# from transformers import pipeline
# import gradio as gr
#
# pipe = pipeline(model="dacavi/whisper-small-hi")  # change to "your-username/the-name-you-picked"
# def transcribe(audio):
#     text = pipe(audio)["text"]
#     return text
#
# iface = gr.Interface(
#     fn=transcribe,
#     inputs=gr.Audio(sources="microphone", type="filepath"),
#     outputs="text",
#     title="Whisper Small Hindi",
#     description="Realtime demo for Hindi speech recognition using a fine-tuned Whisper small model.",
# )
#
# iface.launch()

import gradio as gr
from transformers import pipeline
from moviepy.editor import VideoFileClip
import tempfile
import os
from pydub import AudioSegment
from huggingface_hub import login

with open("../../token.txt", "r") as file:
    token = file.readline().strip()


login(token=token, add_to_git_credential=True)

pipe = pipeline(model="dacavi/whisper-small-hi")

def transcribe_video(video_url):
    # Download video and extract audio
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
        # os.system(f"yt-dlp -o {temp_audio.name} -x --audio-format wav {video_url}")
        os.system(f"yt-dlp -o audioSample.wav -x --audio-format wav {video_url}")

        print("Downloaded audio:", temp_audio.name)


    # Transcribe audio
        text = pipe("audioSample.wav")["text"]

    # Clean up temporary files
        os.remove("audioSample.wav")


    return text
print(transcribe_video("https://www.youtube.com/watch?v=8FkLRUJj-o0"))
# iface = gr.Interface(
#     fn=transcribe_video,
#     inputs="text",
#     outputs="text",
#     live=True,
#     title="Video Transcription",
#     description="Paste the URL of a video to transcribe the spoken content.",
# )
#
# iface.launch()