Update app.py

app.py

@@ -1,65 +1,20 @@
 import gradio as gr
-from transformers import (
-    WhisperProcessor, WhisperForConditionalGeneration,
-    AutoModelForCausalLM, AutoTokenizer, pipeline,
-)
-from huggingface_hub import snapshot_download
-import sounddevice as sd
-import numpy as np
-import torch
-from gtts import gTTS
-import pygame
-
-class InteractiveChat:
-    def __init__(self, model_name="openai/whisper-large", tts_choice="OpenVoice"):
-        self.whisper_processor = WhisperProcessor.from_pretrained(model_name)
-        self.whisper_model = WhisperForConditionalGeneration.from_pretrained(model_name)
-        self.zephyr_tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-        self.zephyr_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta", device_map="auto")
-        self.zephyr_pipeline = pipeline("text-generation", model=self.zephyr_model, tokenizer=self.zephyr_tokenizer)
-        self.tts_choice = tts_choice
-
-    def generate_response(self, input_data):
-        input_features = self.whisper_processor(input_data, sampling_rate=16_000, return_tensors="pt").input_features
-        predicted_ids = self.whisper_model.generate(input_features)
-        transcription = self.whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-        
-        # Use the transcription as input for Zephyr
-        response = self.zephyr_pipeline(transcription, max_length=1000)[0]["generated_text"] 
-        return transcription, response
-
-    def speak(self, text):
-        try:
-            if self.tts_choice == "OpenVoice":
-                model_path = snapshot_download("facebook/mms-tts-eng") 
-                pipe = pipeline("text-to-speech", model=model_path)
-                audio_array = pipe(text).audio
-                pygame.mixer.init()
-                sound = pygame.sndarray.make_sound(audio_array)
-                sound.play()
-                pygame.time.delay(int(sound.get_length() * 1000))
-            else:  # gTTS
-                tts = gTTS(text=text, lang='en')
-                tts.save("response.mp3")
-                pygame.mixer.init()
-                pygame.mixer.music.load("response.mp3")
-                pygame.mixer.music.play()
-                while pygame.mixer.music.get_busy():
-                    pygame.time.Clock().tick(10)
-        except Exception as e:
-            print("Error occurred during speech generation:", e)
-
-
 
 with gr.Blocks() as demo:
-    model_choice = gr.Dropdown(["openai/whisper-large"], label="Whisper Model", value="openai/whisper-large")
-    tts_choice = gr.Radio(["OpenVoice", "gTTS"], label="TTS Engine", value="OpenVoice")
-    input_data = gr.Audio(source="microphone", type="numpy", label="Speak Your Message")
+    model_choice = gr.Dropdown(["openai/whisper-large", "HuggingFaceH4/zephyr-7b-beta"], 
+                                label="Choose Model", value="openai/whisper-large")
+    input_data = gr.Audio(source="microphone", type="filepath", label="Speak Your Message")  # Note: type is now "filepath"
     output_text = gr.Textbox(label="Transcription and Response")
 
-    model_choice.change(lambda x, y: InteractiveChat(x, y), inputs=[model_choice, tts_choice], outputs=None)
-    tts_choice.change(lambda x, y: InteractiveChat(y, x), inputs=[tts_choice, model_choice], outputs=None)
-    input_data.change(lambda x, model: model.generate_response(x), inputs=[input_data, model_choice], outputs=output_text)
-    input_data.change(lambda x, model: model.speak(x[1]), inputs=[output_text, model_choice], outputs=None) # Speak the response
+    def generate_response(audio_path, model_name):
+        hf_interface = gr.Interface.load(model_name)  # Load the model directly from the Hub
+        if model_name == "openai/whisper-large":
+            transcription = hf_interface(audio_path)  # Handle transcription
+        else:  # Zephyr
+            transcription = hf_interface(audio_path)[0]["generated_text"] # Extract transcription from zephyr
+        response = hf_interface(transcription)[0]["generated_text"]  # Get Zephyr's response 
+        return transcription, response
+    
+    input_data.change(generate_response, inputs=[input_data, model_choice], outputs=output_text)
 
-demo.launch(share=True)
+demo.launch()