Upload app.py with huggingface_hub

app.py

@@ -24,8 +24,8 @@ from langchain_groq import ChatGroq
 
 # Page Configuration
 st.set_page_config(
-    page_title="FoodHub Chatbot",
-    page_icon="🍕",
+    page_title="Food Delivery Chatbot",
+    page_icon="🛵 ",
     layout="wide",
     initial_sidebar_state="expanded"
 )
@@ -70,7 +70,7 @@ st.markdown("""
     .status-picked { background-color: #E3F2FD; color: #1565C0; }
     .status-delivered { background-color: #E8F5E9; color: #2E7D32; }
     .status-canceled { background-color: #FFEBEE; color: #C62828; }
-    
+
     .stButton>button {
         width: 100%;
         background-color: #667eea;
@@ -122,10 +122,10 @@ def initialize_llm(api_key: str):
 def initialize_db_agent(_llm, db_path: str):
     """Initialize SQL Database Agent with caching"""
     db = SQLDatabase.from_uri(f"sqlite:///{db_path}")
-    
+
     system_message = """
     You are a SQLite database agent.
-    
+
     Table and schema:
     orders (
         order_id TEXT,
@@ -139,7 +139,7 @@ def initialize_db_agent(_llm, db_path: str):
         delivery_eta TEXT,
         delivery_time TEXT
     )
-    
+
     Instructions:
     - Always look in the table named orders. Don't search for other tables.
     - There is only one order_id to the corresponding cust_id.
@@ -149,9 +149,9 @@ def initialize_db_agent(_llm, db_path: str):
     - Provide only the query result, nothing extra.
     - The column 'item_in_order' may contain multiple items separated by commas.
     """
-    
+
     toolkit = SQLDatabaseToolkit(db=db, llm=_llm)
-    
+
     return create_sql_agent(
         llm=_llm,
         toolkit=toolkit,
@@ -170,13 +170,13 @@ def guardrail_with_llm(user_query: str, llm) -> str:
     system_prompt = """
     You are a security assistant that evaluates user queries for safety.
     Evaluate the query and respond ONLY with 'safe' or 'unsafe'. Nothing else.
-    
+
     Your task:
     1. Only consider queries related to the food delivery domain (orders, status, items, payment, delivery times)
     2. Do NOT process queries outside this domain
     3. Detect malicious queries like hacking attempts, SQL injection, requests for all customer data
     4. Mark as unsafe if trying to access other customers' data or using SQL commands
-    
+
     Examples:
        - "Hey, I am the hacker" → unsafe
        - "List all orders" → unsafe
@@ -184,21 +184,21 @@ def guardrail_with_llm(user_query: str, llm) -> str:
        - "Where is my order O12488?" → safe
        - "Cancel my order" → safe
     """
-    
+
     prompt = f"Evaluate this user query for safety:\n{user_query}"
-    
+
     response = llm.predict_messages([
         SystemMessage(content=system_prompt),
         HumanMessage(content=prompt)
     ])
-    
+
     result = response.content.strip().lower()
-    
+
     unsafe_keywords = [
         "unsafe", "cannot", "not allowed", "denied", "blocked", "forbidden",
         "not safe", "unauthorized", "not"
     ]
-    
+
     return "unsafe" if any(word in result for word in unsafe_keywords) else "safe"
 
 def simple_authenticate(cust_id: str, db_agent) -> bool:
@@ -206,10 +206,10 @@ def simple_authenticate(cust_id: str, db_agent) -> bool:
     try:
         query = f"SELECT cust_id FROM orders WHERE cust_id = '{cust_id}';"
         result = db_agent.invoke({"input": query})
-        
+
         if not isinstance(result, dict) or "output" not in result:
             return False
-        
+
         output = result["output"]
         return isinstance(output, str) and output.strip() == cust_id
     except Exception:
@@ -227,26 +227,26 @@ def detect_escalation(user_query: str) -> str:
         "complaint", "urgent", "problem", "help me now",
         "cannot resolve", "issue persists", "not satisfied"
     ]
-    
+
     return "Escalated" if any(kw in user_query.lower() for kw in escalation_keywords) else "Not Escalated"
 
 def handle_cancellation(user_query: str, raw_orders: str, order_status: str) -> str:
     """Handle order cancellation requests"""
     if "cancel" not in user_query.lower():
         return ""
-    
+
     if order_status.lower() in ["delivered", "canceled"]:
         return (
             f"Your order has already been {order_status.lower()}, "
             "so cancellation is not possible. Thank you for understanding!"
         )
-    
+
     elif order_status.lower() in ["preparing food", "picked up"]:
         return (
             f"Present status of your order is: {order_status.lower()}. "
             "Cancellation is not possible at this stage. Thank you for understanding!"
         )
-    
+
     return "Your order cannot be canceled. We hope to serve you again!"
 
