Update app.py

app.py

@@ -1,9 +1,9 @@
 import streamlit as st
 import pandas as pd
-from transformers import pipeline
+from huggingface_hub import InferenceClient
 
-# Initialize LLM pipeline
-parser = pipeline("text2text-generation", model="google/flan-t5-base")
+# Initialize hosted inference client
+client = InferenceClient(model="google/flan-t5-base")
 
 # Simulated chart of accounts mapping
 account_map = {
@@ -23,8 +23,8 @@ segment = {
 }
 
 def parse_prompt(prompt):
-    result = parser(f"Extract accounting entry: {prompt}")[0]['generated_text']
-    return result  # Simplified: in real app, use structured parsing
+    response = client.text_generation(prompt=f"Extract accounting entry: {prompt}", max_new_tokens=50)
+    return response
 
 def handle_gl_entry(prompt):
     # Simulate parsing response