import gradio as gr
from transformers import pipeline, AutoTokenizer
import numpy as np
from pydub import AudioSegment
import librosa

# Load the pipeline for speech recognition and translation
pipe = pipeline(
    "automatic-speech-recognition",
    model="Akashpb13/Hausa_xlsr",
    tokenizer="Akashpb13/Hausa_xlsr"
)
translator = pipeline("text2text-generation", model="Baghdad99/saad-hausa-text-to-english-text")
tts = pipeline("text-to-speech", model="Baghdad99/english_voice_tts")

def translate_speech(audio_input):
    print(f"Type of audio: {type(audio_data_tuple)}, Value of audio: {audio_data_tuple}")  # Debug line
    # Check if the input is a tuple (recorded audio) or a string (uploaded file)
    if isinstance(audio_input, tuple):
        # Extract the audio data from the tuple
        sample_rate, audio_data = audio_input
    else:
        # Load the audio file as a floating point time series
        audio_data, sample_rate = librosa.load(audio_input, sr=None)

    # Normalize the audio data to the range [-1, 1]
    audio_data_normalized = audio_data / np.iinfo(audio_data.dtype).max

    # Convert the normalized audio data to float64
    audio_data_float64 = audio_data_normalized.astype(np.float64)

    # Use the speech recognition pipeline to transcribe the audio
    output = pipe(audio_data_float64)

    print(f"Output: {output}")  # Print the output to see what it contains

    # Check if the output contains 'text'
    if 'text' in output:
        transcription = output["text"]
    else:
        print("The output does not contain 'text'")
        return

    # Print the transcription
    print(f"Transcription: {transcription}")

    # Use the translation pipeline to translate the transcription
    translated_text = translator(transcription, return_tensors="pt")
    print(f"Translated text: {translated_text}")  # Print the translated text to see what it contains

    # Check if the translated text contains 'generated_token_ids'
    if 'generated_token_ids' in translated_text[0]:
        # Decode the tokens into text
        translated_text_str = translator.tokenizer.decode(translated_text[0]['generated_token_ids'])
    else:
        print("The translated text does not contain 'generated_token_ids'")
        return

    # Print the translated text string
    print(f"Translated text string: {translated_text_str}")

    # Use the text-to-speech pipeline to synthesize the translated text
    synthesised_speech = tts(translated_text_str)
    print(f"Synthesised speech: {synthesised_speech}")  # Print the synthesised speech to see what it contains

    # Check if the synthesised speech contains 'audio'
    if 'audio' in synthesised_speech:
        synthesised_speech_data = synthesised_speech['audio']
    else:
        print("The synthesised speech does not contain 'audio'")
        return

    # Flatten the audio data
    synthesised_speech_data = synthesised_speech_data.flatten()

    # Print the shape and type of the synthesised speech data
    print(f"Synthesised speech data type: {type(synthesised_speech_data)}, Synthesised speech data shape: {synthesised_speech_data.shape}")

    # Scale the audio data to the range of int16 format
    synthesised_speech = (synthesised_speech_data * 32767).astype(np.int16)

    return 16000, synthesised_speech

# Define the Gradio interface
iface = gr.Interface(
    fn=translate_speech, 
    inputs=gr.inputs.Audio(source="microphone", type="filepath"),  # Change this line
    outputs=gr.outputs.Audio(type="numpy"),
    title="Hausa to English Translation",
    description="Realtime demo for Hausa to English translation using speech recognition and text-to-speech synthesis."
)

iface.launch()