import streamlit as st
import pandas as pd
from pathlib import Path
from demo import load_data, run_checks, format_console_output
from llm_utils import generate_summary

# ---------------------------------------------------------------------------
# Configuration & Setup
# ---------------------------------------------------------------------------
st.set_page_config(
    page_title="Analytics Validation Demo",
    page_icon="📊",
    layout="wide"
)

# ---------------------------------------------------------------------------
# Helper Functions
# ---------------------------------------------------------------------------
@st.cache_data
def load_and_verify_data(filepath):
    """Load data using existing logic from demo.py."""
    return load_data(filepath)

def display_issues(issues):
    """Render list of issues as Streamlit alerts."""
    if not issues:
        st.success("✅ No issues detected. Data appears clean.")
        return

    st.warning(f"⚠️ Found {len(issues)} issues")
    
    for issue in issues:
        severity_icon = "🔴" if issue["severity"] == "ERROR" else "⚠️"
        with st.expander(f"{severity_icon} [{issue['severity']}] {issue['type']} in {issue.get('column', 'General')}"):
            st.write(f"**Detail:** {issue['detail']}")
            if issue.get("dates"):
                st.write(f"**Affected Dates:** {', '.join(issue['dates'])}")

def display_metrics(stats):
    """Render Key Metrics in columns."""
    st.subheader("Key Metrics")
    
    col1, col2 = st.columns(2)
    
    with col1:
        rev = stats["revenue"]
        st.metric("Total Revenue", f"${rev['total']:,.2f}")
        st.caption(f"Mean: ${rev['mean']:,.2f} | Missing: {rev['missing_count']}")

    with col2:
        ord_ = stats["orders"]
        st.metric("Total Orders", f"{int(ord_['total']):,}")
        st.caption(f"Mean: {ord_['mean']:,.0f} | Missing: {ord_['missing_count']}")
    
    st.divider()

# ---------------------------------------------------------------------------
# Main App Layout
# ---------------------------------------------------------------------------
def main():
    st.title("📊 Analytics Validation Engine")
    st.markdown("""
    This demo validates daily business metrics for anomalies, missing data, and consistency errors.
    It uses a deterministic rule engine and an optional local LLM for narration.
    """)

    # Sidebar parameters
    st.sidebar.header("Configuration")
    uploaded_file = st.sidebar.file_uploader("Upload CSV", type=["csv"])
    
    # Check for default data if no upload
    data_path = "data.csv"
    if uploaded_file is not None:
        data_path = uploaded_file
    elif not Path(data_path).exists():
        st.error("❌ data.csv not found and no file uploaded.")
        st.stop()

    # Load Data
    try:
        if uploaded_file:
            df = pd.read_csv(uploaded_file, parse_dates=["date"])
             # Quick re-validation using original logic if possible, or just use pandas directly
             # Ideally we re-use load_data but it expects a filepath string. 
             # For simplicity in this demo, let's just use the loaded DF if it's from upload,
             # but we need to ensure it matches schema. 
            if not pd.api.types.is_datetime64_any_dtype(df["date"]):
                 df["date"] = pd.to_datetime(df["date"])
            df = df.sort_values("date").reset_index(drop=True)
        else:
            df = load_and_verify_data(data_path)
            
        st.sidebar.success(f"Loaded {len(df)} rows")
        
    except Exception as e:
        st.error(f"Error loading data: {e}")
        st.stop()

    # Run Analysis
    with st.spinner("Running validation rules..."):
        results = run_checks(df)
    
    # Display Findings
    
    # 1. Executive Summary
    st.header("Executive Summary")
    
    # We use a placeholder to update it if we want to stream (future optimization), 
    # but for now synchronous generation is fine.
    with st.spinner("Generating summary (LLM or Rule-based)..."):
        summary = generate_summary(results)
    
    st.info(summary)
    
    # 2. Detailed Issues
    st.header("Detected Issues")
    display_issues(results["issues"])
    
    # 3. Data Overview
    st.header("Data Overview")
    display_metrics(results["stats"])
    
    with st.expander("View Raw Data"):
        st.dataframe(df)

if __name__ == "__main__":
    main()