Update app.py

app.py

@@ -4,75 +4,65 @@ 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
-# ──────────────────────────────────────────────────────────────
+# Page setup
 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)
-# ──────────────────────────────────────────────────────────────
+# 1.  Data source
 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)
+    up_csv = st.file_uploader("Upload CSV", type="csv")
+    if up_csv:
+        csv_path = os.path.join(TEMP_DIR, up_csv.name)
         with open(csv_path, "wb") as f:
-            f.write(uploaded_csv.read())
-        st.success("CSV saved to temporary storage ✅")
+            f.write(up_csv.read())
+        st.success("CSV saved ✅")
 
 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)
+    engine = st.selectbox("DB engine", SUPPORTED_ENGINES)
+    conn = st.text_input("SQLAlchemy connection string")
+    if conn:
+        tables = list_tables(conn)
         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 ✅")
+            table = st.selectbox("Table", tables)
+            if table:
+                csv_path = fetch_data_from_db(conn, table)
+                st.success(f"Fetched **{table}** as CSV ✅")
 
-# ──────────────────────────────────────────────────────────────
-# 2.  OPTIONAL IMAGE UPLOAD & PREVIEW
-# ──────────────────────────────────────────────────────────────
+# 2.  Optional image preview
 st.markdown("---")
-st.subheader("📷 Optional: Upload an Image for Preview / Future Analysis")
+st.subheader("📷 Optional image upload")
 
-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)
+img = st.file_uploader("PNG/JPG", type=["png", "jpg", "jpeg"], key="img")
+if img:
+    img_path = os.path.join(TEMP_DIR, img.name)
     with open(img_path, "wb") as f:
-        f.write(img_file.read())
+        f.write(img.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
-# ──────────────────────────────────────────────────────────────
+# 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)
+    # Robust call: try invoke() → run() → __call__()
+    if hasattr(analytics_coordinator, "invoke"):
+        report = analytics_coordinator.invoke(input=csv_path)
+    elif hasattr(analytics_coordinator, "run"):
+        report = analytics_coordinator.run(input=csv_path)
+    else:                         # fallback to callable
+        report = analytics_coordinator(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)
+    # Show charts if present
+    for name, caption in [("sales_plot.png", "Sales Trend"),
+                          ("forecast_plot.png", "Forecast Chart")]:
+        p = os.path.join(TEMP_DIR, name)
+        if os.path.exists(p):
+            st.image(p, caption=caption, use_column_width=True)