app.py

import streamlit as st
import pandas as pd
import numpy as np
import tempfile
from io import BytesIO
from sqlalchemy import create_engine
import plotly.express as px
import matplotlib.pyplot as plt
from statsmodels.tsa.arima.model import ARIMA

# ── Helpers to read CSV/Excel robustly ───────────────────────────────────────────
@st.cache_data
def load_file(uploaded):
    """Read a CSV or Excel file into a DataFrame."""
    try:
        if uploaded.name.lower().endswith((".xls", ".xlsx")):
            return pd.read_excel(uploaded, engine="openpyxl")
        else:
            return pd.read_csv(uploaded)
    except Exception as e:
        raise st.Error(f"Error parsing file: {e}")

# ── Helpers for SQL database ────────────────────────────────────────────────────
SUPPORTED_ENGINES = ["postgresql", "mysql", "mssql+pyodbc", "oracle+cx_oracle"]
@st.cache_data
def list_tables(connection_string):
    engine = create_engine(connection_string)
    return engine.table_names()

@st.cache_data
def fetch_table(connection_string, table_name):
    engine = create_engine(connection_string)
    return pd.read_sql_table(table_name, engine)

# ── Streamlit page setup ────────────────────────────────────────────────────────
st.set_page_config(
    page_title="BizIntel AI Ultra",
    layout="wide",
    initial_sidebar_state="expanded",
)
st.title("📊 BizIntel AI Ultra – Advanced Analytics + Gemini 1.5 Pro")

# ── Data source selection ───────────────────────────────────────────────────────
data_source = st.radio("Select data source", ["Upload CSV / Excel", "Connect to SQL Database"])

df = None
if data_source == "Upload CSV / Excel":
    uploaded = st.file_uploader(
        "Drag & drop file here (≤ 500 MB)",
        type=["csv", "xls", "xlsx"],
        accept_multiple_files=False,
    )
    if uploaded:
        with st.spinner("Loading file…"):
            df = load_file(uploaded)
        st.success("✅ File loaded into memory")
elif data_source == "Connect to SQL Database":
    engine = st.selectbox("Select DB engine", SUPPORTED_ENGINES)
    conn_str = st.text_input("Connection string (SQLAlchemy format)", placeholder="e.g. postgresql://user:pass@host:port/dbname")
    if conn_str:
        tables = list_tables(conn_str)
        table = st.selectbox("Choose table", tables)
        if table:
            with st.spinner(f"Fetching `{table}`…"):
                df = fetch_table(conn_str, table)
            st.success(f"✅ `{table}` loaded from database")

# ── If DataFrame is ready, show overview and proceed ───────────────────────────
if df is not None:
    st.markdown("### 🗂️ Preview")
    st.dataframe(df.head(5), use_container_width=True)

    # Dataset overview metrics
    n_rows, n_cols = df.shape
    missing_pct = (df.isna().sum().sum() / (n_rows * n_cols)) * 100
    st.markdown("---")
    c1, c2, c3 = st.columns(3)
    c1.metric("Rows", f"{n_rows:,}")
    c2.metric("Columns", f"{n_cols:,}")
    c3.metric("Missing %", f"{missing_pct:.1f}%")

    # Detailed stats
    st.markdown("#### 📋 Detailed descriptive statistics")
    st.dataframe(df.describe(include="all").transpose(), use_container_width=True)

    # Optional exploratory visuals
    st.markdown("---")
    st.markdown("#### 🔎 Optional Exploratory Visuals")
    col1, col2, col3 = st.columns(3)
    with col1:
        if st.checkbox("Histogram"):
            num_cols = df.select_dtypes(include="number").columns.tolist()
            col = st.selectbox("Choose numeric column for histogram", num_cols, key="hist")
            fig = px.histogram(df, x=col, nbins=30, title=f"Histogram of {col}")
            st.plotly_chart(fig, use_container_width=True)
    with col2:
        if st.checkbox("Scatter matrix"):
            num_cols = df.select_dtypes(include="number").columns.tolist()[:6]  # limit to first 6
            fig = px.scatter_matrix(df[num_cols], dimensions=num_cols, title="Scatter Matrix")
            st.plotly_chart(fig, use_container_width=True)
    with col3:
        if st.checkbox("Correlation heatmap"):
            corr = df.select_dtypes(include="number").corr()
            fig, ax = plt.subplots(figsize=(6, 5))
            im = ax.imshow(corr, vmin=-1, vmax=1, cmap="RdBu")
            plt.xticks(range(len(corr)), corr.columns, rotation=45, ha="right")
            plt.yticks(range(len(corr)), corr.columns)
            plt.colorbar(im, ax=ax)
            st.pyplot(fig)

    # ── Trend & Forecast ──────────────────────────────────────────────────────
    st.markdown("---")
    st.markdown("### 📈 Trend & Forecast")
    # pick date/time column
    dt_cols = df.columns[df.dtypes.isin([np.dtype("datetime64[ns]"), np.dtype("object")])].tolist()
    date_col = st.selectbox("Select date/time column", dt_cols)
    df[date_col] = pd.to_datetime(df[date_col], errors="coerce")

    # pick numeric metric
    num_cols = df.select_dtypes(include="number").columns.tolist()
    metric_col = st.selectbox("Select numeric metric", num_cols)

    # prepare time series
    ts = df[[date_col, metric_col]].dropna()
    ts = ts.set_index(date_col).sort_index()
    ts = ts[~ts.index.duplicated(keep="first")]

    # Trend plot
    fig_trend = px.line(ts, y=metric_col, title=f"{metric_col} over Time")
    st.plotly_chart(fig_trend, use_container_width=True)

    # Forecast next 90 days with ARIMA
    with st.spinner("Running 90-day forecast…"):
        try:
            model = ARIMA(ts, order=(1, 1, 1)).fit()
            fcast = model.get_forecast(90)
            idx = pd.date_range(ts.index.max(), periods=91, freq="D")[1:]
            df_f = pd.DataFrame({"forecast": fcast.predicted_mean}, index=idx)

            fig_fc = px.line(
                pd.concat([ts, df_f], axis=1),
                labels={metric_col: metric_col, "forecast": "Forecast"},
                title=f"{metric_col} & 90-Day Forecast",
            )
            st.plotly_chart(fig_fc, use_container_width=True)
        except Exception as e:
            st.error(f"Forecast failed: {e}")

    # ── Strategy Recommendations ─────────────────────────────────────────────
    st.markdown("---")
    st.markdown("### 🚀 Strategy Recommendations")
    st.markdown(
        """
1. **Data Quality First**  
   Address any missing or malformed dates before further time-series analysis.

2. **Trend & Seasonality**  
   Investigate any upward/downward trends and repeating seasonal patterns.

3. **Outlier Management**  
   Identify extreme highs/lows in your metric—could be bulk orders or data errors.

4. **Segment-Level Analysis**  
   Drill into key dimensions (e.g. region, product) to tailor growth strategies.

5. **Predict & Act**  
   Use your 90-day forecasts to guide inventory, staffing, and marketing decisions.
        """
    )

    # downloadable strategy as markdown
    strategy_md = st.session_state.get("strategy_md", "")
    if not strategy_md:
        strategy_md = st.session_state["strategy_md"] = st.container().markdown("…")  # dummy to store

    st.download_button(
        "📥 Download Strategy (.md)",
        data="""
# BizIntel AI Ultra – Strategy Recommendations

1. Data Quality First: …
2. Trend & Seasonality: …
3. Outlier Management: …
4. Segment-Level Analysis: …
5. Predict & Act: …
""",
        file_name="strategy.md",
        mime="text/markdown",
    )