import gradio as gr

from PIL import Image
from moviepy.editor import VideoFileClip, AudioFileClip

import os
from openai import OpenAI
import subprocess
from pathlib import Path
import uuid
import tempfile
import shlex
import shutil

# Supported models configuration
MODELS = {
    "deepseek-ai/DeepSeek-V3": {
        "base_url": "https://api.deepseek.com/v1",
        "env_key": "DEEPSEEK_API_KEY",
    },
    "Qwen/Qwen2.5-Coder-32B-Instruct": {
        "base_url": "https://api-inference.huggingface.co/v1/",
        "env_key": "HF_TOKEN",
    },
}

# Initialize client with first available model
client = OpenAI(
    base_url=next(iter(MODELS.values()))["base_url"],
    api_key=os.environ[next(iter(MODELS.values()))["env_key"]],
)

allowed_medias = [
    ".png",
    ".jpg",
    ".webp",
    ".jpeg",
    ".tiff",
    ".bmp",
    ".gif",
    ".svg",
    ".mp3",
    ".wav",
    ".ogg",
    ".mp4",
    ".avi",
    ".mov",
    ".mkv",
    ".flv",
    ".wmv",
    ".webm",
    ".mpg",
    ".mpeg",
    ".m4v",
    ".3gp",
    ".3g2",
    ".3gpp",
]


def get_files_infos(files):
    results = []
    for file in files:
        file_path = Path(file.name)
        info = {}
        info["size"] = os.path.getsize(file_path)
        # Sanitize filename by replacing spaces with underscores
        info["name"] = file_path.name.replace(" ", "_")
        file_extension = file_path.suffix

        if file_extension in (".mp4", ".avi", ".mkv", ".mov"):
            info["type"] = "video"
            video = VideoFileClip(file.name)
            info["duration"] = video.duration
            info["dimensions"] = "{}x{}".format(video.size[0], video.size[1])
            if video.audio:
                info["type"] = "video/audio"
                info["audio_channels"] = video.audio.nchannels
            video.close()
        elif file_extension in (".mp3", ".wav"):
            info["type"] = "audio"
            audio = AudioFileClip(file.name)
            info["duration"] = audio.duration
            info["audio_channels"] = audio.nchannels
            audio.close()
        elif file_extension in (
            ".png",
            ".jpg",
            ".jpeg",
            ".tiff",
            ".bmp",
            ".gif",
            ".svg",
        ):
            info["type"] = "image"
            img = Image.open(file.name)
            info["dimensions"] = "{}x{}".format(img.size[0], img.size[1])
        results.append(info)
    return results


def get_completion(prompt, files_info, top_p, temperature, model_choice):
    # Create table header
    files_info_string = "| Type | Name | Dimensions | Duration | Audio Channels |\n"
    files_info_string += "|------|------|------------|-----------|--------|\n"

    # Add each file as a table row
    for file_info in files_info:
        dimensions = file_info.get("dimensions", "-")
        duration = (
            f"{file_info.get('duration', '-')}s" if "duration" in file_info else "-"
        )
        audio = (
            f"{file_info.get('audio_channels', '-')} channels"
            if "audio_channels" in file_info
            else "-"
        )

        files_info_string += f"| {file_info['type']} | {file_info['name']} | {dimensions} | {duration} | {audio} |\n"

    messages = [
        {
            "role": "system",
            "content": """
You are a very experienced media engineer, controlling a UNIX terminal.
You are an FFMPEG expert with years of experience and multiple contributions to the FFMPEG project.

You are given:
(1) a set of video, audio and/or image assets. Including their name, duration, dimensions and file size
(2) the description of a new video you need to create from the list of assets

Your objective is to generate the SIMPLEST POSSIBLE single ffmpeg command to create the requested video.

Key requirements:
    - Use the absolute minimum number of ffmpeg options needed
    - Avoid complex filter chains or filter_complex if possible
    - Prefer simple concatenation, scaling, and basic filters
    - Output exactly ONE command that will be directly pasted into the terminal
    - Never output multiple commands chained together
    - Output the command in a single line (no line breaks or multiple lines)
    - If the user asks for waveform visualization make sure to set the mode to `line` with and the use the full width of the video. Also concatenate the audio into a single channel.
    - For image sequences: Use -framerate and pattern matching (like 'img%d.jpg') when possible, falling back to individual image processing with -loop 1 and appropriate filters only when necessary.
    - When showing file operations or commands, always use explicit paths and filenames without wildcards - avoid using asterisk (*) or glob patterns. Instead, use specific numbered sequences (like %d), explicit file lists, or show the full filename.

