Update app.py

app.py

@@ -2,6 +2,7 @@ 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")
@@ -18,84 +19,84 @@ account_map = {
     "salary": "50002"
 }
 
-# Simulated business segments
-segment = {
-    "company": "01",
-    "business_type": "102",
-    "location": "001",
-    "cost_center": "001",
-    "future": "000"
-}
-
-# Session state to store entries
+# 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"
 
-# Inference logic
+# 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 parse_prompt(prompt):
-    return client.text_generation(prompt=f"Extract accounting entry: {prompt}", max_new_tokens=50).strip()
+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 = 0
-    account_name = ""
+    amount = extract_amount(prompt)
+    date_str = extract_date(prompt)
+    description = ""
 
-    # Extract amount using regex
-    amount_match = re.search(r'(\d{1,3}(,\d{3})*|\d+)(\.\d{1,2})?', prompt)
-    if amount_match:
-        amount = float(amount_match.group().replace(',', ''))
+    # 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()
 
-    # Identify transaction type
     if any(word in prompt_lower for word in ["invest", "capital", "start"]):
-        account_name = "capital"
         description = "Owner Capital Contribution"
-        debit_account = "cash"
-        credit_account = account_name
+        debit_account, credit_account = "cash", "capital"
     elif "rent" in prompt_lower:
-        account_name = "rent"
         description = "Rent Expense"
-        debit_account = account_name
-        credit_account = "cash"
+        debit_account, credit_account = "rent", "cash"
     elif "utilities" in prompt_lower:
-        account_name = "utilities"
         description = "Utilities Expense"
-        debit_account = account_name
-        credit_account = "cash"
+        debit_account, credit_account = "utilities", "cash"
     elif any(word in prompt_lower for word in ["sale", "revenue"]):
-        account_name = "sales"
         description = "Sales Revenue"
-        debit_account = "cash"
-        credit_account = account_name
+        debit_account, credit_account = "cash", "sales"
     elif "supplies" in prompt_lower:
-        account_name = "supplies"
         description = "Supplies Purchase"
-        debit_account = account_name
-        credit_account = "cash"
+        debit_account, credit_account = "supplies", "cash"
     elif "salary" in prompt_lower or "payroll" in prompt_lower:
-        account_name = "salary"
         description = "Salary Expense"
-        debit_account = account_name
-        credit_account = "cash"
+        debit_account, credit_account = "salary", "cash"
     else:
-        description = "Unrecognized Entry"
-        return pd.DataFrame([{"Date": "2025-04-01", "Description": description, "Account Code": "N/A", "Debit": 0, "Credit": 0}])
+        return pd.DataFrame([{"Date": date_str, "Description": "Unrecognized Entry", "Account Code": "N/A", "Account Type": "N/A", "Debit": 0, "Credit": 0}])
 
-    debit_code = f"{segment['company']}-{segment['business_type']}-{segment['location']}-{segment['cost_center']}-{account_map[debit_account]}-{segment['future']}"
-    credit_code = f"{segment['company']}-{segment['business_type']}-{segment['location']}-{segment['cost_center']}-{account_map[credit_account]}-{segment['future']}"
+    def format_code(name):
+        return f"01-102-001-001-{account_map[name]}-000"
 
     entry = [
         {
-            "Date": "2025-04-01",
+            "Date": date_str,
             "Description": description,
-            "Account Code": debit_code,
+            "Account Code": format_code(debit_account),
+            "Account Type": debit_account.title(),
             "Debit": amount,
             "Credit": 0
         },
         {
-            "Date": "2025-04-01",
+            "Date": date_str,
             "Description": f"Offset for {description.lower()}",
-            "Account Code": credit_code,
+            "Account Code": format_code(credit_account),
+            "Account Type": credit_account.title(),
             "Debit": 0,
             "Credit": amount
         }
@@ -103,25 +104,24 @@ def handle_gl_entry(prompt):
     st.session_state.gl_entries.extend(entry)
     return pd.DataFrame(entry)
 
-# Streamlit UI
-st.set_page_config(page_title="AI ERP App", layout="wide")
-st.title("AI-Powered ERP Accounting App")
-prompt = st.text_input("📌 Enter your accounting instruction:")
-
-download = 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")
-
-delete_records = st.button("🗑️ Delete All Records")
-if delete_records:
-    st.session_state.gl_entries = []
-    st.success("✅ All records have been deleted.")
-
+# 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))