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| import gradio as gr | |
| import speech_recognition as sr | |
| import pyttsx3 | |
| from transformers import pipeline | |
| # Initialize the text-to-speech engine | |
| engine = pyttsx3.init() | |
| # Initialize the transformer pipeline for NLP (Text Classification or any specific task) | |
| nlp = pipeline("zero-shot-classification") | |
| # Function to convert speech to text | |
| def speech_to_text(audio_file): | |
| recognizer = sr.Recognizer() | |
| with sr.AudioFile(audio_file.name) as source: | |
| audio = recognizer.record(source) | |
| try: | |
| text = recognizer.recognize_google(audio) | |
| return text | |
| except sr.UnknownValueError: | |
| return "Sorry, I didn't catch that." | |
| except sr.RequestError: | |
| return "Sorry, there's an issue with the speech recognition service." | |
| # Function to process text (handle menu ordering) | |
| def process_order(text): | |
| # You can add your logic here for handling various food orders and preferences | |
| result = nlp(text, candidate_labels=["Vegan", "Halal", "Guilt-Free", "Regular"]) | |
| category = result['labels'][0] | |
| if "Vegan" in category: | |
| response = "You've chosen a Vegan dish." | |
| elif "Halal" in category: | |
| response = "You've chosen a Halal dish." | |
| elif "Guilt-Free" in category: | |
| response = "You've chosen a Guilt-Free dish." | |
| else: | |
| response = "You've chosen a regular dish." | |
| return response | |
| # Function for Text-to-Speech (Response back to user) | |
| def speak_response(text): | |
| engine.say(text) | |
| engine.runAndWait() | |
| # Create Gradio interface | |
| def voice_assistant(audio_file): | |
| text = speech_to_text(audio_file) | |
| response = process_order(text) | |
| speak_response(response) | |
| return response | |
| iface = gr.Interface(fn=voice_assistant, | |
| inputs=gr.inputs.Audio(source="microphone", type="file"), | |
| outputs="text", | |
| live=True) | |
| # Launch Gradio app | |
| if __name__ == "__main__": | |
| iface.launch() | |