Delete app.py

app.py

@@ -1,62 +0,0 @@
-import gradio as gr
-import os
-import numpy as np
-import librosa
-import asyncio
-import edge_tts
-import soundfile as sf
-from groq import Groq
-from fastrtc import WebRTC, ReplyOnPause, get_hf_turn_credentials
-
-# Initialize Groq
-client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
-async def text_to_speech_logic(text):
-    communicate = edge_tts.Communicate(text, "en-US-AndrewNeural")
-    await communicate.save("temp_op.mp3")
-    audio, sr = librosa.load("temp_op.mp3", sr=16000)
-    # Ensure audio is in the correct shape (1, samples) for FastRTC
-    if len(audio.shape) == 1:
-        audio = audio.reshape(1, -1)
-    return sr, audio
-
-def process_audio(audio: tuple[int, np.ndarray]):
-    sr, y = audio
-    # FastRTC audio can be (samples, channels), we need (samples,)
-    if len(y.shape) > 1:
-        y = y.mean(axis=1)
-        
-    sf.write("input.wav", y, sr)
-    
-    with open("input.wav", "rb") as file:
-        transcription = client.audio.transcriptions.create(
-            file=("input.wav", file.read()),
-            model="whisper-large-v3-turbo",
-        )
-    
-    response = client.chat.completions.create(
-        model="llama-3.3-70b-versatile",
-        messages=[
-            {"role": "system", "content": "You are a concise voice assistant. Give 1-sentence answers."},
-            {"role": "user", "content": transcription.text}
-        ]
-    )
-    reply_text = response.choices[0].message.content
-    return asyncio.run(text_to_speech_logic(reply_text))
-
-with gr.Blocks() as demo:
-    gr.Markdown("# 🎙️ Voice Agent Live (CPU)")
-    webrtc_comp = WebRTC(
-        label="Voice Chat",
-        mode="send-receive",
-        modality="audio",
-        rtc_configuration=get_hf_turn_credentials()
-    )
-    webrtc_comp.stream(
-        fn=ReplyOnPause(process_audio),
-        inputs=[webrtc_comp],
-        outputs=[webrtc_comp]
-    )
-
-if __name__ == "__main__":
-    demo.launch()