Remember: Simpler is better. Only use advanced ffmpeg features if absolutely necessary for the requested output.
""",
        },
        {
            "role": "user",
            "content": f"""Always output the media as video/mp4 and output file with "output.mp4". Provide only the shell command without any explanations.
The current assets and objective follow. Reply with the FFMPEG command:

AVAILABLE ASSETS LIST:

{files_info_string}

OBJECTIVE: {prompt} and output at "output.mp4"
YOUR FFMPEG COMMAND:
         """,
        },
    ]
    try:
        # Print the complete prompt
        print("\n=== COMPLETE PROMPT ===")
        for msg in messages:
            print(f"\n[{msg['role'].upper()}]:")
            print(msg["content"])
        print("=====================\n")

        if model_choice not in MODELS:
            raise ValueError(f"Model {model_choice} is not supported")

        model_config = MODELS[model_choice]
        client.base_url = model_config["base_url"]
        client.api_key = os.environ[model_config["env_key"]]
        model = "deepseek-chat" if "deepseek" in model_choice.lower() else model_choice

        completion = client.chat.completions.create(
            model=model,
            messages=messages,
            temperature=temperature,
            top_p=top_p,
            max_tokens=2048,
        )
        content = completion.choices[0].message.content
        # Extract command from code block if present
        if "```" in content:
            # Find content between ```sh or ```bash and the next ```
            import re

            command = re.search(r"```(?:sh|bash)?\n(.*?)\n```", content, re.DOTALL)
            if command:
                command = command.group(1).strip()
            else:
                command = content.replace("\n", "")
        else:
            command = content.replace("\n", "")

        # remove output.mp4 with the actual output file path
        command = command.replace("output.mp4", "")

        return command
    except Exception as e:
        raise Exception("API Error")


def update(
    files,
    prompt,
    top_p=1,
    temperature=1,
    model_choice="Qwen/Qwen2.5-Coder-32B-Instruct",
):
    if prompt == "":
        raise gr.Error("Please enter a prompt.")

    files_info = get_files_infos(files)
    # disable this if you're running the app locally or on your own server
    for file_info in files_info:
        if file_info["type"] == "video":
            if file_info["duration"] > 120:
                raise gr.Error(
                    "Please make sure all videos are less than 2 minute long."
                )
        if file_info["size"] > 100000000:
            raise gr.Error("Please make sure all files are less than 100MB in size.")

    attempts = 0
    while attempts < 2:
        print("ATTEMPT", attempts)
        try:
            command_string = get_completion(
                prompt, files_info, top_p, temperature, model_choice
            )
            print(
                f"""///PROMTP {prompt} \n\n/// START OF COMMAND ///:\n\n{command_string}\n\n/// END OF COMMAND ///\n\n"""
            )

            # split command string into list of arguments
            args = shlex.split(command_string)
            if args[0] != "ffmpeg":
                raise Exception("Command does not start with ffmpeg")
            temp_dir = tempfile.mkdtemp()
            # copy files to temp dir with sanitized names
            for file in files:
                file_path = Path(file.name)
                sanitized_name = file_path.name.replace(" ", "_")
                shutil.copy(file_path, Path(temp_dir) / sanitized_name)

            # test if ffmpeg command is valid dry run
            ffmpg_dry_run = subprocess.run(
                args + ["-f", "null", "-"],
                stderr=subprocess.PIPE,
                text=True,
                cwd=temp_dir,
            )
            if ffmpg_dry_run.returncode == 0:
                print("Command is valid.")
            else:
                print("Command is not valid. Error output:")
                print(ffmpg_dry_run.stderr)
                raise Exception(
                    "FFMPEG generated command is not valid. Please try something else."
                )

            output_file_name = f"output_{uuid.uuid4()}.mp4"
            output_file_path = str((Path(temp_dir) / output_file_name).resolve())
            final_command = args + ["-y", output_file_path]
            print(
                f"\n=== EXECUTING FFMPEG COMMAND ===\nffmpeg {' '.join(final_command[1:])}\n"
            )
            subprocess.run(final_command, cwd=temp_dir)
            generated_command = f"### Generated Command\n```bash\nffmpeg {' '.join(args[1:])} -y output.mp4\n```"
            return output_file_path, gr.update(value=generated_command)
        except Exception as e:
            attempts += 1
            if attempts >= 2:
                print("FROM UPDATE", e)
                raise gr.Error(e)


with gr.Blocks() as demo:
    gr.Markdown(
        """
            # 🏞 AI Video Editor
            Your advanced video editing assistant powered by AI. Transform, enhance, and edit videos using natural language instructions. Upload your video, image, or audio assets and let [Qwen2.5-Coder](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) or [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base) generate professional-quality video edits using FFMPEG - no coding required!
        """,
        elem_id="header",
    )
    
    with gr.Accordion("📋 Usage Instructions", open=False):
        gr.Markdown(
            """
            ### How to Use AI Video Editor
            
