| import streamlit as st
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| import pandas as pd
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|
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| from agents.langchain_sql_agent import ask_agent
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|
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| st.set_page_config(
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| page_title="GenAI Text2SQL Assistant",
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| page_icon="🤖",
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| layout="wide"
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| )
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| st.title("🤖 GenAI Text2SQL Analytics Assistant")
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|
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| st.markdown("""
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| Query your business database using natural language.
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|
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| Examples:
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| - Top selling products
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| - Revenue by category
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| - Monthly sales trend
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| - Customer spending analysis
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| """)
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| question = st.text_input(
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| "Ask your business question:"
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| )
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| if st.button("Generate Insights"):
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|
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| if question:
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| with st.spinner("Analyzing database..."):
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|
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| response = ask_agent(question)
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|
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|
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| if "error" in response:
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|
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| st.error(response["error"])
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|
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| else:
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| st.subheader("🧠 Generated SQL")
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| st.code(response["sql"], language="sql")
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| st.subheader("📊 Query Results")
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| st.dataframe(
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| response["data"],
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| use_container_width=True
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| )
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| csv = response["data"].to_csv(index=False)
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|
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| st.download_button(
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| label="⬇ Download Results CSV",
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| data=csv,
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| file_name="query_results.csv",
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| mime="text/csv"
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| )
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| st.subheader("🤖 AI Business Summary")
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| st.write(response["summary"])
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| df = response["data"]
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|
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| if len(df.columns) >= 2:
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|
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| numeric_cols = df.select_dtypes(
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| include="number"
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| ).columns
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|
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| if len(numeric_cols) > 0:
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|
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| st.subheader("📈 Visualization")
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| chart_col = numeric_cols[0]
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|
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| st.bar_chart(
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| df.set_index(df.columns[0])[chart_col]
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| ) |
