Update app.py

app.py

@@ -1,132 +1,167 @@
-# app.py  —  BizIntel AI Ultra  (Gemini 1.5 Pro, interactive Plotly visuals)
+# app.py  —  BizIntel AI Ultra v2
+# Features: CSV upload, SQL DB fetch, interactive Plotly, Gemini 1.5 Pro,
+# optional EDA, download buttons.
 
 import os
 import tempfile
+from io import StringIO, BytesIO
+
 import pandas as pd
 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_sales_tool         # accepts date_col, returns Figure or str
-from tools.forecaster import forecast_tool               # accepts date_col
+from tools.plot_generator import plot_sales_tool
+from tools.forecaster import forecast_tool          # returns text & PNG; we’ll also grab df
 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 (1.5‑Pro, temperature 0.7)
+# Gemini 1.5‑Pro
 # ──────────────────────────────────────────────────────────────
 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",  # must be allowed type
-    },
+    generation_config={"temperature": 0.7, "top_p": 0.9, "response_mime_type": "text/plain"},
 )
 
 # ──────────────────────────────────────────────────────────────
-# 2.  STREAMLIT PAGE SETUP
+# Streamlit page
 # ──────────────────────────────────────────────────────────────
-st.set_page_config(page_title="BizIntel AI Ultra – Gemini 1.5 Pro", layout="wide")
-st.title("📊 BizIntel AI Ultra – Advanced Analytics")
+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.  CSV UPLOAD
+# 1.  CHOOSE DATA SOURCE
 # ──────────────────────────────────────────────────────────────
-csv_file = st.file_uploader("Upload CSV (≤ 200 MB)", type=["csv"])
-if csv_file is None:
-    st.info("⬆️ Upload a CSV to begin.")
+data_source = st.radio("Select data source", ["Upload CSV", "Connect to SQL Database"])
+csv_path: str | None = None
+
+if data_source == "Upload CSV":
+    csv_file = st.file_uploader("Upload CSV (≤ 200 MB)", type=["csv"])
+    if csv_file:
+        csv_path = os.path.join(TEMP_DIR, csv_file.name)
+        with open(csv_path, "wb") as f:
+            f.write(csv_file.read())
+        st.success("CSV saved ✅")
+
+elif data_source == "Connect to SQL Database":
+    engine = st.selectbox("DB engine", SUPPORTED_ENGINES)
+    conn_str = st.text_input("SQLAlchemy connection string")
+    if conn_str:
+        try:
+            tables = list_tables(conn_str)
+        except Exception as e:
+            st.error(f"Connection failed: {e}")
+            st.stop()
+
+        table = st.selectbox("Select table", tables)
+        if st.button("Fetch table"):
+            csv_path = fetch_data_from_db(conn_str, table)
+            st.success(f"Fetched ‘{table}’ into CSV ✅")
+
+# Stop if we still don’t have a CSV
+if csv_path is None:
     st.stop()
 
-csv_path = os.path.join(TEMP_DIR, csv_file.name)
-with open(csv_path, "wb") as f:
-    f.write(csv_file.read())
-st.success("CSV saved ✅")
+# Offer original CSV download
+with open(csv_path, "rb") as f:
+    st.download_button("⬇️ Download original CSV", f, file_name=os.path.basename(csv_path))
 
-# Preview & date column selection
+# ──────────────────────────────────────────────────────────────
+# 2.  PREVIEW + DATE COL
+# ──────────────────────────────────────────────────────────────
 df_preview = pd.read_csv(csv_path, nrows=5)
 st.dataframe(df_preview)
+
 date_col = st.selectbox("Select date/time column for forecasting", df_preview.columns)
 
 # ──────────────────────────────────────────────────────────────
-# 4.  LOCAL TOOL EXECUTION
+# 3.  RUN LOCAL TOOLS
 # ──────────────────────────────────────────────────────────────
-with st.spinner("🔍 Parsing CSV…"):
+with st.spinner("Parsing CSV…"):
     summary_text = parse_csv_tool(csv_path)
 
-with st.spinner("📈 Generating sales trend chart…"):
+with st.spinner("Generating sales trend…"):
     sales_fig = plot_sales_tool(csv_path, date_col=date_col)
 
-# Show chart or warn
 if isinstance(sales_fig, go.Figure):
     st.plotly_chart(sales_fig, use_container_width=True)
-else:  # returned error message
+else:
     st.warning(sales_fig)
 
