Update app.py

app.py

@@ -1,10 +1,9 @@
 # app.py  —  BizIntel AI Ultra  
 # Supports: CSV/Excel/DB ingestion, date+metric plotting, ARIMA forecasting,
-# safe Plotly writes, Gemini 1.5 Pro strategy, KPI cards, optional EDA.
+# safe Plotly writes into /tmp, Gemini 1.5 Pro strategy, KPI cards, optional EDA.
 
 import os
 import tempfile
-from typing import Literal
 
 import pandas as pd
 import streamlit as st
@@ -12,24 +11,22 @@ import google.generativeai as genai
 import plotly.graph_objects as go
 
 # ──────────────────────────────────────────────────────────────
-# 0) Redirect all Plotly write_image() into a writable temp dir
+# 0) Monkey‐patch Plotly to write images into /tmp (writable)
 # ──────────────────────────────────────────────────────────────
 TMP = tempfile.gettempdir()
-_original_write = go.Figure.write_image
-
+_orig_write = go.Figure.write_image
 def _safe_write(self, path, *args, **kwargs):
     filename = os.path.basename(path)
     safe_path = os.path.join(TMP, filename)
-    return _original_write(self, safe_path, *args, **kwargs)
-
+    return _orig_write(self, safe_path, *args, **kwargs)
 go.Figure.write_image = _safe_write
 
 # ──────────────────────────────────────────────────────────────
-# 1) Imports for tools & DB connector
+# 1) Tool & DB imports
 # ──────────────────────────────────────────────────────────────
 from tools.csv_parser      import parse_csv_tool
-from tools.plot_generator  import plot_metric_tool      # (csv_path, date_col, metric_col) → Figure or error
-from tools.forecaster      import forecast_metric_tool  # (csv_path, date_col, metric_col) → text + /tmp/forecast_plot.png
+from tools.plot_generator  import plot_metric_tool
+from tools.forecaster      import forecast_metric_tool
 from tools.visuals         import histogram_tool, scatter_matrix_tool, corr_heatmap_tool
 from db_connector          import fetch_data_from_db, list_tables, SUPPORTED_ENGINES
 
@@ -71,7 +68,7 @@ if source == "Upload CSV / Excel":
         else:
             try:
                 df_xl = pd.read_excel(tmp_file, sheet_name=0)
-                csv_path = os.path.splitext(tmp_file)[0] + ".csv"
+                csv_path = tmp_file.rsplit(".", 1)[0] + ".csv"
                 df_xl.to_csv(csv_path, index=False)
             except Exception as e:
                 st.error(f"Excel parsing failed: {e}")
@@ -100,14 +97,21 @@ with open(csv_path, "rb") as f:
     st.download_button("⬇️ Download working CSV", f, file_name=os.path.basename(csv_path))
 
 # ──────────────────────────────────────────────────────────────
-# 5) Show first rows & let user pick date + numeric metric
+# 5) Show head & pick date + metric (but never the same column)
 # ──────────────────────────────────────────────────────────────
 df_head = pd.read_csv(csv_path, nrows=5)
 st.dataframe(df_head)
 
-date_col     = st.selectbox("Select date/time column", df_head.columns)
+# a) Date dropdown over all columns
+date_col = st.selectbox("Select date/time column", df_head.columns)
+
+# b) Metric dropdown only numeric columns, excluding the chosen date_col
 numeric_cols = df_head.select_dtypes("number").columns.tolist()
-metric_col   = st.selectbox("Select numeric metric column", numeric_cols)
+metric_options = [c for c in numeric_cols if c != date_col]
+if not metric_options:
+    st.error(f"No numeric columns available once we exclude '{date_col}'.")
+    st.stop()
+metric_col = st.selectbox("Select numeric metric column", metric_options)
 
 # ──────────────────────────────────────────────────────────────
 # 6) Local analysis: summary, trend chart, forecast
@@ -156,22 +160,29 @@ st.download_button("⬇️ Download Strategy (.md)", strategy_md, file_name="str
 # ──────────────────────────────────────────────────────────────
 # 8) KPI cards + detailed Stats
 # ──────────────────────────────────────────────────────────────
-full_df    = pd.read_csv(csv_path, low_memory=False)
-total_rows = len(full_df)
-num_cols   = full_df.shape[1]
+full_df     = pd.read_csv(csv_path, low_memory=False)
+total_rows  = len(full_df)
+num_columns = full_df.shape[1]
 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))
+c1.metric("Rows",      f"{total_rows:,}")
+c2.metric("Columns",   str(num_columns))
 c3.metric("Missing %", f"{missing_pct:.1f}%")
 
 with st.expander("🔎 Detailed descriptive statistics"):
-    stats_df = full_df.describe().T.reset_index().rename(columns={"index":"Feature"})
-    st.dataframe(stats_df.style.format(precision=2).background_gradient(cmap="Blues"),
-                 use_container_width=True)
+    stats_df = (
+        full_df.describe()
+        .T
+        .reset_index()
+        .rename(columns={"index":"Feature"})
+    )
+    st.dataframe(
+        stats_df.style.format(precision=2).background_gradient(cmap="Blues"),
+        use_container_width=True
+    )
 
 # ──────────────────────────────────────────────────────────────
 # 9) Optional Exploratory Visuals
@@ -183,10 +194,10 @@ if st.checkbox("Histogram"):
     hcol = st.selectbox("Variable", numeric_cols, key="hist")
     st.plotly_chart(histogram_tool(csv_path, hcol), use_container_width=True)
 
-if st.checkbox("Scatter-matrix"):
+if st.checkbox("Scatter Matrix"):
     sel = st.multiselect("Choose columns", numeric_cols, default=numeric_cols[:3])
     if sel:
         st.plotly_chart(scatter_matrix_tool(csv_path, sel), use_container_width=True)
 
-if st.checkbox("Correlation heat-map"):
+if st.checkbox("Correlation Heatmap"):
     st.plotly_chart(corr_heatmap_tool(csv_path), use_container_width=True)