import gradio as gr
import os
import subprocess
import cv2
from moviepy.editor import VideoFileClip, concatenate_videoclips
import math

from huggingface_hub import snapshot_download

model_ids = [
    'runwayml/stable-diffusion-v1-5',
    'lllyasviel/sd-controlnet-depth', 
    'lllyasviel/sd-controlnet-canny', 
    'lllyasviel/sd-controlnet-openpose',
]
for model_id in model_ids:
    model_name = model_id.split('/')[-1]
    snapshot_download(model_id, local_dir=f'checkpoints/{model_name}')



def get_frame_count_in_duration(filepath):
    video = cv2.VideoCapture(filepath)
    fps = video.get(cv2.CAP_PROP_FPS)
    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
    duration = frame_count / fps
    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
    video.release()
    return gr.update(maximum=frame_count)


# Function to split video into chunks
def split_video_into_chunks(video_path, chunk_size):
    # Load the video clip
    video = VideoFileClip(video_path)

    # Calculate the total number of frames
    total_frames = int(video.duration * video.fps)

    # Calculate the number of chunks needed
    num_chunks = math.ceil(total_frames / chunk_size)

    # Create a list to store the chunks and their frame counts
    chunks = []

    # Split the video into chunks
    for i in range(num_chunks):
        # Calculate the start and end frame for the chunk
        start_frame = i * chunk_size
        end_frame = min((i + 1) * chunk_size, total_frames)

        # Extract the chunk from the video
        chunk = video.subclip(start_frame / video.fps, end_frame / video.fps)

        # Calculate the frame count for the chunk
        frame_count = end_frame - start_frame

        # Add the chunk and its frame count to the list
        chunks.append((chunk, frame_count))

    # If the last chunk is smaller than the chunk size
    if len(chunks) > 0 and chunks[-1][1] < chunk_size:
        # Adjust the end frame of the last chunk to the total frames
        last_chunk = chunks[-1][0]
        last_frame_count = chunks[-1][1]
        chunks[-1] = (video.subclip(last_chunk.t_start, video.duration), last_frame_count)

    return chunks




def run_inference(prompt, video_path, condition, video_length):
    
    output_path = 'output/'
    os.makedirs(output_path, exist_ok=True)

    # Construct the final video path
    video_path_output = 'final_video.mp4'

    # Check if the file already exists
    if os.path.exists(video_path_output):
        # Delete the existing file
        os.remove(video_path_output)

    # Specify the path to your video file
    video_path = video_path

    # Specify the maximum number of frames per chunk
    chunk_size = 12

    # Split the video into chunks
    video_chunks = split_video_into_chunks(video_path, chunk_size)

    # Process each chunk and store the processed chunk filenames
    processed_chunk_filenames = []
    for i, chunk in enumerate(video_chunks):
        # Count the frame number of the video chunk
        frame_count = len(chunk)
        command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{chunk}' --output_path '{output_path}' --video_length {frame_count} --smoother_steps 19 20"
        subprocess.run(command, shell=True)

        # Construct the video path
        video_path_output = os.path.join(output_path, f"{prompt}_{i}.mp4")

        processed_chunk_filename = video_path_output
        processed_chunk_filenames.append(processed_chunk_filename)

    # Load the processed video chunks
    processed_chunks = [VideoFileClip(filename) for filename in processed_chunk_filenames]

    # Concatenate the processed video chunks into a final video
    final_video = concatenate_videoclips(processed_chunks)

    # Export the final video to a file
    final_video.write_videofile('final_video.mp4')

    # Clean up the temporary processed chunk files (optional)
    for filename in processed_chunk_filenames:
        os.remove(filename)

    return "done", 'final_video.mp4' 


with gr.Blocks() as demo:
    with gr.Column():
        prompt = gr.Textbox(label="prompt")
        video_path = gr.Video(source="upload", type="filepath")
        condition = gr.Textbox(label="Condition", value="depth")
        video_length = gr.Slider(label="video length", minimum=1, maximum=15, step=1, value=2)
        #seed = gr.Number(label="seed", value=42)
        submit_btn = gr.Button("Submit")
        video_res = gr.Video(label="result")
        status = gr.Textbox(label="result")
    video_path.change(fn=get_frame_count_in_duration,
                      inputs=[video_path],
                      outputs=[video_length]
                     )
    submit_btn.click(fn=run_inference, 
                     inputs=[prompt,
                             video_path,
                             condition,
                             video_length
                            ],
                    outputs=[status, video_res])

demo.queue(max_size=12).launch()