Update app.py

app.py

@@ -1,4 +1,6 @@
-# app.py  —  BizIntel AI Ultra  (Any metric, CSV/Excel/DB, Plotly, Gemini 1.5 Pro)
+# app.py  —  BizIntel AI Ultra  
+# Supports: CSV/Excel/DB ingestion, any numeric metric + date, interactive Plotly, Gemini 1.5 Pro,
+# KPI cards, optional EDA, safe image writes, and updated use_container_width flag.
 
 import os
 import tempfile
@@ -10,34 +12,29 @@ import google.generativeai as genai
 import plotly.graph_objects as go
 
 # ──────────────────────────────────────────────────────────────
-# SAFELY REDIRECT ALL write_image CALLS TO /tmp
+# REDIRECT ALL write_image CALLS TO A WRITABLE TEMP DIRECTORY
 # ──────────────────────────────────────────────────────────────
 _tmp = tempfile.gettempdir()
-_orig_write = go.Figure.write_image
+_original_write = go.Figure.write_image
 
 def _safe_write(self, path, *args, **kwargs):
-    # keep only filename, write into tempdir
     fname = os.path.basename(path)
     safe_path = os.path.join(_tmp, fname)
-    return _orig_write(self, safe_path, *args, **kwargs)
+    return _original_write(self, safe_path, *args, **kwargs)
 
 go.Figure.write_image = _safe_write
 
 # ──────────────────────────────────────────────────────────────
-# TOOL IMPORTS
+# TOOL IMPORTS (updated generic versions)
 # ──────────────────────────────────────────────────────────────
-from tools.csv_parser     import parse_csv_tool
-from tools.plot_generator import plot_metric_tool       # generic: date + metric
-from tools.forecaster     import forecast_metric_tool   # generic: date + metric
-from tools.visuals        import (
-    histogram_tool,
-    scatter_matrix_tool,
-    corr_heatmap_tool,
-)
-from db_connector         import fetch_data_from_db, list_tables, SUPPORTED_ENGINES
+from tools.csv_parser      import parse_csv_tool
+from tools.plot_generator  import plot_metric_tool      # date_col, metric_col
+from tools.forecaster      import forecast_metric_tool  # date_col, metric_col
+from tools.visuals         import histogram_tool, scatter_matrix_tool, corr_heatmap_tool
+from db_connector          import fetch_data_from_db, list_tables, SUPPORTED_ENGINES
 
 # ──────────────────────────────────────────────────────────────
-# 1.  GEMINI CONFIG
+# 1.  GEMINI 1.5-PRO INITIALIZATION
 # ──────────────────────────────────────────────────────────────
 genai.configure(api_key=os.getenv("GEMINI_APIKEY"))
 gemini = genai.GenerativeModel(
@@ -50,7 +47,7 @@ gemini = genai.GenerativeModel(
 )
 
 # ──────────────────────────────────────────────────────────────
-# 2.  PAGE SETUP
+# 2.  STREAMLIT PAGE CONFIG
 # ──────────────────────────────────────────────────────────────
 st.set_page_config(page_title="BizIntel AI Ultra", layout="wide")
 st.title("📊 BizIntel AI Ultra – Advanced Analytics + Gemini 1.5 Pro")
@@ -58,22 +55,21 @@ st.title("📊 BizIntel AI Ultra – Advanced Analytics + Gemini 1.5 Pro")
 TEMP_DIR = tempfile.gettempdir()
 
 # ──────────────────────────────────────────────────────────────
-# 3.  DATA SOURCE (CSV, Excel, or DB)
+# 3.  DATA SOURCE: CSV / EXCEL / SQL DATABASE
 # ──────────────────────────────────────────────────────────────
 source = st.radio("Select data source", ["Upload CSV / Excel", "Connect to SQL Database"])
 csv_path: str | None = None
 
 if source == "Upload CSV / Excel":
-    up = st.file_uploader("Upload CSV or Excel (≤ 500 MB)", type=["csv", "xlsx", "xls"])
-    if up:
-        temp_path = os.path.join(TEMP_DIR, up.name)
+    upload = st.file_uploader("Upload CSV or Excel (≤ 500 MB)", type=["csv", "xlsx", "xls"])
+    if upload:
+        temp_path = os.path.join(TEMP_DIR, upload.name)
         with open(temp_path, "wb") as f:
-            f.write(up.read())
+            f.write(upload.read())
 
-        if up.name.lower().endswith(".csv"):
+        if upload.name.lower().endswith(".csv"):
             csv_path = temp_path
         else:
-            # Excel → load sheet0 → write out to CSV
             try:
                 df_xl = pd.read_excel(temp_path, sheet_name=0)
                 csv_path = os.path.splitext(temp_path)[0] + ".csv"
@@ -81,10 +77,9 @@ if source == "Upload CSV / Excel":
             except Exception as e:
                 st.error(f"Excel parsing failed: {e}")
                 st.stop()
+        st.success(f"{upload.name} saved ✅")
 
