import os
import spaces
import torch
import gradio as gr
from transformers import pipeline

MODEL_NAME = "openai/whisper-large-v3"
BATCH_SIZE = 8
FILE_LIMIT_MB = 1000

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

@spaces.GPU(duration=120)
def respond_to_question_llama(transcript, question):
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        "meta-llama/Meta-Llama-3.1-70B-Instruct",
        token=os.environ["HUGGINGFACEHUB_API_TOKEN"],
    )

    response = client.chat_completion(
        messages=[{"role": "user", "content": f"Transcript: {transcript}\n\nUser: {question}"}],
        max_tokens=150,
    ).choices[0].message.content

    return response

@spaces.GPU
def audio_transcribe(inputs):
    if inputs is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")

    text = pipe(inputs, batch_size=BATCH_SIZE, return_timestamps=True)["text"]

    return  text

with gr.Blocks() as transcriberUI:
    gr.Markdown(
        """
        # Ola!
        Clique no botao abaixo para selecionar o Audio que deseja conversar!
        Ambiente disponivel 24x7. Running on ZeroGPU with openai/whisper-large-v3
        """
    )
    inp = gr.File(label="Arquivo de Audio", show_label=True, type="filepath", file_count="single", file_types=["mp3"])
    transcribe = gr.Textbox(label="Transcricao", show_label=True, show_copy_button=True)
    ask_question = gr.Textbox(label="Ask a question", visible=True)
    response_output = gr.Textbox(label="Response", visible=True)
    submit_question = gr.Button("Submit question", visible=True)

    def ask_question_callback(transcription,question):
        if ask_question:
            response = respond_to_question_llama(transcription, question)
            response_output.value = response
        else:
            response_output.value = "No question asked"

        return response_output

    inp.upload(audio_transcribe, inputs=inp, outputs=transcribe)
    submit_question.click(ask_question_callback, outputs=[response_output], inputs=[transcribe, ask_question])


transcriberUI.queue().launch(debug=True)