Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py  —  BizIntel AI Ultra (Any metric, CSV/Excel/DB, Plotly, Gemini 1.5 Pro)
+# app.py  —  BizIntel AI Ultra  (Any metric, CSV/Excel/DB, Plotly, Gemini 1.5 Pro)
 
 import os
 import tempfile
@@ -9,11 +9,32 @@ import streamlit as st
 import google.generativeai as genai
 import plotly.graph_objects as go
 
-from tools.csv_parser      import parse_csv_tool
-from tools.plot_generator  import plot_metric_tool          # NEW generic
-from tools.forecaster      import forecast_metric_tool      # NEW generic
-from tools.visuals         import histogram_tool, scatter_matrix_tool, corr_heatmap_tool
-from db_connector          import fetch_data_from_db, list_tables, SUPPORTED_ENGINES
+# ──────────────────────────────────────────────────────────────
+# SAFELY REDIRECT ALL write_image CALLS TO /tmp
+# ──────────────────────────────────────────────────────────────
+_tmp = tempfile.gettempdir()
+_orig_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)
+
+go.Figure.write_image = _safe_write
+
+# ──────────────────────────────────────────────────────────────
+# TOOL IMPORTS
+# ──────────────────────────────────────────────────────────────
+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
 
 # ──────────────────────────────────────────────────────────────
 # 1.  GEMINI CONFIG
@@ -21,55 +42,58 @@ from db_connector          import fetch_data_from_db, list_tables, SUPPORTED_ENG
 genai.configure(api_key=os.getenv("GEMINI_APIKEY"))
 gemini = genai.GenerativeModel(
     "gemini-1.5-pro-latest",
-    generation_config={"temperature": 0.7, "top_p": 0.9, "response_mime_type": "text/plain"},
+    generation_config={
+        "temperature": 0.7,
+        "top_p": 0.9,
+        "response_mime_type": "text/plain",
+    },
 )
 
 # ──────────────────────────────────────────────────────────────
-# 2.  PAGE CONFIG
+# 2.  PAGE SETUP
 # ──────────────────────────────────────────────────────────────
-st.set_page_config(page_title="BizIntel AI Ultra", layout="wide")
-st.title("📊 BizIntel AI Ultra – Advanced Analytics + Gemini 1.5 Pro")
+st.set_page_config(page_title="BizIntel AI Ultra", layout="wide")
+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, or DB)
 # ──────────────────────────────────────────────────────────────
-src = st.radio("Select data source", ["Upload CSV / Excel", "Connect to SQL Database"])
+source = st.radio("Select data source", ["Upload CSV / Excel", "Connect to SQL Database"])
 csv_path: str | None = None
-file_kind: Literal["csv", "excel"] | None = None
 
-if src == "Upload CSV / Excel":
-    up = st.file_uploader("Upload CSV or Excel (≤ 500 MB)", type=["csv", "xlsx", "xls"])
+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)
         with open(temp_path, "wb") as f:
             f.write(up.read())
 
-        if up.name.lower().endswith("csv"):
-            csv_path, file_kind = temp_path, "csv"
-        else:  # Excel → convert first sheet to CSV
+        if up.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"
                 df_xl.to_csv(csv_path, index=False)
-                file_kind = "excel"
             except Exception as e:
                 st.error(f"Excel parsing failed: {e}")
                 st.stop()
+
         st.success(f"{up.name} saved ✅")
 
-else:  # SQL DB
+else:
     engine = st.selectbox("DB engine", SUPPORTED_ENGINES)
     conn   = st.text_input("SQLAlchemy connection string")
     if conn:
         try:
-            tbls = list_tables(conn)
-            tbl  = st.selectbox("Table", tbls)
+            tables = list_tables(conn)
+            table  = st.selectbox("Table", tables)
             if st.button("Fetch table"):
-                csv_path = fetch_data_from_db(conn, tbl)
-                file_kind = "csv"
-                st.success(f"Fetched **{tbl}** as CSV ✅")
+                csv_path = fetch_data_from_db(conn, table)
+                st.success(f"Fetched **{table}** as CSV ✅")
         except Exception as e:
             st.error(f"Connection failed: {e}")
             st.stop()
@@ -78,60 +102,66 @@ if csv_path is None:
     st.stop()
 
 with open(csv_path, "rb") as f:
-    st.download_button("⬇️ Download working CSV", f, file_name=os.path.basename(csv_path))
+    st.download_button("⬇️ Download working CSV", f, file_name=os.path.basename(csv_path))
 
