import gradio as gr
import whisper
import cohere
from deep_translator import GoogleTranslator
from gtts import gTTS
import gtts.langs
#from dotenv import load_dotenv

#load_dotenv()
co = cohere.Client('0brA5yZUeNlQM98z5h4XQAiYYpCGNMbGPjk5ghE6')

model = whisper.load_model("tiny")

LANGUAGES = list(gtts.lang.tts_langs())

def transcribe(audio,language):
    
    #time.sleep(3)
    # load audio and pad/trim it to fit 30 seconds
    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)

    # make log-Mel spectrogram and move to the same device as the model
    mel = whisper.log_mel_spectrogram(audio).to(model.device)

    # detect the spoken language
    _, probs = model.detect_language(mel)
    print(f"Detected language: {max(probs, key=probs.get)}")

    # decode the audio
    options = whisper.DecodingOptions(fp16 = False)
    result = whisper.decode(model, mel, options)

    #cohere
    response = co.generate(
    model='xlarge',
    prompt=f'This program will generate an introductory paragraph to a blog post given a blog title, audience, and tone of voice.\n--\nBlog Title: Best Activities in Toronto\nAudience: Millennials\nTone of Voice: Lighthearted\nFirst Paragraph: Looking for fun things to do in Toronto? When it comes to exploring Canada\'s largest city, there\'s an ever-evolving set of activities to choose from. Whether you\'re looking to visit a local museum or sample the city\'s varied cuisine, there is plenty to fill any itinerary. In this blog post, I\'ll share some of my favorite recommendations\n--\nBlog Title: Mastering Dynamic Programming\nAudience: Developers\nTone: Informative\nFirst Paragraph: In this piece, we\'ll help you understand the fundamentals of dynamic programming, and when to apply this optimization technique. We\'ll break down bottom-up and top-down approaches to solve dynamic programming problems.\n--\nBlog Title: {result.text}\nAudience: Athletes\nTone: Enthusiastic\nFirst Paragraph:',
    max_tokens=150,
    temperature=0.8,
    k=0,
    p=1,
    frequency_penalty=0,
    presence_penalty=0,
    stop_sequences=["--"],
    return_likelihoods='NONE')
    #result.text
    reptxt = response.generations[0].text.strip("--")

    #Google models
    translated = GoogleTranslator(source='auto', target=language).translate(reptxt)
    filename = 'result.mp3'
    tts = gTTS(text=translated, lang=language)
    tts.save(filename)
    return filename, translated



gr.Interface(
    title = 'OpenAI Whisper ASR Gradio Web UI', 
    fn=transcribe, 
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath"), 
            gr.Radio(label="Language", choices=LANGUAGES, value="en")
    ],
    outputs=[gr.Audio(label="Output",type="filepath"),gr.outputs.Textbox(label="Generated Text")],
    live=True).launch()