Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import pipeline
+
+# Initialize LLM pipeline
+parser = pipeline("text2text-generation", model="google/flan-t5-base")
+
+# Simulated chart of accounts mapping
+account_map = {
+    "rent": "60001",
+    "utilities": "60002",
+    "cash": "10001",
+    "bank": "10002"
+}
+
+# Simulated business segments
+segment = {
+    "company": "01",
+    "business_type": "102",  # Grocery Store
+    "location": "001",
+    "cost_center": "001",
+    "future": "000"
+}
+
+def parse_prompt(prompt):
+    result = parser(f"Extract accounting entry: {prompt}")[0]['generated_text']
+    return result  # Simplified: in real app, use structured parsing
+
+def handle_gl_entry(prompt):
+    # Simulate parsing response
+    if "rent" in prompt.lower():
+        account_name = "rent"
+        amount = 500  # Normally extracted from LLM
+    else:
+        account_name = "utilities"
+        amount = 300
+
+    expense_account = account_map[account_name]
+    cash_account = account_map["cash"]
+
+    expense_account_code = f"{segment['company']}-{segment['business_type']}-{segment['location']}-{segment['cost_center']}-{expense_account}-{segment['future']}"
+    cash_account_code = f"{segment['company']}-{segment['business_type']}-{segment['location']}-{segment['cost_center']}-{cash_account}-{segment['future']}"
+
+    entry = pd.DataFrame([
+        {
+            "Date": "2025-04-01",
+            "Description": f"{account_name.title()} Expense",
+            "Account Code": expense_account_code,
+            "Debit": amount,
+            "Credit": 0
+        },
+        {
+            "Date": "2025-04-01",
+            "Description": f"Payment for {account_name}",
+            "Account Code": cash_account_code,
+            "Debit": 0,
+            "Credit": amount
+        }
+    ])
+    return entry
+
+# Streamlit UI
+st.title("AI ERP Chat - MVP")
+prompt = st.text_input("Enter your accounting instruction:")
+
+if prompt:
+    result = handle_gl_entry(prompt)
+    st.dataframe(result)  # Show result as a DataFrame