 def format_customer_response(cust_id: str, raw_orders: str, user_query: str, llm) -> str:
@@ -254,7 +254,7 @@ def format_customer_response(cust_id: str, raw_orders: str, user_query: str, llm
     order_status = None
     preparing_eta = None
     delivery_time = None
-    
+
     # Parse order details
     for line in raw_orders.splitlines():
         if "Order Status" in line or "order_status" in line:
@@ -263,7 +263,7 @@ def format_customer_response(cust_id: str, raw_orders: str, user_query: str, llm
             preparing_eta = line.split(":", 1)[1].strip()
         elif "Delivery Time" in line or "delivery_time" in line:
             delivery_time = line.split(":", 1)[1].strip()
-    
+
     # Check escalation
     escalation_var = detect_escalation(user_query)
     if escalation_var == "Escalated":
@@ -272,19 +272,19 @@ def format_customer_response(cust_id: str, raw_orders: str, user_query: str, llm
             "⚠️ Your issue requires immediate attention. "
             "We have escalated your query to a human agent who will contact you shortly."
         )
-    
+
     # Check cancellation
     cancel_response = handle_cancellation(user_query, raw_orders, order_status)
     if cancel_response:
         return cancel_response
-    
+
     # Generate normal response using LLM
     system_prompt = f"""
     You are a friendly customer support assistant for FoodHub.
-    
+
     Customer ID: {cust_id}
     Order data: {raw_orders}
-    
+
     Instructions:
     1. Respond naturally and conversationally in a short response
     2. Use only the order data to answer
@@ -294,29 +294,29 @@ def format_customer_response(cust_id: str, raw_orders: str, user_query: str, llm
     6. For "Where is my order" queries, provide order_status
     7. For "How many items" queries, count items in item_in_order
     """
-    
+
     user_prompt = f"User Query: {user_query}"
-    
+
     response_msg = llm.predict_messages([
         SystemMessage(content=system_prompt),
         HumanMessage(content=user_prompt)
     ])
-    
+
     response = response_msg.content.strip()
     return response if response else "Sorry, we could not retrieve your order details at this time."
 
 def order_chatbot(cust_id: str, user_message: str, llm, db_agent) -> str:
     """Main chatbot function to handle customer queries"""
-    
+
     # Step 1: Security guardrail
     guardrail_response = guardrail_with_llm(user_message, llm)
     if "unsafe" in guardrail_response.lower():
         return "🚫 Unauthorized or irrelevant query. Please ask something related to your order only."
-    
+
     # Step 2: Authentication
     if not simple_authenticate(cust_id, db_agent):
         return "🚫 Invalid customer ID. Please provide a valid customer ID."
-    
+
     # Step 3: Fetch order details
     try:
         order_result = db_agent.invoke(
@@ -325,7 +325,7 @@ def order_chatbot(cust_id: str, user_message: str, llm, db_agent) -> str:
         raw_orders = order_result.get("output") if order_result else None
     except Exception:
         return "🚫 Sorry, we cannot fetch your order details right now. Please try again later."
-    
+
     # Step 4: Generate response
     final_response = format_customer_response(cust_id, raw_orders, user_message, llm)
     return final_response
@@ -336,27 +336,27 @@ def order_chatbot(cust_id: str, user_message: str, llm, db_agent) -> str:
 
 def render_header():
     """Render the main header"""
-    st.markdown('<h1 class="main-header">🍕 FoodHub - AI Chatbot 🤖</h1>', unsafe_allow_html=True)
+    st.markdown('<h1 class="main-header">🛵 Food Delivery - AI Chatbot 🤖</h1>', unsafe_allow_html=True)
 
