Update app.py

app.py

@@ -1,14 +1,15 @@
 import os
-import re
+import requests
 import gradio as gr
 from gtts import gTTS
 from datetime import datetime
 from openpyxl import Workbook, load_workbook
-from langchain.memory import ConversationBufferMemory
-from langchain.llms import HuggingFacePipeline
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import re
+
+# ========== CONFIGURATION ==========
+API_KEY = "v1-ad9cdbda8503e3a1b67e9087b4a3ab5f0c115c217eea5b392dad8c06c6537db4"  
+MODEL = "google/gemini-pro"  # You can also try "mistral/mixtral-8x7b"
 
-# ========== SETUP ==========
 MENU = {
     "Cheeseburger": 5.99,
     "Fries": 2.99,
@@ -17,12 +18,13 @@ MENU = {
     "Chicken Wings": 7.99,
     "Salad": 6.99
 }
+
+chat_history = []
 order = []
 customer_name = ""
-EXCEL_FILE = "orders.xlsx"
-chat_memory = ConversationBufferMemory()
 
 # ========== Excel Setup ==========
+EXCEL_FILE = "orders.xlsx"
 def setup_excel():
     if not os.path.exists(EXCEL_FILE):
         wb = Workbook()
@@ -30,7 +32,6 @@ def setup_excel():
         ws.title = "Orders"
         ws.append(["Order ID", "Date", "Customer", "Items", "Total", "Time"])
         wb.save(EXCEL_FILE)
-
 setup_excel()
 
 def save_to_excel(name, items):
@@ -51,25 +52,62 @@ def clean_text(text):
     return text.strip()
 
 def speak(text, filename="response.mp3"):
-    tts = gTTS(text=clean_text(text))
+    cleaned = clean_text(text)
+    tts = gTTS(text=cleaned)
     tts.save(filename)
     return filename
 
-# ========== Load Hugging Face Model ==========
-def load_llm():
-    model_id = "HuggingFaceH4/zephyr-7b-beta"
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
-    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")
-    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=200)
-    return HuggingFacePipeline(pipeline=pipe)
-
-llm = load_llm()
-
-# ========== Chat Handling ==========
+# ========== OpenRouter API ==========
+def generate_response(user_input):
+    global chat_history, customer_name, order
+
+    menu_description = "\n".join([f"{item}: ${price}" for item, price in MENU.items()])
+    order_summary = ", ".join([f"{qty} x {item}" for item, qty in order]) if order else "No items yet"
+
+    context = f"""
+You are a friendly, helpful restaurant assistant at 'Systaurant'.
+MENU:
+{menu_description}
+Customer name: {customer_name}
+Current order: {order_summary}
+Instructions:
+- Ask for name if not known.
+- Show menu if requested.
+- Extract item names and quantities from messages.
+- Say 'Order summary' and ask 'Confirm?' when user is done.
+- Respond only as the bot, no need to prefix with "Bot:".
+- Keep tone human, natural, and friendly.
+Conversation:
+"""
+    for u, b in chat_history:
+        context += f"\nCustomer: {u}\nBot: {b}"
+
+    context += f"\nCustomer: {user_input}\nBot:"
+
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "model": MODEL,
+        "messages": [{"role": "user", "content": context}],
+    }
+
+    try:
+        response = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload)
+        response.raise_for_status()
+        return response.json()["choices"][0]["message"]["content"]
+    except Exception as e:
+        return f"❌ OpenRouter Error: {str(e)}"
+
+# ========== Chat Logic ==========
 def handle_chat(user_input):
     global customer_name, order
 
-    # Update customer name if mentioned
+    bot_reply = generate_response(user_input)
+
+    # Extract name
     if "my name is" in user_input.lower():
         customer_name = user_input.split("my name is")[-1].strip().split()[0].title()
 
@@ -83,43 +121,25 @@ def handle_chat(user_input):
                     break
             order.append((item, qty))
 
-    # Prepare dynamic prompt with memory
-    menu_str = "\n".join([f"{item}: ${price}" for item, price in MENU.items()])
-    order_str = ", ".join([f"{qty} x {item}" for item, qty in order]) if order else "No items yet"
-    prompt = f"""
-You are a friendly restaurant assistant for 'Systaurant'.
-Here is the menu:
-{menu_str}
-
-Current customer name: {customer_name or 'Not provided'}
-Current order: {order_str}
-
-User: {user_input}
-Bot:"""
-
-    # Generate LLM response
-    chat_memory.chat_memory.add_user_message(user_input)
-    reply = llm(prompt)
-    chat_memory.chat_memory.add_ai_message(reply)
-
-    # Confirm order if user says yes
+    # Confirm order
     if "confirm" in user_input.lower() or "yes" in user_input.lower():
         if customer_name and order:
             order_id = save_to_excel(customer_name, order)
-            reply += f"\n✅ Your order ID is {order_id}. Thank you for ordering from Systaurant!"
+            bot_reply += f"\n✅ Your order ID is {order_id}. Thank you for ordering from Saad's Restaurant!"
 
-    audio = speak(reply)
-    return reply, audio
+    chat_history.append((user_input, bot_reply))
+    audio_file = speak(bot_reply)
+    return bot_reply, audio_file
 
-# ========== Gradio Interface ==========
+# ========== Gradio UI ==========
 gr.Interface(
     fn=handle_chat,
-    inputs=gr.Textbox(label="👤 You", placeholder="What would you like to order?"),
+    inputs=gr.Textbox(label="👤 You", placeholder="Type your order..."),
     outputs=[
-        gr.Textbox(label="🤖 Bot"),
-        gr.Audio(label="🔊 Response", autoplay=True)
+        gr.Textbox(label="🤖 Bot Response"),
+        gr.Audio(label="🔊 Speaking", autoplay=True)
     ],
-    title="🍽️ Systaurant VoiceBot (HuggingFace LLM)",
-    description="Order food from the bot with voice output",
+    title="🍔 SysTaurant Voice Bot (OpenRouter powered)",
+    description="Talk to the bot and it will handle your order using OpenRouter API.",
     theme="soft"
-).launch()
+).launch(share=True)