app.py

import gradio as gr
import requests
import os
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "HuggingFaceH4/zephyr-7b-beta"  # Change here!
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
llm = pipeline("text-generation", model=model, tokenizer=tokenizer)


# --- SAP Sales Order Header tool ---
def query_sales_order_header():
    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")
    headers = {
        "APIKey": api_key,
        "Accept": "application/json"
    }
    try:
        r = requests.get(api_url, headers=headers, timeout=10)
        r.raise_for_status()
        data = r.json()
        results = data.get('d', {}).get('results', [])
        if not results:
            return "No sales orders found."
        summaries = []
        for order in results:
            summaries.append(
                f"Order: {order['SalesOrder']} | "
                f"Type: {order['SalesOrderType']} | "
                f"Org: {order['SalesOrganization']} | "
                f"Date: {order['SalesOrderDate']} | "
                f"SoldTo: {order['SoldToParty']} | "
                f"Net: {order['TotalNetAmount']} {order['TransactionCurrency']} | "
                f"Status: {order['OverallSDProcessStatus']}"
            )
        return "\n".join(summaries)
    except Exception as e:
        return f"Error fetching Sales Orders: {e}"

# --- Main chat function ---
def chat_agent(message, history):
    # Rudimentary tool use: check for sales order question
    tool_trigger = ["sales order", "sap order", "sap sales"]
    if any(kw in message.lower() for kw in tool_trigger):
        tool_response = query_sales_order_header()
        response = f"(SAP Sales Orders)\n{tool_response}"
    else:
        # Pure LLM response
        response = llm(message, max_new_tokens=256)[0]["generated_text"]
    history = history or []
    history.append((message, response))
    return history, history

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown(
        """
        # SAP Sales Order Chat Agent  
        Ask about SAP sales orders or general questions.  
        Example:  
        - `Show me the latest SAP sales orders.`  
        - `Tell me about SAP order values.`  
        """
    )
    chatbot = gr.Chatbot()
    txt = gr.Textbox(label="Your question")
    clear = gr.Button("Clear chat")

    txt.submit(chat_agent, [txt, chatbot], [chatbot, chatbot])
    clear.click(lambda: ([], []), None, [chatbot, chatbot])

demo.launch()