# 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
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

# ──────────────────────────────────────────────────────────────
# 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"},
)

# ──────────────────────────────────────────────────────────────
# Streamlit page
# ──────────────────────────────────────────────────────────────
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()

# ──────────────────────────────────────────────────────────────
# 1.  CHOOSE DATA SOURCE
# ──────────────────────────────────────────────────────────────
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()

# 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))

# ──────────────────────────────────────────────────────────────
# 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)

# ──────────────────────────────────────────────────────────────
# 3.  RUN LOCAL TOOLS
# ──────────────────────────────────────────────────────────────
with st.spinner("Parsing CSV…"):
    summary_text = parse_csv_tool(csv_path)

with st.spinner("Generating sales trend…"):
    sales_fig = plot_sales_tool(csv_path, date_col=date_col)

if isinstance(sales_fig, go.Figure):
    st.plotly_chart(sales_fig, use_container_width=True)
else:
    st.warning(sales_fig)

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

# 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")

# ──────────────────────────────────────────────────────────────
# 4.  GEMINI STRATEGY
# ──────────────────────────────────────────────────────────────
prompt = (
    f"You are **BizIntel Strategist AI**.\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\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("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")

# ──────────────────────────────────────────────────────────────
# 5.  OPTIONAL EXPLORATORY VISUALS
# ──────────────────────────────────────────────────────────────
st.markdown("---")
st.subheader("🔍 Optional Exploratory Visuals")

num_cols = df_preview.select_dtypes("number").columns

if st.checkbox("Histogram"):
    col = st.selectbox("Variable", num_cols, key="hist")
    st.plotly_chart(histogram_tool(csv_path, col), 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)

if st.checkbox("Correlation heat‑map"):
    st.plotly_chart(corr_heatmap_tool(csv_path), use_container_width=True)

# ──────────────────────────────────────────────────────────────
# 6.  FULL SUMMARY
# ──────────────────────────────────────────────────────────────
st.markdown("---")
st.subheader("📑 CSV Summary (stats)")
st.text(summary_text)