 # ──────────────────────────────────────────────────────────────
-# 4.  COLUMN PICKERS
+# 4.  COLUMN SELECTION
 # ──────────────────────────────────────────────────────────────
 df_head = pd.read_csv(csv_path, nrows=5)
 st.dataframe(df_head)
 
-date_col   = st.selectbox("Select date/time column", df_head.columns)
+date_col    = st.selectbox("Select date/time column", df_head.columns)
 numeric_cols = df_head.select_dtypes("number").columns
-metric_col = st.selectbox("Select numeric metric column", numeric_cols)
+metric_col  = st.selectbox("Select numeric metric column", numeric_cols)
 
 # ──────────────────────────────────────────────────────────────
-# 5.  LOCAL TOOLS (TREND + FORECAST)
+# 5.  LOCAL ANALYTICS: SUMMARY, TREND, FORECAST
 # ──────────────────────────────────────────────────────────────
 with st.spinner("Parsing dataset…"):
     summary_text = parse_csv_tool(csv_path)
 
-with st.spinner("📈 Building trend chart…"):
+with st.spinner("Building 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)
 else:
     st.warning(trend_fig)
 
-with st.spinner("🔮 Forecasting…"):
+with st.spinner("Forecasting…"):
     forecast_text = forecast_metric_tool(csv_path, date_col, metric_col)
-    forecast_png  = "forecast_plot.png" if os.path.exists("forecast_plot.png") else None
 
-if forecast_png:
-    st.image(forecast_png, caption=f"{metric_col} Forecast", use_column_width=True)
+# Show forecast interactive (PNG saved safely in /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.")
 
 # ──────────────────────────────────────────────────────────────
-# 6.  GEMINI INSIGHTS
+# 6.  GEMINI STRATEGY
 # ──────────────────────────────────────────────────────────────
 prompt = (
     f"You are **BizIntel Strategist AI**.\n\n"
     f"### Dataset Summary\n```\n{summary_text}\n```\n\n"
     f"### {metric_col} Forecast\n```\n{forecast_text}\n```\n\n"
-    "Deliver **Markdown** with:\n"
-    f"1. Five key insights focused on **{metric_col}**\n"
-    "2. Three actionable strategies (impact‑oriented)\n"
+    "Return **Markdown** with:\n"
+    "1. Five key insights\n"
+    "2. Three actionable strategies\n"
     "3. Risk factors or anomalies\n"
     "4. Suggested additional visuals\n"
 )
 
-st.subheader("🚀 Strategy Recommendations (Gemini 1.5 Pro)")
+st.subheader("🚀 Strategy Recommendations (Gemini 1.5 Pro)")
 with st.spinner("Generating insights…"):
     strategy_md = gemini.generate_content(prompt).text
 st.markdown(strategy_md)
-st.download_button("���️ Download Strategy (.md)", strategy_md, file_name="strategy.md")
+st.download_button("⬇️ Download Strategy (.md)", strategy_md, file_name="strategy.md")
 
 # ──────────────────────────────────────────────────────────────
-# 7.  KPI CARDS + STAT EXPANDER
+# 7.  KPI CARDS + DETAILED STATS
 # ──────────────────────────────────────────────────────────────
 full_df    = pd.read_csv(csv_path, low_memory=False)
 total_rows = len(full_df)
@@ -141,11 +171,11 @@ 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}%")
+c1.metric("Rows",     f"{total_rows:,}")
+c2.metric("Columns",  str(num_cols))
+c3.metric("Missing %", f"{missing_pct:.1f}%")
 
-with st.expander("🔎 Detailed descriptive statistics"):
+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"),
@@ -159,13 +189,13 @@ st.markdown("---")
 st.subheader("🔍 Optional Exploratory Visuals")
 
 if st.checkbox("Histogram"):
-    hist_col = st.selectbox("Variable", numeric_cols, key="hist")
-    st.plotly_chart(histogram_tool(csv_path, hist_col), use_container_width=True)
+    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 heat-map"):
     st.plotly_chart(corr_heatmap_tool(csv_path), use_container_width=True)