Create app.py

app.py

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+import os
+from groq import Groq, GroqError
+import gradio as gr
+import torch
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer
+import soundfile as sf
+
+# Initialize Groq client with API key
+GROQ_API_KEY = "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"
+
+# Device setup for Parler-TTS
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+parler_model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device)
+parler_tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
+
+# Function to transcribe audio using Whisper through Groq, with error handling
+def transcribe_audio(audio):
+    try:
+        # Ensure the audio is in the correct format supported by Groq
+        audio_input = audio
+        transcription_response = client.transcriptions.create(
+            model="openai/whisper-large-v3",
+            audio=audio_input,
+        )
+        return transcription_response['text']
+    except GroqError as e:
+        print(f"Groq transcription error: {e}")
+        return "Error: Failed to transcribe audio."
+
+# Function to generate a response using LLaMA through Groq, with error handling
+def generate_response(text):
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": text}],
+            model="llama3-70b-8192",  # Modify based on the model you're using
+        )
+        return chat_completion.choices[0].message['content']
+    except GroqError as e:
+        print(f"Groq response generation error: {e}")
+        return "Error: Failed to generate a response."
+
+# Function to convert text to speech using Parler-TTS, unchanged
+def text_to_speech(text):
+    try:
+        description = "A female speaker delivers a slightly expressive and animated speech with a moderate speed and pitch."
+        input_ids = parler_tokenizer(description, return_tensors="pt").input_ids.to(device)
+        prompt_input_ids = parler_tokenizer(text, return_tensors="pt").input_ids.to(device)
+        generation = parler_model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+        audio_arr = generation.cpu().numpy().squeeze()
+        sf.write("parler_tts_out.wav", audio_arr, parler_model.config.sampling_rate)
+        return "parler_tts_out.wav"
+    except Exception as e:
+        print(f"Parler-TTS error: {e}")
+        return "Error: Failed to convert text to speech."
+
+# Gradio interface combining all the components, with error handling in each step
+def chatbot_pipeline(audio):
+    # Step 1: Convert speech to text using Whisper through Groq
+    transcribed_text = transcribe_audio(audio)
+
+    # If there was an error in transcription, return the error message
+    if "Error" in transcribed_text:
+        return transcribed_text, None
+
+    # Step 2: Generate a response using LLaMA through Groq
+    response_text = generate_response(transcribed_text)
+
+    # If there was an error in response generation, return the error message
+    if "Error" in response_text:
+        return response_text, None
+
+    # Step 3: Convert response text to speech using Parler-TTS
+    response_audio_path = text_to_speech(response_text)
+
+    # If there was an error in TTS conversion, return the error message
+    if "Error" in response_audio_path:
+        return response_text, None
+
+    # Return both text and audio for output
+    return response_text, response_audio_path
+
+# Gradio interface setup
+ui = gr.Interface(
+    fn=chatbot_pipeline,
+    inputs=gr.Audio(type="numpy"),  # Removed 'source' and 'streaming'
+    outputs=[gr.Textbox(label="Chatbot Response"), gr.Audio(label="Chatbot Voice Response")],
+    live=True
+)
+
+ui.launch()