-with st.spinner("🔮 Forecasting future metrics…"):
-    forecast_text = forecast_tool(csv_path, date_col=date_col)
+with st.spinner("Forecasting…"):
+    forecast_text = forecast_tool(csv_path, date_col=date_col)   # PNG created
+    # Also capture forecast df from statsmodels if you return it (optional)
+    try:
+        forecast_df = pd.read_csv(StringIO(forecast_text))
+    except Exception:
+        forecast_df = None
 
-# Display forecast PNG if created
+# Forecast plot preview
 if os.path.exists("forecast_plot.png"):
     st.image("forecast_plot.png", caption="Sales Forecast", use_column_width=True)
 
+# Download forecast CSV if df exists
+if forecast_df is not None and not forecast_df.empty:
+    buf = StringIO()
+    forecast_df.to_csv(buf, index=False)
+    st.download_button("⬇️ Download Forecast CSV", buf.getvalue(), file_name="forecast.csv")
+
 # ──────────────────────────────────────────────────────────────
-# 5.  GEMINI 1.5‑PRO STRATEGY
+# 4.  GEMINI STRATEGY
 # ──────────────────────────────────────────────────────────────
 prompt = (
     f"You are **BizIntel Strategist AI**.\n\n"
-    f"### CSV Summary\n```\n{summary_text}\n```\n\n"
-    f"### Forecast Output\n```\n{forecast_text}\n```\n\n"
+    "### CSV Summary\n"
+    f"```\n{summary_text}\n```\n\n"
+    "### Forecast Output\n"
+    f"```\n{forecast_text}\n```\n\n"
     "Return **Markdown** with:\n"
-    "1. Five key insights (bullets)\n"
+    "1. Five key insights\n"
     "2. Three actionable strategies (with expected impact)\n"
     "3. Risk factors or anomalies\n"
     "4. Suggested additional visuals\n"
 )
 
 st.subheader("🚀 Strategy Recommendations (Gemini 1.5 Pro)")
-with st.spinner("Gemini 1.5 Pro is generating insights…"):
+with st.spinner("Generating insights…"):
     strategy_md = gemini.generate_content(prompt).text
 st.markdown(strategy_md)
 
+# Download strategy as Markdown
+st.download_button("⬇️ Download Strategy (.md)", strategy_md, file_name="strategy.md")
+
 # ──────────────────────────────────────────────────────────────
-# 6.  OPTIONAL EXPLORATORY VISUALS
+# 5.  OPTIONAL EXPLORATORY VISUALS
 # ──────────────────────────────────────────────────────────────
 st.markdown("---")
 st.subheader("🔍 Optional Exploratory Visuals")
 
 num_cols = df_preview.select_dtypes("number").columns
 
-# Histogram
-if st.checkbox("Show histogram"):
-    hist_col = st.selectbox("Histogram variable", num_cols, key="hist")
-    fig_hist = histogram_tool(csv_path, hist_col)
-    st.plotly_chart(fig_hist, use_container_width=True)
+if st.checkbox("Histogram"):
+    col = st.selectbox("Variable", num_cols, key="hist")
+    st.plotly_chart(histogram_tool(csv_path, col), use_container_width=True)
 
-# Scatter‑matrix
-if st.checkbox("Show scatter‑matrix"):
-    mult_cols = st.multiselect(
-        "Choose up to 5 columns", num_cols, default=num_cols[:3], key="scatter"
-    )
-    if mult_cols:
-        fig_scatter = scatter_matrix_tool(csv_path, mult_cols)
-        st.plotly_chart(fig_scatter, use_container_width=True)
+if st.checkbox("Scatter‑matrix"):
+    cols = st.multiselect("Choose up to 5 columns", num_cols, default=num_cols[:3])
+    if cols:
+        st.plotly_chart(scatter_matrix_tool(csv_path, cols), use_container_width=True)
 
-# Correlation heat‑map
-if st.checkbox("Show correlation heat‑map"):
-    fig_corr = corr_heatmap_tool(csv_path)
-    st.plotly_chart(fig_corr, use_container_width=True)
+if st.checkbox("Correlation heat‑map"):
+    st.plotly_chart(corr_heatmap_tool(csv_path), use_container_width=True)
 
 # ──────────────────────────────────────���───────────────────────
-# 7.  CSV SUMMARY TEXT
+# 6.  FULL SUMMARY
 # ──────────────────────────────────────────────────────────────
 st.markdown("---")
-st.subheader("📑 CSV Summary (full stats)")
+st.subheader("📑 CSV Summary (stats)")
 st.text(summary_text)