app.py

"""
VynFi Streamlit Template — Generate, Explore, Visualize
"""

import os
import streamlit as st
import pandas as pd
import vynfi

st.set_page_config(page_title="VynFi Explorer", page_icon="📊", layout="wide")

st.title("📊 VynFi Data Explorer")

api_key = os.environ.get("VYNFI_API_KEY", "")
if not api_key:
    api_key = st.sidebar.text_input("VynFi API Key", type="password", placeholder="vf_live_...")

if not api_key:
    st.info("Enter your VynFi API key in the sidebar to get started. [Get a free key →](https://vynfi.com/signup)")
    st.stop()

client = vynfi.VynFi(api_key=api_key)

st.sidebar.header("Generate")

sector = st.sidebar.selectbox(
    "Sector",
    ["retail", "manufacturing", "financial_services", "banking_aml", "healthcare", "technology", "energy"],
    index=1,
)
rows = st.sidebar.slider("Rows", min_value=100, max_value=100_000, value=1000, step=100)
companies = st.sidebar.slider("Companies", min_value=1, max_value=20, value=3)
fraud_rate = st.sidebar.slider("Fraud rate", min_value=0.0, max_value=0.20, value=0.03, step=0.01)

st.sidebar.divider()
nl_description = st.sidebar.text_area(
    "Or describe what you want (Scale+)",
    placeholder="e.g. 6 months of P2P for a German manufacturer with IFRS",
    height=80,
)

generate = st.sidebar.button("Generate", type="primary", use_container_width=True)

if generate:
    with st.spinner("Generating..."):
        try:
            if nl_description.strip():
                resp = client._request("POST", "/v1/configs/from-description", json={
                    "description": nl_description.strip()
                })
                config = resp.get("config", {})
                st.sidebar.success(f"AI config: {config.get('sector')} / {config.get('rows')} rows")
            else:
                config = {
                    "sector": sector,
                    "rows": rows,
                    "companies": companies,
                    "fraudRate": fraud_rate,
                    "complexity": "medium",
                    "exportFormat": "json",
                    "output": {"numericMode": "native"},
                }

            job = client.jobs.generate_config(config=config)
            completed = client.jobs.wait(job.id, poll_interval=3.0, timeout=300.0)

            if completed.status != "completed":
                st.error(f"Job failed: {completed.error_detail}")
                st.stop()

            st.session_state["job_id"] = completed.id
            st.session_state["archive"] = client.jobs.download_archive(completed.id)
            st.success(f"Job {completed.id} completed")
        except Exception as e:
            st.error(f"Error: {e}")

if "archive" in st.session_state:
    archive = st.session_state["archive"]

    tab1, tab2, tab3, tab4 = st.tabs(["Journal Entries", "Documents", "Quality", "Files"])

    with tab1:
        st.subheader("Journal Entries")
        try:
            entries = archive.json("journal_entries.json")
            rows_flat = []
            for entry in entries[:500]:
                header = entry.get("header", entry)
                lines = entry.get("lines", [entry])
                for line in lines:
                    rows_flat.append({
                        "document_id": header.get("document_id", ""),
                        "company_code": header.get("company_code", ""),
                        "posting_date": header.get("posting_date", ""),
                        "document_type": header.get("document_type", ""),
                        "is_fraud": header.get("is_fraud", False),
                        "gl_account": line.get("gl_account", ""),
                        "debit_amount": line.get("debit_amount", 0),
                        "credit_amount": line.get("credit_amount", 0),
                    })
            df = pd.DataFrame(rows_flat)

            # Convert amounts to numeric (handles both string and native)
            for col in ["debit_amount", "credit_amount"]:
                df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0)

            st.metric("Line items", f"{len(df):,}")

            col1, col2 = st.columns(2)
            with col1:
                st.metric("Total debits", f"${df['debit_amount'].sum():,.2f}")
            with col2:
                fraud_count = int(df["is_fraud"].sum())
                pct = fraud_count / len(df) * 100 if len(df) > 0 else 0
                st.metric("Fraud entries", f"{fraud_count} ({pct:.1f}%)")

            st.dataframe(df, use_container_width=True, hide_index=True)
        except Exception as e:
            st.warning(f"Could not load journal entries: {e}")

    with tab2:
        st.subheader("Document Flows")
        for doc_type in ["purchase_orders", "goods_receipts", "vendor_invoices", "payments"]:
            try:
                docs = archive.json(f"document_flows/{doc_type}.json")
                st.write(f"**{doc_type.replace('_', ' ').title()}**: {len(docs)} records")
            except Exception:
                pass

    with tab3:
        st.subheader("Quality Metrics")
        try:
            analytics = client.jobs.analytics(st.session_state["job_id"])
            if hasattr(analytics, "benford_analysis") and analytics.benford_analysis:
                b = analytics.benford_analysis
                col1, col2, col3 = st.columns(3)
                col1.metric("Benford MAD", f"{b.mad:.4f}")
                col2.metric("Chi-squared", f"{b.chi_squared:.2f}")
                col3.metric("Conforms", "✅" if b.passes else "❌")
        except Exception:
            st.info("Quality analytics not available for this job.")

    with tab4:
        st.subheader("Archive Files")
        for f in archive.files():
            st.text(f)

else:
    st.info("Click **Generate** in the sidebar to create a dataset.")