Update app.py

app.py

@@ -268,90 +268,86 @@
 
 # demo.launch()
 
+
+
 import gradio as gr
 from transformers import pipeline
 import requests
-import os
 import pandas as pd
 import time
-import io
-from contextlib import redirect_stdout
 
 # =========================
-# ENV VARIABLES (use HF Secrets)
+# SAP CONFIG
 # =========================
-client_id = "sb-cap1-3c4588e0trial-dev!t617058"
-client_secret = "acbe78be-ead5-4b12-b3b4-32fdb27d0f5f$hFj-hDXxwHkNHC-CAvv-OKSr3KH96nLL4KqwIg7M8D8="
-token_url = "https://3c4588e0trial.authentication.us10.hana.ondemand.com/oauth/token"
+client_id = "YOUR_CLIENT_ID"
+client_secret = "YOUR_CLIENT_SECRET"
+token_url = "YOUR_TOKEN_URL"
 
-cap_service_url_customers = "https://3c4588e0trial-dev-cap1-srv.cfapps.us10-001.hana.ondemand.com/odata/v4/sales/Customers?$top=2"
-cap_service_url_products = "https://3c4588e0trial-dev-cap1-srv.cfapps.us10-001.hana.ondemand.com/odata/v4/sales/Products?$top=2"
-cap_service_url_saleorders = "https://3c4588e0trial-dev-cap1-srv.cfapps.us10-001.hana.ondemand.com/odata/v4/sales/SalesOrders?$top=2"
-cap_service_url_saleorderitems = "https://3c4588e0trial-dev-cap1-srv.cfapps.us10-001.hana.ondemand.com/odata/v4/sales/SalesOrderItems?$top=2"
+cap_service_url_customers = "YOUR_URL/Customers"
+cap_service_url_products = "YOUR_URL/Products"
+cap_service_url_saleorders = "YOUR_URL/SalesOrders"
+cap_service_url_saleorderitems = "YOUR_URL/SalesOrderItems"
 
 # =========================
-# GLOBAL VARIABLES
+# GLOBAL CACHE
 # =========================
 access_token = None
-cached_customers = None
-cached_products = None
-cached_salesorders = None
-cached_salesorderitems = None
+cached_data = None
 last_refresh = 0
 
 # =========================
-# LOAD MODEL (once) - Smaller & faster version recommended
+# LOAD SMALL FAST MODEL
 # =========================
 print("Loading model...")
 pipe = pipeline(
     "text-generation",
-    model="Qwen/Qwen2.5-0.5B-Instruct",   # Change to 0.5B for faster speed (still very good)
-    # model="Qwen/Qwen2.5-1.5B-Instruct", # Uncomment if you prefer 1.5B
+    model="Qwen/Qwen2.5-0.5B-Instruct",  # ⚡ faster than 1.5B
     device="cpu"
 )
 
 # =========================
-# TOKEN FUNCTION
+# TOKEN GENERATION
 # =========================
-def generate_sap_xsuaa_token():
+def generate_token():
     global access_token
-    print("Generating SAP token...")
-    auth_response = requests.post(
+
+    response = requests.post(
         token_url,
         data={"grant_type": "client_credentials"},
         auth=(client_id, client_secret)
     )
-    if auth_response.status_code != 200:
-        print("Token Error:", auth_response.text)
+
+    if response.status_code != 200:
         return None
-    access_token = auth_response.json().get("access_token")
-    print("Token generated!")
+
+    access_token = response.json().get("access_token")
     return access_token
 
 # =========================
-# FETCH SAP DATA
+# FETCH DATA
 # =========================
-def fetch_sap_data():
+def fetch_data():
     global access_token
+
     if not access_token:
-        generate_sap_xsuaa_token()
-    
+        generate_token()
+
     headers = {
         "Authorization": f"Bearer {access_token}",
         "Accept": "application/json"
     }
-    
+
     res1 = requests.get(cap_service_url_customers, headers=headers)
     res2 = requests.get(cap_service_url_products, headers=headers)
     res3 = requests.get(cap_service_url_saleorders, headers=headers)
     res4 = requests.get(cap_service_url_saleorderitems, headers=headers)
 
     # Retry if token expired
-    if res1.status_code in [400, 401, 403]:
-        print("Token expired. Regenerating...")
+    if res1.status_code in [401, 403]:
         access_token = None
-        generate_sap_xsuaa_token()
+        generate_token()
         headers["Authorization"] = f"Bearer {access_token}"
+
         res1 = requests.get(cap_service_url_customers, headers=headers)
         res2 = requests.get(cap_service_url_products, headers=headers)
         res3 = requests.get(cap_service_url_saleorders, headers=headers)
@@ -359,106 +355,116 @@ def fetch_sap_data():
 
     df_customers = pd.DataFrame(res1.json()["value"])
     df_products = pd.DataFrame(res2.json()["value"])
-    df_saleorders = pd.DataFrame(res3.json()["value"])
-    df_saleorderitems = pd.DataFrame(res4.json()["value"])
+    df_orders = pd.DataFrame(res3.json()["value"])
+    df_items = pd.DataFrame(res4.json()["value"])
 
-    # Keep only important columns
-    df_customers = df_customers[["ID", "name", "country", "industry"]]
-    df_products = df_products[["ID", "name", "category", "price", "currency"]]
-    df_saleorders = df_saleorders[["ID", "customer_ID", "orderDate", "status"]]
-    df_saleorderitems = df_saleorderitems[["ID", "parent_ID", "product_ID", "quantity", "netAmount"]]
+    # Select columns
+    df_customers = df_customers[["ID","name","country","industry"]]
+    df_products = df_products[["ID","name","category","price","currency"]]
+    df_orders = df_orders[["ID","customer_ID","orderDate","status"]]
+    df_items = df_items[["ID","parent_ID","product_ID","quantity","netAmount"]]
 
