import gradio as gr
import librosa
import torch
from transformers import AutoTokenizer, pipeline, logging
from transformers import SpeechT5Processor, SpeechT5ForSpeechToText



# Audio 

checkpoint = "microsoft/speecht5_asr"
audio_processor = SpeechT5Processor.from_pretrained(checkpoint)
audio_model = SpeechT5ForSpeechToText.from_pretrained(checkpoint)

def process_audio(sampling_rate, waveform):
    # convert from int16 to floating point
    waveform = waveform / 32678.0

    # convert to mono if the stereo
    if len(waveform.shape) > 1:
        waveform = librosa.to_mono(waveform.T)

    # resample to 16 kHz if necessary
    if sampling_rate != 16000:
        waveform = librosa.resample(waveform, orig_sr=sampling_rate, target_sr=16000)

    # limit to 30 seconds
    waveform = waveform[:16000*30]

    # make PyTorch tensor
    waveform = torch.tensor(waveform)
    return waveform


def audio_to_text(audio, mic_audio=None):
    # audio = tuple (sample_rate, frames) or (sample_rate, (frames, channels))
    if mic_audio is not None:
        sampling_rate, waveform = mic_audio
    elif audio is not None:
        sampling_rate, waveform = audio
    else:
        return "(please provide audio)"

    waveform = process_audio(sampling_rate, waveform)
    inputs = audio_processor(audio=waveform, sampling_rate=16000, return_tensors="pt")
    predicted_ids = audio_model.generate(**inputs, max_length=400)
    transcription = audio_processor.batch_decode(predicted_ids, skip_special_tokens=True)
    return transcription[0]


# Text Generation

model_path= hf_hub_download(repo_id="TheBloke/Llama-2-7B-Chat-GGML", filename="llama-2-7b-chat.ggmlv3.q4_0.bin")
llm2 = Llama(model_path=model_path)

def generate(text):
  system_message = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. Give short, simple and direct answers"
  prompt_template=f'''[INST] <<SYS>>
  {system_message}
  <</SYS>>

  {text} [/INST]'''
  chat_compl = llm2.create_completion(prompt=prompt_template, top_k=50, top_p=0.7, temperature=0.7, repeat_penalty=1.5)
  return chat_compl['choices'][0]['text'].strip()

def audio_text_generate(audio):
  audio_text = audio_to_text(audio)
  generated_text = generate(audio_text)
  # response = generated_text[generated_text.index("[/INST]")+7:].strip()
  return audio_text, generated_text


demo = gr.Interface(fn=audio_text_generate,
                    inputs=gr.Audio(source="microphone"),
                    outputs=[gr.Text(label="Audio Text"), gr.Text(label="Generated Text")])
                    # examples=["https://samplelib.com/lib/preview/mp3/sample-3s.mp3"], cache_examples=True)

demo.launch()