app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, WhisperProcessor, WhisperForConditionalGeneration
from gtts import gTTS
import os

class InteractiveChat:
    def __init__(self):
        self.whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-large") 
        self.whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
        self.zephyr_tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
        self.zephyr_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", device_map="auto")

    def generate_response(self, input_data):
        input_features = self.whisper_processor(input_data)  
        predicted_ids = self.whisper_model.generate(input_features)
        transcription = self.whisper_processor.batch_decode(predicted_ids)
        
        response = self.get_zephyr_response(transcription)
        
        self.speak(response)
        return response

    def get_zephyr_response(self, transcription):
        zephyr_pipeline = pipeline("text-generation")  
        response = zephyr_pipeline(transcription)[0]["generated_text"]
        return response

    def speak(self, text):
        tts = gTTS(text=text, lang='en')
        tts.save("output.mp3")
        os.system("mpg321 output.mp3")

# Create an instance of the InteractiveChat class
chat = InteractiveChat()

# Define a function that wraps the generate_response method
def generate_response_fn(input_data):
    return chat.generate_response(input_data)

# Use the function in gr.Interface
interface = gr.Interface(
  gr.Audio(type="filepath"),  # Accept audio files
  gr.Textbox(),
  generate_response_fn  # Pass the function here
)

interface.launch()