Update app.py

app.py

@@ -1,22 +1,32 @@
 import streamlit as st
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from pyngrok import ngrok
 import random
-import re
 
-# ✅ Set up ngrok
-ngrok.set_auth_token("2ppPfZORNKDM4PrFh24fot8Dgmu_7tfFX5fvm1gHnzoyAY236")
-public_url = ngrok.connect(8501).public_url
-
-# ✅ Load AI Model
-model_name = "deepseek-ai/deepseek-llm-7b-chat"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name, torch_dtype=torch.float16, device_map="auto", offload_folder="offload_weights"
+# Set up the page title and description
+st.set_page_config(page_title="OrderBot - AI Chatbot", page_icon="🛒")
+st.title("🛒 OrderBot - AI Chatbot")
+st.markdown(
+    """
+    ### Hey there! I am OrderBot, an AI-driven assistant powered by the DeepSeek-7B Chat model.
+    I am designed for seamless natural language interaction. Leveraging advanced machine learning,
+    I process and respond to human input with precision and efficiency.
+    """
 )
 
-# 📌 Menu for chatbot
+# Load tokenizer and model
+@st.cache_resource()
+def load_model():
+    model_name = "deepseek-ai/deepseek-llm-7b-chat"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name, torch_dtype=torch.float16, device_map="auto", offload_folder="offload_weights"
+    )
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Define menu
 menu = {
     "meals": ["Grilled Chicken with Rice", "Beef Steak", "Salmon with Lemon Butter Sauce", "Vegetable Stir-Fry"],
     "fast_foods": ["Cheeseburger", "Pepperoni Pizza", "Fried Chicken", "Hot Dog", "Tacos", "French Fries"],
@@ -24,6 +34,7 @@ menu = {
     "sweets": ["Chocolate Cake", "Ice Cream", "Apple Pie", "Cheesecake", "Brownies", "Donuts"]
 }
 
+# Order processing
 system_prompt = f"""
 You are OrderBot, a virtual restaurant assistant.
 You help customers order food from the following menu:
@@ -40,67 +51,38 @@ Rules:
 """
 
 def process_order(user_input):
-    """
-    Handles chatbot conversation and order processing.
-    """
     responses = {
-        "greetings": ["Hello! How can I assist you today?", "Hey there! What would you like to order?", "Hi! Ready to place an order? 😊"],
-        "farewell": ["Goodbye! Have a great day! 👋", "See you next time!", "Take care!"],
-        "thanks": ["You're welcome! 😊", "Happy to help!", "Anytime!"],
-        "default": ["I'm not sure how to respond to that. Can I take your order?", "Interesting! Tell me more.", "I'm here to assist with your order."]
+        "greetings": ["Hello! How can I assist you today?", "Hey there! What would you like to order? 😊"],
+        "farewell": ["Goodbye! Have a great day! 👋", "See you next time!"],
+        "thanks": ["You're welcome! 😊", "Happy to help!"],
+        "default": ["I'm not sure how to respond to that. Can I take your order?", "Tell me more!"]
     }
-
-    user_input = user_input.lower()
-
-    if any(word in user_input for word in ["hello", "hi", "hey"]):
+    
+    if any(word in user_input.lower() for word in ["hello", "hi", "hey"]):
         return random.choice(responses["greetings"])
-    elif any(word in user_input for word in ["bye", "goodbye", "see you"]):
+    elif any(word in user_input.lower() for word in ["bye", "goodbye", "see you"]):
         return random.choice(responses["farewell"])
-    elif any(word in user_input for word in ["thank you", "thanks"]):
+    elif any(word in user_input.lower() for word in ["thank you", "thanks"]):
         return random.choice(responses["thanks"])
-
-    # AI-generated response
-    prompt = f"{system_prompt}\nUser: {user_input}\nOrderBot:"
-    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-    output = model.generate(**inputs, max_new_tokens=150)
-    raw_response = tokenizer.decode(output[0], skip_special_tokens=True)
-    
-    response = raw_response.split("OrderBot:")[-1].strip()
-    response = re.sub(r"Setting `pad_token_id`.*\n", "", response)
-
-    return response
-
-# 🎨 Streamlit UI
-st.title("🛒 OrderBot: AI Restaurant Assistant")
-st.write(f"🌍 **Public URL:** [{public_url}]({public_url}) (via ngrok)")
-
-# ℹ️ Display OrderBot Description
-st.markdown("""
-### 👋 Hey there, I am OrderBot! Your friendly Restaurants AI Agent.
-I am an **AI-driven assistant** powered by the **DeepSeek-7B Chat** model, designed for seamless natural language interaction.  
-I leverage **advanced machine learning** to process and respond to human input with **precision and efficiency**.  
-Let me take your order! 🍔🥤🍰
-""")
-
-# Chat History
+    else:
+        prompt = f"{system_prompt}\nUser: {user_input}\nOrderBot:"
+        inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+        output = model.generate(**inputs, max_new_tokens=150)
+        response = tokenizer.decode(output[0], skip_special_tokens=True).split("OrderBot:")[-1].strip()
+        return response
+
+# Chat interface
+st.subheader("💬 Chat with OrderBot")
 if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Display previous chat messages
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.write(message["content"])
+    st.session_state["messages"] = []
 
-# User Input
-user_input = st.chat_input("Type your message here...")
+for msg in st.session_state["messages"]:
+    st.chat_message(msg["role"]).write(msg["content"])
 
+user_input = st.text_input("You:", placeholder="Type your message here...")
 if user_input:
-    st.session_state.messages.append({"role": "user", "content": user_input})
-    with st.chat_message("user"):
-        st.write(user_input)
-
     response = process_order(user_input)
-
-    st.session_state.messages.append({"role": "assistant", "content": response})
-    with st.chat_message("assistant"):
-        st.write(response)
+    st.session_state["messages"].append({"role": "user", "content": user_input})
+    st.session_state["messages"].append({"role": "assistant", "content": response})
+    st.chat_message("user").write(user_input)
+    st.chat_message("assistant").write(response)