app.py

import os
import tempfile
import streamlit as st
from agents.analytics_pipeline import analytics_coordinator
from db_connector import fetch_data_from_db, list_tables, SUPPORTED_ENGINES

# ──────────────────────────────────────────────────────────────
#  PAGE CONFIG
# ──────────────────────────────────────────────────────────────
st.set_page_config(page_title="BizIntel AI Ultra", layout="wide")
st.title("📊 BizIntel AI Ultra – Business Intelligence Agent")

# Writable directory inside Hugging Face container
TEMP_DIR = tempfile.gettempdir()

# ──────────────────────────────────────────────────────────────
# 1.  DATA SOURCE SELECTION (CSV or DB)
# ──────────────────────────────────────────────────────────────
input_source = st.radio("Select data source", ["Upload CSV", "Connect to SQL Database"])
csv_path: str | None = None

if input_source == "Upload CSV":
    uploaded_csv = st.file_uploader("Upload CSV", type="csv")
    if uploaded_csv:
        csv_path = os.path.join(TEMP_DIR, uploaded_csv.name)
        with open(csv_path, "wb") as f:
            f.write(uploaded_csv.read())
        st.success("CSV saved to temporary storage ✅")

elif input_source == "Connect to SQL Database":
    engine = st.selectbox("Database engine", SUPPORTED_ENGINES)
    conn_str = st.text_input("SQLAlchemy connection string")
    if conn_str:
        tables = list_tables(conn_str)
        if tables:
            table_name = st.selectbox("Choose a table", tables)
            if table_name:
                csv_path = fetch_data_from_db(conn_str, table_name)
                st.success(f"Fetched **{table_name}** as CSV ✅")

# ──────────────────────────────────────────────────────────────
# 2.  OPTIONAL IMAGE UPLOAD & PREVIEW
# ──────────────────────────────────────────────────────────────
st.markdown("---")
st.subheader("📷 Optional: Upload an Image for Preview / Future Analysis")

img_file = st.file_uploader(
    "Upload an image (PNG/JPG)", type=["png", "jpg", "jpeg"], key="img"
)

if img_file:
    img_path = os.path.join(TEMP_DIR, img_file.name)
    with open(img_path, "wb") as f:
        f.write(img_file.read())
    st.image(img_path, caption="Uploaded Image", use_column_width=True)
    # 👉 To analyse with a vision agent later, call it here with `img_path`.

# ──────────────────────────────────────────────────────────────
# 3.  RUN ANALYTICS PIPELINE
# ──────────────────────────────────────────────────────────────
if csv_path:
    st.markdown("---")
    st.info("Running analytics pipeline… ⏳")

    # Google ADK agents now use .invoke() (sync) instead of .run()
    report = analytics_coordinator.invoke(input=csv_path)

    st.subheader("📝 Analysis & Strategy Report")
    st.text(report)

    # Display charts saved by tool functions (if any)
    for plot_name, caption in [
        ("sales_plot.png", "Sales Trend"),
        ("forecast_plot.png", "Forecast Chart"),
    ]:
        plot_path = os.path.join(TEMP_DIR, plot_name)
        if os.path.exists(plot_path):
            st.image(plot_path, caption=caption, use_column_width=True)