Update app.py

app.py

@@ -3,15 +3,14 @@ import requests
 import os
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-# ---- Hugging Face model: use a public chat/instruct model ----
-model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+# -- Use a tiny, chat-tuned model --
+model_id = "Qwen/Qwen1.5-0.5B-Chat"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 llm = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 # --- SAP Sales Order Header tool ---
 def fetch_sales_order_headers():
-    """Fetches the top 5 SAP sales orders from the sandbox API and returns as a list of dicts."""
     api_url = "https://sandbox.api.sap.com/s4hanacloud/sap/opu/odata/sap/API_SALES_ORDER_SRV/A_SalesOrder?$top=5&$inlinecount=allpages"
     api_key = os.getenv("SAP_SANDBOX_API_KEY", "YOUR_API_KEY")  # Set in Space secrets!
     headers = {
@@ -30,9 +29,8 @@ def fetch_sales_order_headers():
         return f"Error fetching Sales Orders: {e}"
 
 def format_sales_orders_for_llm(orders):
-    """Converts the SAP sales order list to readable context for the LLM."""
     if isinstance(orders, str):
-        return orders  # Pass through error message
+        return orders  # Error
     context = "Here are the latest SAP sales orders:\n"
     for i, order in enumerate(orders, 1):
         context += (
@@ -47,37 +45,27 @@ def format_sales_orders_for_llm(orders):
     return context
 
 def chat_agent(message, history):
-    # Step 1: Fetch SAP data every time
     sales_orders = fetch_sales_order_headers()
     context = format_sales_orders_for_llm(sales_orders)
-    # Step 2: Prompt LLM with both the question and the context
     prompt = (
         f"{context}\n"
         f"User asked: {message}\n"
         "Based on the above SAP sales orders, answer the user's question as accurately as possible. "
-        "If the question asks for sorting, filtering, maximum values, customer-specific info, or other details, do it based on the orders above. "
-        "If the question is unclear, summarize the available sales orders."
+        "If the question asks for sorting, filtering, or summarizing, do it based on the data above."
     )
-    # Step 3: Generate LLM answer
     llm_output = llm(prompt, max_new_tokens=256)[0]["generated_text"]
-    # Remove the prompt from the response if present
+    # Remove the prompt if echoed
     response = llm_output.replace(prompt, "").strip()
     history = history or []
     history.append((message, response))
     return history, history
 
-# ---- Gradio UI ----
 with gr.Blocks() as demo:
     gr.Markdown(
         """
-        # SAP Sales Order Chat Agent (Smart Reasoning)
-        Ask about SAP sales orders, values, filtering, sorting, etc.  
-        Example questions:
-        - `Show me the latest SAP sales orders.`
-        - `Tell me about SAP order values.`
-        - `Show me top 2 sales orders with maximum value.`
-        - `Which order has the highest net value?`
-        - `List orders by customer.`
+        # SAP Sales Order Chat Agent (Small Chat Model)
+        - Asks about SAP sales orders, values, filtering, sorting, etc.
+        - Example: `Show me top 2 sales orders by value.`
         """
     )
     chatbot = gr.Chatbot()