Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from huggingface_hub import InferenceClient
+import re
+
+# 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"
+}
+
+# Simulated business segments
+segment = {
+    "company": "01",
+    "business_type": "102",
+    "location": "001",
+    "cost_center": "001",
+    "future": "000"
+}
+
+# Session state to store entries
+if "gl_entries" not in st.session_state:
+    st.session_state.gl_entries = []
+
+# Inference logic
+
+def parse_prompt(prompt):
+    return client.text_generation(prompt=f"Extract accounting entry: {prompt}", max_new_tokens=50).strip()
+
+def handle_gl_entry(prompt):
+    prompt_lower = prompt.lower()
+    amount = 0
+    account_name = ""
+
+    # 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(',', ''))
+
+    # 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
+    elif "rent" in prompt_lower:
+        account_name = "rent"
+        description = "Rent Expense"
+        debit_account = account_name
+        credit_account = "cash"
+    elif "utilities" in prompt_lower:
+        account_name = "utilities"
+        description = "Utilities Expense"
+        debit_account = account_name
+        credit_account = "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
+    elif "supplies" in prompt_lower:
+        account_name = "supplies"
+        description = "Supplies Purchase"
+        debit_account = account_name
+        credit_account = "cash"
+    elif "salary" in prompt_lower or "payroll" in prompt_lower:
+        account_name = "salary"
+        description = "Salary Expense"
+        debit_account = account_name
+        credit_account = "cash"
+    else:
+        description = "Unrecognized Entry"
+        return pd.DataFrame([{"Date": "2025-04-01", "Description": description, "Account Code": "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']}"
+
+    entry = [
+        {
+            "Date": "2025-04-01",
+            "Description": description,
+            "Account Code": debit_code,
+            "Debit": amount,
+            "Credit": 0
+        },
+        {
+            "Date": "2025-04-01",
+            "Description": f"Offset for {description.lower()}",
+            "Account Code": credit_code,
+            "Debit": 0,
+            "Credit": amount
+        }
+    ]
+    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.")
+
+if prompt:
+    result = handle_gl_entry(prompt)
+    st.dataframe(result)
+
+if st.session_state.gl_entries:
+    st.subheader("📊 All Journal Entries")
+    st.dataframe(pd.DataFrame(st.session_state.gl_entries))