# -*- coding: utf-8 -*-
"""Youtube Pipeline

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1tHUaxj9C_-nAr4AUMg8MHCwvUNPISNm2
"""

# Commented out IPython magic to ensure Python compatibility.
# %%capture
# !pip install streamlit
# # !pip install diffusers
# !pip install transformers
# !pip install torch torchvision imageio
# !pip install gradio
# !pip install diffusers==0.11.1
# !pip install transformers scipy ftfy accelerate
# !pip install torch
# !pip install accelerate
# !pip install diffusers
# !pip install yt_dlp
# !pip install transformers
#

# !streamlit run youtube_pipeline.py & npx localtunnel --port 8501

import torch
import yt_dlp as youtube_dl
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import tempfile
import os

MODEL_NAME = "openai/whisper-large-v3"
BATCH_SIZE = 8
YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files

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

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

def transcribe_audio(inputs, task):
    if inputs is None:
        raise ValueError("No audio file submitted! Please provide an audio file.")

    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
    return text

def download_yt_audio(yt_url, filename):
    info_loader = youtube_dl.YoutubeDL()

    try:
        info = info_loader.extract_info(yt_url, download=False)
    except youtube_dl.utils.DownloadError as err:
        raise ValueError(str(err))

    file_length = info["duration"]
    if file_length > YT_LENGTH_LIMIT_S:
        raise ValueError(f"Maximum YouTube length is {YT_LENGTH_LIMIT_S} seconds, got {file_length} seconds YouTube video.")

    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}

    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
        try:
            ydl.download([yt_url])
        except youtube_dl.utils.ExtractorError as err:
            raise ValueError(str(err))

def yt_transcribe(yt_url, task):
    with tempfile.TemporaryDirectory() as tmpdirname:
        filepath = os.path.join(tmpdirname, "video.mp4")
        download_yt_audio(yt_url, filepath)
        with open(filepath, "rb") as f:
            inputs = f.read()

    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}

    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]

    return text

def main():
    print("Whisper Large V3: Transcribe YouTube Video\n")
    yt_url = "https://www.youtube.com/watch?v=Vd-YX40Zz0Q"
    # task = input("Select Task ('transcribe' or 'translate'): ")

    # if task.lower() not in ['transcribe', 'translate']:
    #     print("Invalid task! Please choose either 'transcribe' or 'translate'.")
    #     return

    try:
        global text
        text = yt_transcribe(yt_url, "transcribe")
        print("\nTranscription:\n", text)
    except Exception as e:
        print("\nAn error occurred:", str(e))

if __name__ == "__main__":
    main()

print(text)

from huggingface_hub import InferenceClient


client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")


def save_to_file(output_text, filename='script.txt'):
    with open(filename, 'w') as f:
        f.write(output_text)

def format_prompt(message, history):
    prompt = ""
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt


def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(prompt, history)

    stream = client.text_generation(
        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False
    )
    output = ""

    for response in stream:
        output += response.token.text

    return output

def main():






    history = []  # You may add functionality to maintain history here

    max_new_tokens = 768

    prompt = "Summarize this transcription into a video summary -"+text
    print("Generating A Script For you...")
    global qt
    qt = generate(prompt, history, 0.90, max_new_tokens, 0.90, 1.20)
    print(qt)

    # save_to_file(output_text)
    # print("Script Saved'")

if __name__ == "__main__":
    main()

from huggingface_hub import InferenceClient


client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")


def save_to_file(output_text, filename='script.txt'):
    with open(filename, 'w') as f:
        f.write(output_text)

def format_prompt(message, history):
    prompt = ""
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt


def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(prompt, history)

    stream = client.text_generation(
        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False
    )
    output = ""

    for response in stream:
        output += response.token.text

    return output

def main():






    history = []  # You may add functionality to maintain history here

    max_new_tokens = 768

    prompt = "Give me a new script for a youtube video.The script should be inspired by this youtube video transcription"+qt
    print("Generating A Script For you...")
    global output_text
    output_text = generate(prompt, history, 0.90, max_new_tokens, 0.90, 1.20)
    print(output_text[:-4])

    save_to_file(output_text)
    print("Script Saved'")

if __name__ == "__main__":
    main()

from huggingface_hub import InferenceClient


client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")


def save_to_file(output_text, filename='script.txt'):
    with open(filename, 'w') as f:
        f.write(output_text)

def format_prompt(message, history):
    prompt = ""
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt


def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(prompt, history)

    stream = client.text_generation(
        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False
    )
    output = ""

    for response in stream:
        output += response.token.text

    return output

def main():






    history = []  # You may add functionality to maintain history here

    max_new_tokens =128

    prompt = "Generate a prompt for stable diffusion to generate a youtube thumbnail for the script.I ONLY WANT A PROMPT (SO I CAN GIVE AN AI TO GENERATE A THUMBNAIL)The script is as follows"+output_text

    global popo
    popo = generate(prompt, history, 0.90, max_new_tokens, 0.90, 1.20)
    print(popo[:-4])


if __name__ == "__main__":
    main()

"""Youtube Pipeline

Hugging Face-Stable Diffusion
"""

import torch
from diffusers import StableDiffusionPipeline

pipe1 = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)

pipe1 = pipe1.to("cuda")

# prompt = "a cat running on a treadmill in a rich house"
image = pipe1(popo).images[0]  # image here is in [PIL format](https://pillow.readthedocs.io/en/stable/)

# Now to display an image you can either save it such as:
image.save("thumbnail.png")

# or if you're in a google colab you can directly display it with
image

"""Mid journey

"""

import requests

API_URL = "https://api-inference.huggingface.co/models/Kvikontent/midjourney-v6"
headers = {"Authorization": "Bearer HUGGINGFACE_API_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.content
image_bytes = query({
    "inputs": popo,
})
# You can access the image with PIL.Image for example
import io
from PIL import Image
image = Image.open(io.BytesIO(image_bytes))