_out = gr.Textbox(label="Generated SQL", interactive=False)
why_out = gr.Textbox(label="Reasoning", interactive=False)
df_out = gr.Dataframe(label="Query Result", interactive=True)
cf_out = gr.Dataframe(label="Projected Cash-Flows (if applicable)", interactive=True)
gap_out = gr.Dataframe(label="Liquidity Gap (monthly)", interactive=True)
# IMPORTANT: keep event binding INSIDE the Blocks context
btn.click(
fn=run_nl,
inputs=[nl],
outputs=[df_out, sql_out, why_out, cf_out, gap_out],
)
with gr.Tab("Run Raw SQL"):
sql_in = gr.Code(
label="SQL",
language="sql",
value=f"SELECT * FROM {DEFAULT_SCHEMA}.{DEFAULT_TABLE} LIMIT 20;",
)
btn2 = gr.Button("Execute")
df2 = gr.Dataframe(label="Result", interactive=True)
status = gr.Textbox(label="Status", interactive=False)
cf2 = gr.Dataframe(label="Projected Cash-Flows (if applicable)", interactive=True)
gap2 = gr.Dataframe(label="Liquidity Gap (monthly)", interactive=True)
btn2.click(
fn=run_sql,
inputs=[sql_in],
outputs=[df2, status, cf2, gap2],
)
# --- Launch ---
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
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