-        st.success(f"{up.name} saved ✅")
-
-else:
+else:  # SQL Database path
     engine = st.selectbox("DB engine", SUPPORTED_ENGINES)
     conn   = st.text_input("SQLAlchemy connection string")
     if conn:
@@ -101,17 +96,18 @@ else:
 if csv_path is None:
     st.stop()
 
+# Offer download of the working CSV
 with open(csv_path, "rb") as f:
     st.download_button("⬇️ Download working CSV", f, file_name=os.path.basename(csv_path))
 
 # ──────────────────────────────────────────────────────────────
-# 4.  COLUMN SELECTION
+# 4.  COLUMN SELECTION: DATE + METRIC
 # ──────────────────────────────────────────────────────────────
 df_head = pd.read_csv(csv_path, nrows=5)
 st.dataframe(df_head)
 
 date_col    = st.selectbox("Select date/time column", df_head.columns)
-numeric_cols = df_head.select_dtypes("number").columns
+numeric_cols = df_head.select_dtypes("number").columns.tolist()
 metric_col  = st.selectbox("Select numeric metric column", numeric_cols)
 
 # ──────────────────────────────────────────────────────────────
@@ -120,7 +116,7 @@ metric_col  = st.selectbox("Select numeric metric column", numeric_cols)
 with st.spinner("Parsing dataset…"):
     summary_text = parse_csv_tool(csv_path)
 
-with st.spinner("Building trend chart…"):
+with st.spinner("Generating trend chart…"):
     trend_fig = plot_metric_tool(csv_path, date_col, metric_col)
 if isinstance(trend_fig, go.Figure):
     st.plotly_chart(trend_fig, use_container_width=True)
@@ -130,18 +126,16 @@ else:
 with st.spinner("Forecasting…"):
     forecast_text = forecast_metric_tool(csv_path, date_col, metric_col)
 
-# Show forecast interactive (PNG saved safely in /tmp)
+# Display the forecast image saved under /tmp
 st.subheader(f"🔮 {metric_col} Forecast")
-with st.spinner("Rendering forecast…"):
-    # read PNG from tempdir if exists
-    png_path = os.path.join(TEMP_DIR, "forecast_plot.png")
-    if os.path.exists(png_path):
-        st.image(png_path, use_column_width=True)
-    else:
-        st.warning("Forecast image not found.")
+forecast_png = os.path.join(TEMP_DIR, "forecast_plot.png")
+if os.path.exists(forecast_png):
+    st.image(forecast_png, use_container_width=True)
+else:
+    st.warning("Forecast image not found.")
 
 # ──────────────────────────────────────────────────────────────
-# 6.  GEMINI STRATEGY
+# 6.  GEMINI STRATEGY RECOMMENDATIONS
 # ──────────────────────────────────────────────────────────────
 prompt = (
     f"You are **BizIntel Strategist AI**.\n\n"
@@ -161,7 +155,7 @@ st.markdown(strategy_md)
 st.download_button("⬇️ Download Strategy (.md)", strategy_md, file_name="strategy.md")
 
 # ──────────────────────────────────────────────────────────────
-# 7.  KPI CARDS + DETAILED STATS
+# 7.  KPI CARDS + DETAILED STATS EXPANDER
 # ──────────────────────────────────────────────────────────────
 full_df    = pd.read_csv(csv_path, low_memory=False)
 total_rows = len(full_df)
@@ -170,10 +164,10 @@ missing_pct = full_df.isna().mean().mean() * 100
 
 st.markdown("---")
 st.subheader("📑 Dataset Overview")
-c1, c2, c3 = st.columns(3)
-c1.metric("Rows",     f"{total_rows:,}")
-c2.metric("Columns",  str(num_cols))
-c3.metric("Missing %", f"{missing_pct:.1f}%")
+col1, col2, col3 = st.columns(3)
+col1.metric("Rows",     f"{total_rows:,}")
+col2.metric("Columns",  str(num_cols))
+col3.metric("Missing %", f"{missing_pct:.1f}%")
 
 with st.expander("🔎 Detailed descriptive statistics"):
     stats_df = full_df.describe().T.reset_index().rename(columns={"index": "Feature"})
@@ -189,8 +183,8 @@ st.markdown("---")
 st.subheader("🔍 Optional Exploratory Visuals")
 
 if st.checkbox("Histogram"):
-    hcol = st.selectbox("Variable", numeric_cols, key="hist")
-    st.plotly_chart(histogram_tool(csv_path, hcol), use_container_width=True)
+    hist_col = st.selectbox("Variable", numeric_cols, key="hist")
+    st.plotly_chart(histogram_tool(csv_path, hist_col), use_container_width=True)
 
 if st.checkbox("Scatter-matrix"):
     sel = st.multiselect("Choose columns", numeric_cols, default=numeric_cols[:3])