Update app.py

app.py

@@ -1,61 +1,20 @@
 import gradio as gr
-import speech_recognition as sr
-from gtts import gTTS
-import os
-import pygame  # Use pygame for playing audio
-from transformers import pipeline
-
-# Initialize pygame for audio playback
-pygame.mixer.init()
-
-# 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 from audio files or microphone
-def recognize_speech(audio):
-    try:
-        # If audio is from file, use SpeechRecognition to convert speech to text
-        if isinstance(audio, str):  # Audio file input (filepath)
-            with sr.AudioFile(audio) as source:
-                audio_data = recognizer.record(source)
-                text = recognizer.recognize_google(audio_data)
-        else:  # Audio from microphone input
-            text = recognizer.recognize_google(audio)
-        
-        print(f"Recognized text: {text}")  # Print the recognized text for debugging
-        
-        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 pygame
-        pygame.mixer.music.load("response.mp3")
-        pygame.mixer.music.play()
-
-        return response
-    except Exception as e:
-        print(f"Error: {e}")  # Print the error for debugging
-        return "Sorry, I could not understand."
+from transformers import pipeline, TFAutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+
+# Initialize Hugging Face pipelines
+speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-large")
+text_to_speech = pipeline("text-to-speech", model="facebook/tacotron2", device=0)  # Set device to CPU (0) or GPU (cuda)
+
+# Function to process speech to text and text to speech
+def process_audio(input_audio):
+    # Convert the audio to text using Whisper model (speech-to-text)
+    recognized_text = speech_to_text(input_audio)["text"]
+    print(f"Recognized text: {recognized_text}")
+    
+    # Process the text to speech using Tacotron2 model
+    audio_response = text_to_speech(recognized_text)
+    return audio_response, recognized_text
 
 # Gradio Interface for the app
 def create_gradio_interface():
@@ -66,10 +25,11 @@ def create_gradio_interface():
         audio_input = gr.Audio(type="filepath", label="Speak to the bot (Upload or Record Audio)")
 
         # Display the bot's response after recognition
-        output_text = gr.Textbox(label="Bot Response")
+        output_audio = gr.Audio(label="Bot Response", type="numpy")
+        output_text = gr.Textbox(label="Bot Response (Text)")
 
         # Define the button to process the audio input
-        audio_input.change(fn=recognize_speech, inputs=audio_input, outputs=output_text)
+        audio_input.change(fn=process_audio, inputs=audio_input, outputs=[output_audio, output_text])
 
     return demo