import streamlit as st
import pandas as pd
from db import get_leads, init_db
import json

st.set_page_config(page_title="TradingTwins Lead Engine", layout="wide")

st.title("🚀 Lead Engine: TradingTwins")

# Metrics
leads = get_leads()
df = pd.DataFrame(leads)

if not df.empty:
    col1, col2, col3 = st.columns(3)
    col1.metric("Total Leads", len(df))
    col2.metric("New Today", len(df[df['status'] == 'new']))
    col3.metric("Action Required", len(df[df['status'] == 'enriched']))

    st.subheader("Latest Inquiries")
    
    for index, row in df.iterrows():
        with st.expander(f"{row['company_name']} - {row['status']}"):
            c1, c2 = st.columns(2)
            c1.write(f"**Contact:** {row['contact_name']}")
            c1.write(f"**Email:** {row['email']}")
            
            enrichment = json.loads(row['enrichment_data']) if row['enrichment_data'] else {}
            
            if enrichment:
                c2.success(f"Score: {enrichment.get('score')}")
                c2.write(f"**Vertical:** {enrichment.get('vertical')}")
                c2.info(f"💡 {enrichment.get('recommendation')}")
            
            if st.button(f"Generate Response for {row['id']}"):
                st.write("Generating draft... (Simulated)")
                # Here we would call the LLM
                draft = f"Hallo {row['contact_name']},\n\ndanke für Ihre Anfrage..."
                st.text_area("Draft", draft, height=200)

else:
    st.info("No leads found. Waiting for ingest...")

if st.sidebar.button("Run Ingest (Mock)"):
    from ingest import ingest_mock_leads
    init_db()
    count = ingest_mock_leads()
    st.sidebar.success(f"Ingested {count} leads.")
    st.rerun()

if st.sidebar.button("Run Enrichment"):
    from enrich import run_enrichment
    run_enrichment()
    st.sidebar.success("Enrichment complete.")
    st.rerun()