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import gradio as gr
from transformers import pipeline
from gtts import gTTS
import os
import numpy as np
# Initialize the speech recognition pipeline
asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-large")
# Conversation history and context
conversation_history = []
context = {"last_action": None, "order": []}
# Menu for the restaurant
menu = {
"Starters": ["Soup", "Spring Rolls"],
"Main Course": ["Paneer Butter Masala", "Chicken Curry", "Veg Biryani"],
"Breads": ["Roti", "Naan", "Paratha"],
"Desserts": ["Gulab Jamun", "Ice Cream"],
"Drinks": ["Mango Lassi", "Soda", "Water"]
}
# Text-to-Speech Function
def speak_and_save(text, filename="response.mp3"):
tts = gTTS(text=text, lang='en')
tts.save(filename)
return filename
# Process the audio file and generate response
def process_order(audio_file_path):
if audio_file_path is None:
raise ValueError("Audio file path is None. Please provide a valid path.")
# Recognize speech
try:
transcript = asr_pipeline(audio_file_path)["text"]
except Exception as e:
return f"Error in speech recognition: {e}", None
# Process the recognized text
global context
user_input = transcript.lower()
conversation_history.append(f"Customer: {user_input}")
response = ""
if context["last_action"] is None:
response = "Welcome to our restaurant! How can I assist you today?"
context["last_action"] = "greet"
elif "menu" in user_input:
response = "Here is our menu:\n"
for category, items in menu.items():
response += f"{category}: {', '.join(items)}\n"
response += "What would you like to order?"
context["last_action"] = "show_menu"
elif "order" in user_input or any(item.lower() in user_input for item in sum(menu.values(), [])):
for category, items in menu.items():
for item in items:
if item.lower() in user_input:
context["order"].append(item)
response = f"I have added {', '.join(context['order'])} to your order. Would you like anything else?"
context["last_action"] = "place_order"
elif "no" in user_input or "that's it" in user_input:
response = f"Your final order is: {', '.join(context['order'])}. Thank you for your order. Your food will arrive shortly."
context["last_action"] = "final_order"
context["order"] = [] # Reset the order
else:
response = "I'm not sure what you meant. Could you clarify?"
conversation_history.append(f"AI: {response}")
audio_response_path = speak_and_save(response)
return response, audio_response_path
# Save Conversation History
def save_conversation():
with open("conversation_history.txt", "w") as f:
f.write("\n".join(conversation_history))
return "Conversation history saved successfully!"
# Gradio Interface
def create_interface():
return gr.Interface(
fn=process_order,
inputs=gr.Audio(type="filepath", label="Your Voice Input"),
outputs=[
gr.Textbox(label="Text Response"),
gr.Audio(label="Audio Response")
],
title="Restaurant Voice Assistant",
description="Talk to our voice assistant to place your order or ask about the menu!",
live=True
)
if __name__ == "__main__":
try:
app = create_interface()
app.launch()
finally:
save_conversation()
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