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import gradio as gr
import speech_recognition as sr
from gtts import gTTS
import os
from playsound import playsound # Import playsound library
from transformers import pipeline
# Initialize recognizer for speech recognition
recognizer = sr.Recognizer()
# Initialize Hugging Face NLP pipeline for intent recognition using a specific model
nlp = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
# Define the food menu
menu = {
'Pizza': ['Cheese', 'Pepperoni', 'Vegetarian'],
'Beverages': ['Coke', 'Pepsi', 'Water']
}
# Function to process the order
def process_order(order):
if 'pizza' in order.lower():
return "What type of pizza would you like? Cheese, Pepperoni, or Vegetarian?"
elif 'coke' in order.lower():
return "One Coke added to your order."
else:
return "Sorry, we didn't catch that. Please try again."
# Function to handle speech recognition
def recognize_speech(audio):
try:
# Recognize speech using SpeechRecognition
text = recognizer.recognize_google(audio)
response = process_order(text)
# Using gTTS to respond back with speech
tts = gTTS(text=response, lang='en')
tts.save("response.mp3")
# Play the MP3 response using playsound
playsound("response.mp3")
return response
except Exception as e:
return "Sorry, I could not understand."
# Gradio Interface for the app
def create_gradio_interface():
with gr.Blocks() as demo:
gr.Markdown("## AI Voice Bot for Food Ordering")
# Audio Input: User speaks into microphone
audio_input = gr.Audio(type="numpy", label="Speak to the bot")
# Display the bot's response after recognition
output_text = gr.Textbox(label="Bot Response")
# Define the button to process the audio input
audio_input.change(fn=recognize_speech, inputs=audio_input, outputs=output_text)
return demo
# Create and launch the Gradio app
if __name__ == "__main__":
app = create_gradio_interface()
app.launch(share=True)
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