import gradio as gr
import os
from datasets import load_dataset
import random
import hashlib
import datetime

# Initialize Hugging Face API
hf_token = os.environ['HF_TOKEN']
dataset_repo_id = "kmanninen/heyakida"
audio_dataset = load_dataset(dataset_repo_id, token=hf_token)

class GlobalState:
    def new_label(self):
        texts = [
            "Hey Akida",
            "Okay Akida",
            "Hey Anita",
            "Hey Chiquita"
        ]
        label = random.choice(texts)
        return label
    
    def update_label(self):
        self.label = self.new_label()

    def __init__(self):
        self.label = self.new_label()

global_state = GlobalState()

def generate_unique_code():
    current_time = datetime.datetime.now().isoformat()
    hash_object = hashlib.sha256(current_time.encode())
    hex_dig = hash_object.hexdigest()
    unique_code = hex_dig[:8]
    return unique_code

def save_audio_dataset(audio, label, dataset, dataset_repo_id):
        """Save recorded audio and return status"""
        if audio is None:
            return "No audio recorded"
            
        # audio is a tuple of (sample_rate, audio_data) or a file path
        if isinstance(audio, tuple):
            # If it's a tuple, the audio was recorded in memory
            audio_path = audio[0]  # Gradio saves it temporarily and returns the path
        else:
            # If it's a string, it's already a file path
            audio_path = audio
            
        print(audio_path)
        
        if not audio_path:
            return "No audio file path received"
        
        try:
        
            # Create dataset object.
            new_data = {
                "audio": audio_path,
                "label": "Hey Akida"
            }

            # Concatenate the new dataset with the existing one
            dataset = dataset['train'].add_item(new_data)
            dataset.push_to_hub(repo_id=dataset_repo_id, token=hf_token)

            unique_code = generate_unique_code()            

            return unique_code
        except Exception as e:
            print(f"Failed to push {e}")
            raise e

def save_audio(audio):
    try:
        unique_code = save_audio_dataset(audio, global_state.label, audio_dataset, dataset_repo_id)
        global_state.update_label()
        confirmation_text = f"Your audio recording was successfully submitted, thank you! Here is the confirmation code: **{unique_code}**"
        return confirmation_text
    except Exception as e:
        print (f"Error saving audio: {str(e)}")
        raise e

# Define the Gradio interface
with gr.Blocks() as block:

    prompt_box = gr.Markdown(value=f"## Please record a short audio snippet of the following phrase using your microphone:")
    label_box = gr.Markdown(value="\"Hey Akida\"")

    audio_input = gr.Audio(
        sources=['microphone'],
        type="filepath",
        min_length=1,
        max_length=3
    )

    submit_button = gr.Button("Submit")

    submit_button.click(
        fn=save_audio,
        inputs=[audio_input],
        outputs=[label_box]
    )

    

# Launch the app
block.launch()