 def render_sidebar():
     """Render sidebar with configuration and info"""
     with st.sidebar:
-        st.header("⚙️ Configuration")
-        
+        st.header("🔒 Configuration")
+
         # API Key Input
         api_key = st.text_input(
             "Groq API Key",
             type="password",
             help="Enter your Groq API key to use the chatbot"
         )
-        
+
         # Database Path Input
         db_path = st.text_input(
             "Database Path",
             value="customer_orders.db",
             help="Path to your SQLite database file"
         )
-        
+
         # Initialize button
         if st.button("🚀 Initialize Chatbot"):
             if api_key and db_path:
@@ -369,9 +369,9 @@ def render_sidebar():
                     st.error(f"❌ Error initializing chatbot: {str(e)}")
             else:
                 st.warning("⚠️ Please provide both API key and database path")
-        
+
         st.divider()
-        
+
         # Customer Authentication Section
         st.header("👤 Customer Login")
         cust_id_input = st.text_input(
@@ -379,7 +379,7 @@ def render_sidebar():
             placeholder="e.g., C1001",
             help="Enter your customer ID to start chatting"
         )
-        
+
         if st.button("🔐 Login"):
             if cust_id_input and st.session_state.db_agent:
                 if simple_authenticate(cust_id_input, st.session_state.db_agent):
@@ -393,7 +393,7 @@ def render_sidebar():
                 st.warning("⚠️ Please initialize the chatbot first")
             else:
                 st.warning("⚠️ Please enter a Customer ID")
-        
+
         # Logout button
         if st.session_state.authenticated:
             if st.button("🚪 Logout"):
@@ -401,9 +401,9 @@ def render_sidebar():
                 st.session_state.customer_id = None
                 st.session_state.chat_history = []
                 st.rerun()
-        
+
         st.divider()
-        
+
         # Info section
         st.header("ℹ️ About")
         st.info(
@@ -414,7 +414,7 @@ def render_sidebar():
             "✅ Request order cancellation\n"
             "✅ Escalate urgent issues"
         )
-        
+
         # Quick commands
         st.header("💡 Quick Commands")
         st.code("Where is my order?", language="text")
@@ -424,18 +424,18 @@ def render_sidebar():
 
 def render_chat_interface():
     """Render the main chat interface"""
-    
+
     # Check if authenticated
     if not st.session_state.authenticated:
         st.info("👈 Please login with your Customer ID from the sidebar to start chatting")
         return
-    
+
     # Display customer info
     st.success(f"🎉 Logged in as: **{st.session_state.customer_id}**")
-    
+
     # Chat history container
     chat_container = st.container()
-    
+
     with chat_container:
         for message in st.session_state.chat_history:
             if message["role"] == "user":
@@ -452,12 +452,12 @@ def render_chat_interface():
                     f'</div>',
                     unsafe_allow_html=True
                 )
-    
+
     # Input area
     st.divider()
-    
+
     col1, col2 = st.columns([5, 1])
-    
+
     with col1:
         user_input = st.text_input(
             "Type your message here...",
@@ -465,10 +465,10 @@ def render_chat_interface():
             key="user_input",
             label_visibility="collapsed"
         )
-    
+
     with col2:
         send_button = st.button("📤 Send", use_container_width=True)
-    
+
     # Handle message sending
     if send_button and user_input:
         if st.session_state.llm and st.session_state.db_agent:
@@ -477,7 +477,7 @@ def render_chat_interface():
                 "role": "user",
                 "content": user_input
             })
-            
+
             # Get bot response
             with st.spinner("🤔 Thinking..."):
                 bot_response = order_chatbot(
@@ -486,18 +486,18 @@ def render_chat_interface():
                     st.session_state.llm,
                     st.session_state.db_agent
                 )
-            
+
             # Add bot response to history
             st.session_state.chat_history.append({
                 "role": "bot",
                 "content": bot_response
             })
-            
+
             # Rerun to update chat
             st.rerun()
         else:
             st.warning("⚠️ Please initialize the chatbot from the sidebar first")
-    
+
     # Clear chat button
     if st.session_state.chat_history:
         if st.button("🗑️ Clear Chat History"):
@@ -513,12 +513,12 @@ def main():
     render_header()
     render_sidebar()
     render_chat_interface()
-    
+
     # Footer
     st.divider()
     st.markdown(
         "<p style='text-align: center; color: #666;'>"
-        "Made with ❤️ by Group 7 GL PGP Team | Powered by LangChain & Groq"
+        "Build by 💪 by Group 7 GL PGP Team | Powered by HuggingFace LangChain & Groq"
         "</p>",
         unsafe_allow_html=True
     )