app.py

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"
)

# 📌 Menu for chatbot
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"],
    "drinks": ["Coke", "Pepsi", "Lemonade", "Orange Juice", "Iced Coffee", "Milkshake"],
    "sweets": ["Chocolate Cake", "Ice Cream", "Apple Pie", "Cheesecake", "Brownies", "Donuts"]
}

system_prompt = f"""
You are OrderBot, a virtual restaurant assistant.
You help customers order food from the following menu:

🍽️ **Meals**: {', '.join(menu['meals'])}
🍔 **Fast Foods**: {', '.join(menu['fast_foods'])}
🥤 **Drinks**: {', '.join(menu['drinks'])}
🍰 **Sweets**: {', '.join(menu['sweets'])}

Rules:
1️⃣ Always confirm the customer's order.
2️⃣ Ask if they need anything else.
3️⃣ Respond in a friendly and professional manner.
"""

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."]
    }

    user_input = user_input.lower()

    if any(word in user_input for word in ["hello", "hi", "hey"]):
        return random.choice(responses["greetings"])
    elif any(word in user_input for word in ["bye", "goodbye", "see you"]):
        return random.choice(responses["farewell"])
    elif any(word in user_input 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
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"])

# User Input
user_input = st.chat_input("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)