Update app.py

app.py

@@ -1,4 +1,5 @@
-import os, tempfile, streamlit as st, inspect
+import os, tempfile, streamlit as st
+from google.adk.runner import run_agent_sync     # ← NEW
 from agents.analytics_pipeline import analytics_coordinator
 from db_connector import fetch_data_from_db, list_tables, SUPPORTED_ENGINES
 
@@ -7,11 +8,11 @@ st.title("📊 BizIntel AI Ultra – Business Intelligence Agent")
 
 TEMP_DIR = tempfile.gettempdir()
 
-# ── 1. Data source ───────────────────────────────────────────
-source = st.radio("Select data source", ["Upload CSV", "Connect to SQL Database"])
+# ── Data source ───────────────────────────────────────────────
+src = st.radio("Select data source", ["Upload CSV", "Connect to SQL Database"])
 csv_path = None
 
-if source == "Upload CSV":
+if src == "Upload CSV":
     up = st.file_uploader("Upload CSV", ["csv"])
     if up:
         csv_path = os.path.join(TEMP_DIR, up.name)
@@ -20,49 +21,31 @@ if source == "Upload CSV":
         st.success("CSV saved ✅")
 
 else:
-    engine = st.selectbox("DB engine", SUPPORTED_ENGINES)
+    eng = st.selectbox("DB engine", SUPPORTED_ENGINES)
     conn = st.text_input("SQLAlchemy connection string")
     if conn:
-        tables = list_tables(conn)
-        table = st.selectbox("Table", tables) if tables else None
-        if table:
-            csv_path = fetch_data_from_db(conn, table)
-            st.success(f"Fetched **{table}** as CSV ✅")
+        tbls = list_tables(conn)
+        tbl = st.selectbox("Table", tbls) if tbls else None
+        if tbl:
+            csv_path = fetch_data_from_db(conn, tbl)
+            st.success(f"Fetched **{tbl}** as CSV ✅")
 
-# ── 2. Optional image preview ────────────────────────────────
+# ── Optional image preview ────────────────────────────────────
 st.markdown("---")
 img = st.file_uploader("Optional image (PNG/JPG)", ["png", "jpg", "jpeg"])
 if img:
-    img_p = os.path.join(TEMP_DIR, img.name)
-    with open(img_p, "wb") as f:
+    img_path = os.path.join(TEMP_DIR, img.name)
+    with open(img_path, "wb") as f:
         f.write(img.read())
-    st.image(img_p, use_column_width=True)
+    st.image(img_path, use_column_width=True)
 
-# ── 3. Run agent pipeline ────────────────────────────────────
+# ── Run pipeline via ADK runner ───────────────────────────────
 if csv_path:
     st.markdown("---")
     st.info("Running analytics pipeline…")
 
-    # Discover an executable method
-    cand_methods = ["invoke", "run", "execute", "chat", "__call__"]
-    exec_fn = None
-    for m in cand_methods:
-        if hasattr(analytics_coordinator, m) and callable(getattr(analytics_coordinator, m)):
-            exec_fn = getattr(analytics_coordinator, m)
-            break
-
-    if exec_fn is None:
-        st.error("Cannot find an execution method on LlmAgent. Available attrs:\n"
-                 + ", ".join(dir(analytics_coordinator)))
-        st.stop()
-
-    # Determine if function expects keyword or positional arg
-    sig = inspect.signature(exec_fn)
     try:
-        if "input" in sig.parameters:
-            report = exec_fn(input=csv_path)
-        else:
-            report = exec_fn(csv_path)
+        report = run_agent_sync(agent=analytics_coordinator, input_data=csv_path)
     except Exception as e:
         st.error(f"Agent execution failed: {e}")
         st.stop()
@@ -70,8 +53,9 @@ if csv_path:
     st.subheader("📝 Analysis & Strategy Report")
     st.text(report)
 
-    # Show charts if any were created
-    for name, cap in [("sales_plot.png", "Sales Trend"), ("forecast_plot.png", "Forecast")]:
+    # Show charts if generated
+    for name, cap 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=cap, use_column_width=True)