app.py

import gradio as gr
from transformers import pipeline
import edge_tts
import numpy as np
import asyncio

# Print Gradio version for debugging
print(f"Gradio version: {gr.__version__}")

# Load speech-to-text model (Whisper small for Farsi)
stt = pipeline("automatic-speech-recognition", model="openai/whisper-small")

# Load chatbot model (GPT2 fine-tuned for Farsi)
chatbot = pipeline("text-generation", model="HooshvareLab/gpt2-fa")

# Function to convert text to speech using edge-tts
async def tts(text, voice="fa-IR-FaridNeural"):
    communicate = edge_tts.Communicate(text, voice)
    audio_data = b""
    async for chunk in communicate.stream():
        if chunk["type"] == "audio":
            audio_data += chunk["data"]
    audio_array = np.frombuffer(audio_data, dtype=np.int16)
    sample_rate = 24000  # As per edge-tts documentation
    return sample_rate, audio_array

# Main function: Audio-to-audio pipeline
async def audio_to_audio(audio_input):
    if audio_input is None:
        return None, "No audio input received."
    sample_rate_in, data_in = audio_input
    audio = {"array": data_in, "sampling_rate": sample_rate_in}
    
    # Step 1: Convert speech to text
    text = stt(audio)["text"]
    
    # Step 2: Generate chatbot response
    response = chatbot(text, max_length=50, num_return_sequences=1)[0]["generated_text"]
    
    # Step 3: Convert text to speech
    sample_rate_out, data_out = await tts(response)
    
    return (sample_rate_out, data_out)

# Gradio interface
demo = gr.Interface(
    fn=audio_to_audio,
    inputs=gr.Audio(source="microphone", type="numpy", label="Speak in Farsi"),
    outputs=gr.Audio(type="numpy", label="Response in Farsi"),
    title="Farsi Audio Chatbot",
    description="Speak in Farsi, and the app will respond in Farsi audio."
)

# Launch the app
demo.launch()