-    return df_customers, df_products, df_saleorders, df_saleorderitems
+    return df_customers, df_products, df_orders, df_items
 
 # =========================
-# CACHE LOGIC (refresh every 5 minutes)
+# CACHE
 # =========================
-def get_cached_data():
-    global cached_customers, cached_products, cached_salesorders, cached_salesorderitems, last_refresh
-    if time.time() - last_refresh > 300 or cached_customers is None:  # 5 minutes
-        print("Refreshing SAP data...")
-        cached_customers, cached_products, cached_salesorders, cached_salesorderitems = fetch_sap_data()
+def get_data():
+    global cached_data, last_refresh
+
+    if cached_data is None or time.time() - last_refresh > 300:
+        cached_data = fetch_data()
         last_refresh = time.time()
-    return cached_customers, cached_products, cached_salesorders, cached_salesorderitems
+
+    return cached_data
+
+# =========================
+# FORMAT FUNCTION
+# =========================
+def format_table(df):
+    if df.empty:
+        return "❌ No data found"
+    return df.to_markdown(index=False)
+
+# =========================
+# LLM (ONLY FOR COMPLEX)
+# =========================
+def call_llm(question, context):
+    messages = [
+        {"role": "system", "content": "Answer strictly based on data. Be short."},
+        {"role": "user", "content": f"Data:\n{context}\n\nQuestion: {question}"}
+    ]
+
+    prompt = pipe.tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    result = pipe(
+        prompt,
+        max_new_tokens=60,
+        do_sample=False   # ⚡ fast + deterministic
+    )
+
+    return result[0]["generated_text"].strip()
 
 # =========================
-# MAIN FUNCTION - LLM generates pandas code + execute
+# MAIN FUNCTION
 # =========================
-def generate_response(user_prompt):
+def generate_response(user_input):
     try:
-        # Get cached SAP data
-        df_customers, df_products, df_saleorders, df_saleorderitems = get_cached_data()
-
-        # System prompt for code generation (strict, no explanations)
-        system_msg = """You are a pandas expert for SAP data.
-        Output ONLY valid Python code. No explanations, no markdown, no ```python blocks.
-        Use the DataFrames that already exist: df_saleorders, df_customers, df_products, df_saleorderitems.
-        Always end with a print() that shows the result as a clean markdown table using .to_markdown(index=False).
-        If no records match, just print: "No records found."""
-
-        user_msg = f"""User question: {user_prompt} Write the pandas code to answer this question."""
-
-        messages = [
-            {"role": "system", "content": system_msg},
-            {"role": "user", "content": user_msg}
-        ]
-
-        # Apply correct chat template
-        prompt = pipe.tokenizer.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True
-        )
-
-        # Generate code (deterministic for accuracy)
-        result = pipe(
-            prompt,
-            max_new_tokens=150,
-            do_sample=False,
-            temperature=0.0,
-            return_full_text=False
-        )
-
-        generated_code = result[0]["generated_text"].strip()
-
-        # Clean possible code fences
-        if generated_code.startswith("```"):
-            generated_code = generated_code.split("\n", 1)[1]
-        if "```" in generated_code:
-            generated_code = generated_code.split("```")[0]
-
-        # Execute the generated code safely
-        local_vars = {
-            "df_customers": df_customers,
-            "df_products": df_products,
-            "df_saleorders": df_saleorders,
-            "df_saleorderitems": df_saleorderitems,
-            "pd": pd
-        }
-
-        output_buffer = io.StringIO()
-        with redirect_stdout(output_buffer):
-            exec(generated_code, {"__builtins__": {}}, local_vars)
-
-        response = output_buffer.getvalue().strip()
-        return response if response else "No records found."
+        df_customers, df_products, df_orders, df_items = get_data()
+        query = user_input.lower()
+
+        # =====================
+        # DIRECT (NO LLM)
+        # =====================
+        if "customer" in query:
+            return "### 👥 Customers\n\n" + format_table(df_customers)
+
+        elif "product" in query:
+            return "### 📦 Products\n\n" + format_table(df_products)
+
+        elif "order item" in query:
+            return "### 🧾 Order Items\n\n" + format_table(df_items)
+
+        elif "order" in query:
+            return "### 📑 Orders\n\n" + format_table(df_orders)
+
+        # =====================
+        # 🤖 COMPLEX QUERY
+        # =====================
+        else:
+            # Send minimal context
+            context = df_orders.head(5).to_markdown(index=False)
+
+            answer = call_llm(user_input, context)
+
+            return f"### 🤖 Answer\n\n{answer}"
 
     except Exception as e:
-        return f"Error: {str(e)}\n\n(The generated code had an issue. Try rephrasing your question.)"
+        return f"❌ Error: {str(e)}"
 
 # =========================
-# GRADIO UI + API
+# GRADIO UI
 # =========================
 with gr.Blocks() as demo:
-    gr.Markdown("# SAP Assistant (Fast & Accurate)")
-    user_input = gr.Textbox(label="User Question", placeholder="e.g. give me those orders where status is Completed")
-    output = gr.Markdown(label="Response")   # Changed to Markdown for better table rendering
+    gr.Markdown("# 🚀 SAP Smart Assistant")
+
+    user_input = gr.Textbox(
+        label="Ask your question",
+        placeholder="e.g. show customers, list products..."
+    )
+
+    output = gr.Markdown(label="Response")
+
     btn = gr.Button("Generate")
 
     btn.click(
         fn=generate_response,
-        inputs=[user_input],
-        outputs=output,
-        api_name="predict"
+        inputs=user_input,
+        outputs=output
     )
 
-demo.queue()
 demo.launch()