import streamlit as st
import pandas as pd
from huggingface_hub import InferenceClient
import re
from datetime import datetime

# Initialize hosted inference client
client = InferenceClient(model="google/flan-t5-base")

# Simulated chart of accounts mapping
account_map = {
    "rent": "60001",
    "utilities": "60002",
    "capital": "30000",
    "cash": "10001",
    "bank": "10002",
    "sales": "40001",
    "supplies": "50001",
    "salary": "50002"
}

# Session state
if "gl_entries" not in st.session_state:
    st.session_state.gl_entries = []
if "company_name" not in st.session_state:
    st.session_state.company_name = "My Company"

# Streamlit UI
st.set_page_config(page_title="AI ERP App", layout="wide")
st.title(f"📘 {st.session_state.company_name} Ledger - AI-Powered ERP")
prompt = st.text_input("📌 Enter your accounting instruction:")

def extract_amount(prompt):
    match = re.search(r'\$?(\d{1,3}(,\d{3})*|\d+)(\.\d{1,2})?', prompt)
    return float(match.group().replace(',', '').replace('$', '')) if match else 0

def extract_date(prompt):
    date_match = re.search(r'(\d{1,2}[/-]\d{1,2}[/-]\d{2,4})|today', prompt.lower())
    if date_match:
        if "today" in date_match.group().lower():
            return datetime.today().strftime("%Y-%m-%d")
        try:
            return datetime.strptime(date_match.group(), "%d/%m/%Y").strftime("%Y-%m-%d")
        except:
            try:
                return datetime.strptime(date_match.group(), "%d-%m-%Y").strftime("%Y-%m-%d")
            except:
                return datetime.today().strftime("%Y-%m-%d")
    return datetime.today().strftime("%Y-%m-%d")

def handle_gl_entry(prompt):
    prompt_lower = prompt.lower()
    amount = extract_amount(prompt)
    date_str = extract_date(prompt)
    description = ""

    # Capture new company name
    company_match = re.search(r"company with a name of ([\w\s&.-]+)", prompt_lower)
    if company_match:
        st.session_state.company_name = company_match.group(1).strip().upper()

    if any(word in prompt_lower for word in ["invest", "capital", "start"]):
        description = "Owner Capital Contribution"
        debit_account, credit_account = "cash", "capital"
    elif "rent" in prompt_lower:
        description = "Rent Expense"
        debit_account, credit_account = "rent", "cash"
    elif "utilities" in prompt_lower:
        description = "Utilities Expense"
        debit_account, credit_account = "utilities", "cash"
    elif any(word in prompt_lower for word in ["sale", "revenue"]):
        description = "Sales Revenue"
        debit_account, credit_account = "cash", "sales"
    elif "supplies" in prompt_lower:
        description = "Supplies Purchase"
        debit_account, credit_account = "supplies", "cash"
    elif "salary" in prompt_lower or "payroll" in prompt_lower:
        description = "Salary Expense"
        debit_account, credit_account = "salary", "cash"
    else:
        return pd.DataFrame([{"Date": date_str, "Description": "Unrecognized Entry", "Account Code": "N/A", "Account Type": "N/A", "Debit": 0, "Credit": 0}])

    def format_code(name):
        return f"01-102-001-001-{account_map[name]}-000"

    entry = [
        {
            "Date": date_str,
            "Description": description,
            "Account Code": format_code(debit_account),
            "Account Type": debit_account.title(),
            "Debit": amount,
            "Credit": 0
        },
        {
            "Date": date_str,
            "Description": f"Offset for {description.lower()}",
            "Account Code": format_code(credit_account),
            "Account Type": credit_account.title(),
            "Debit": 0,
            "Credit": amount
        }
    ]
    st.session_state.gl_entries.extend(entry)
    return pd.DataFrame(entry)

# Buttons
col1, col2 = st.columns([1, 1])
with col1:
    st.download_button("📥 Download All Entries (CSV)",
                       data=pd.DataFrame(st.session_state.gl_entries).to_csv(index=False),
                       file_name="gl_entries.csv",
                       mime="text/csv")
with col2:
    if st.button("🗑️ Delete All Records"):
        st.session_state.gl_entries = []
        st.success("✅ All records have been deleted.")

# Handle input
if prompt:
    result = handle_gl_entry(prompt)
    st.dataframe(result)

# Display ledger
if st.session_state.gl_entries:
    st.subheader("📊 All Journal Entries")
    st.dataframe(pd.DataFrame(st.session_state.gl_entries))