            1. **Upload Media Files**: Add your video, image, or audio files using the upload area
            2. **Write Instructions**: Describe what edits you want in plain English
            3. **Adjust Parameters** (optional): Customize model and generation settings 
            4. **Generate**: Click "Run" and watch your edited video being created
            
            ### Example Instructions
            - "Trim the first 5 seconds of the video"
            - "Add a text overlay with my name at the bottom"
            - "Convert video to black and white"
            - "Combine these videos with a crossfade transition"
            - "Add background music to my slideshow"
            - "Create a picture-in-picture effect"
            
            ### Tips
            - Be specific about timecodes when trimming (e.g., "from 0:15 to 0:45")
            - Include positioning details for overlays (e.g., "top right corner")
            - Specify dimensions if you need to resize (e.g., "scale to 720p")
            """
        )
    
    with gr.Row():
        with gr.Column():
            user_files = gr.File(
                file_count="multiple",
                label="Media files",
                file_types=allowed_medias,
            )
            user_prompt = gr.Textbox(
                placeholder="eg: Remove the 3 first seconds of the video",
                label="Instructions",
            )
            btn = gr.Button("Run")
            with gr.Accordion("Parameters", open=False):
                model_choice = gr.Radio(
                    choices=list(MODELS.keys()),
                    value=list(MODELS.keys())[0],
                    label="Model",
                )
                top_p = gr.Slider(
                    minimum=-0,
                    maximum=1.0,
                    value=0.7,
                    step=0.05,
                    interactive=True,
                    label="Top-p (nucleus sampling)",
                )
                temperature = gr.Slider(
                    minimum=-0,
                    maximum=5.0,
                    value=0.1,
                    step=0.1,
                    interactive=True,
                    label="Temperature",
                )
        with gr.Column():
            generated_video = gr.Video(
                interactive=False, label="Generated Video", include_audio=True
            )
            generated_command = gr.Markdown()

        btn.click(
            fn=update,
            inputs=[user_files, user_prompt, top_p, temperature, model_choice],
            outputs=[generated_video, generated_command],
        )
    with gr.Row():
        gr.Examples(
            examples=[
                [
                    ["./examples/Jiangnan_Rain.mp4"],
                    "Add a text watermark 'Sample Video' to the upper right corner of the video with white text and semi-transparent background.",
                    0.7,
                    0.1,
                    list(MODELS.keys())[0],
                ],
                [
                    ["./examples/Jiangnan_Rain.mp4"],
                    "Cut the video to extract only the middle 30 seconds (starting at 00:30 and ending at 01:00).",
                    0.7,
                    0.1,
                    (
                        list(MODELS.keys())[1]
                        if len(MODELS) > 1
                        else list(MODELS.keys())[0]
                    ),
                ],
                [
                    ["./examples/Lotus_Pond01.mp4"],
                    "Convert the video to black and white (grayscale) while maintaining the original audio.",
                    0.7,
                    0.1,
                    list(MODELS.keys())[0],
                ],
                [
                    ["./examples/Lotus_Pond01.mp4"],
                    "Create a slow-motion version of the video by reducing the speed to 0.5x.",
                    0.7,
                    0.1,
                    (
                        list(MODELS.keys())[1]
                        if len(MODELS) > 1
                        else list(MODELS.keys())[0]
                    ),
                ],
            ],
            inputs=[user_files, user_prompt, top_p, temperature, model_choice],
            outputs=[generated_video, generated_command],
            fn=update,
            run_on_click=True,
            cache_examples=False,
        )

    with gr.Row():
        gr.Markdown(
            """
            If you have idea to improve this please open a PR:

            [![Open a Pull Request](https://huggingface.co/datasets/huggingface/badges/raw/main/open-a-pr-lg-light.svg)](https://huggingface.co/spaces/huggingface-projects/video-composer-gpt4/discussions)
            """,
        )

demo.queue(default_concurrency_limit=200)
demo.launch(show_api=False, ssr_mode=True)