app.py

import gradio as gr
import spaces

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",
    },
}

# 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, previous_error=None, previous_command=None):
    # 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"

    # Build the user message with optional error feedback
    user_content = f"""Always output the media as video/mp4 and output file with "output.mp4".
The current assets and objective follow.

AVAILABLE ASSETS LIST:

{files_info_string}

OBJECTIVE: {prompt} and output at "output.mp4"

First, think step-by-step about what I'm asking for and reformulate it into a clear technical specification.
Then provide the FFMPEG command that will accomplish this task."""

    # Add error feedback if this is a retry
    if previous_error and previous_command:
        user_content += f"""

IMPORTANT: This is a retry attempt. The previous command failed with the following error:

PREVIOUS COMMAND (FAILED):
{previous_command}

ERROR MESSAGE:
{previous_error}

Please analyze the error and generate a corrected command that addresses the specific issue."""

    user_content += "\n\nYOUR RESPONSE:"

    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:
    - First, think step-by-step about what the user is asking for and reformulate it into a clear technical specification
    - 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": user_content,
        },
    ]
    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_match = re.search(
                r"```(?:sh|bash)?\n(.*?)\n```", content, re.DOTALL
            )
            if command_match:
                command = command_match.group(1).strip()
            else:
                # Try to find a line that starts with ffmpeg
                ffmpeg_lines = [
                    line.strip()
                    for line in content.split("\n")
                    if line.strip().startswith("ffmpeg")
                ]
                if ffmpeg_lines:
                    command = ffmpeg_lines[0]
                else:
                    command = content.replace("\n", "")
        else:
            # Try to find a line that starts with ffmpeg
            ffmpeg_lines = [
                line.strip()
                for line in content.split("\n")
                if line.strip().startswith("ffmpeg")
            ]
            if ffmpeg_lines:
                command = ffmpeg_lines[0]
            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")


@spaces.GPU(duration=120)
def execute_ffmpeg_command(args, temp_dir, output_file_path):
    """Execute FFmpeg command with GPU acceleration"""
    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)
    return output_file_path


def compose_video(
    prompt: str,
    files: list = None,
    top_p: float = 0.7,
    temperature: float = 0.1,
    model_choice: str = "deepseek-ai/DeepSeek-V3",
) -> str:
    """
    Compose a video from media assets using natural language instructions.

    This tool generates FFmpeg commands using AI and executes them to create videos
    from uploaded images, videos, and audio files based on natural language descriptions.

    Args:
        prompt (str): Natural language instructions for video composition (e.g., "Create a slideshow with background music")
        files (list, optional): List of media files (images, videos, audio) to use
        top_p (float): Top-p sampling parameter for AI model (0.0-1.0, default: 0.7)
        temperature (float): Temperature parameter for AI model creativity (0.0-5.0, default: 0.1)
        model_choice (str): AI model to use for command generation (default: "deepseek-ai/DeepSeek-V3")

    Returns:
        str: Path to the generated video file

    Example:
        compose_video("Create a 10-second slideshow from the images with fade transitions", files=[img1, img2, img3])
    """
    return update(files or [], prompt, top_p, temperature, model_choice)


def update(
    files,
    prompt,
    top_p=1,
    temperature=1,
    model_choice="deepseek-ai/DeepSeek-V3",
):
    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
    command_attempts = []
    previous_error = None
    previous_command = None
    
    while attempts < 2:
        print("ATTEMPT", attempts + 1)
        try:
            command_string = get_completion(
                prompt, files_info, top_p, temperature, model_choice, previous_error, previous_command
            )
            print(
                f"""///PROMPT {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
            ffmpeg_dry_run = subprocess.run(
                args + ["-f", "null", "-"],
                stderr=subprocess.PIPE,
                text=True,
                cwd=temp_dir,
            )
            
            # Extract command for display
            command_for_display = f"ffmpeg {' '.join(args[1:])} -y output.mp4"
            
            if ffmpeg_dry_run.returncode == 0:
                print("Command is valid.")
                # Add successful command to attempts
                command_attempts.append({
                    "command": command_for_display,
                    "status": "✅ Valid",
                    "attempt": attempts + 1
                })
            else:
                print("Command is not valid. Error output:")
                print(ffmpeg_dry_run.stderr)
                
                # Add failed command to attempts with error
                command_attempts.append({
                    "command": command_for_display,
                    "status": "❌ Invalid",
                    "error": ffmpeg_dry_run.stderr,
                    "attempt": attempts + 1
                })
                
                # Store error details for next retry
                previous_error = ffmpeg_dry_run.stderr
                previous_command = command_for_display
                
                raise Exception(f"FFMPEG command validation failed: {ffmpeg_dry_run.stderr}")

            output_file_name = f"output_{uuid.uuid4()}.mp4"
            output_file_path = str((Path(temp_dir) / output_file_name).resolve())
            execute_ffmpeg_command(args, temp_dir, output_file_path)
            
            # Generate command display with all attempts
            command_display = generate_command_display(command_attempts)
            return output_file_path, gr.update(value=command_display)
            
        except Exception as e:
            attempts += 1
            if attempts >= 2:
                print("FROM UPDATE", e)
                # Show all attempted commands even on final failure
                command_display = generate_command_display(command_attempts)
                command_display += f"\n\n### Final Error\n❌ All attempts failed. Last error: {str(e)}"
                return None, gr.update(value=command_display)


def generate_command_display(command_attempts):
    """Generate a markdown display of all command attempts"""
    if not command_attempts:
        return "### No commands generated"
    
    display = "### Generated Commands\n\n"
    
    for attempt in command_attempts:
        display += f"**Attempt {attempt['attempt']}** {attempt['status']}\n"
        display += f"```bash\n{attempt['command']}\n```\n"
        
        if attempt['status'] == "❌ Invalid" and 'error' in attempt:
            display += f"<details>\n<summary>🔍 Error Details</summary>\n\n```\n{attempt['error']}\n```\n</details>\n\n"
        else:
            display += "\n"
    
    return display


# Create MCP-compatible interface
mcp_interface = gr.Interface(
    fn=compose_video,
    inputs=[
        gr.Textbox(
            value="Create a slideshow with background music",
            label="Video Composition Instructions",
        ),
        gr.File(file_count="multiple", label="Media Files", file_types=allowed_medias),
        gr.Slider(0.0, 1.0, value=0.7, label="Top-p"),
        gr.Slider(0.0, 5.0, value=0.1, label="Temperature"),
        gr.Radio(
            choices=list(MODELS.keys()), value=list(MODELS.keys())[0], label="Model"
        ),
    ],
    outputs=gr.Video(label="Generated Video"),
    title="AI Video Composer MCP Tool",
    description="Compose videos from media assets using natural language",
)

with gr.Blocks() as demo:
    gr.Markdown(
        """
            # 🏞 AI Video Composer
            Compose new videos from your assets using natural language. Add video, image and audio assets and let [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base) generate a new video for you (using FFMPEG).
        """,
        elem_id="header",
    )
    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/ai_talk.wav", "./examples/bg-image.png"],
                    "Use the image as the background with a waveform visualization for the audio positioned in center of the video.",
                    0.7,
                    0.1,
                    list(MODELS.keys())[0],
                ],
                [
                    ["./examples/ai_talk.wav", "./examples/bg-image.png"],
                    "Use the image as the background with a waveform visualization for the audio positioned in center of the video. Make sure the waveform has a max height of 250 pixels.",
                    0.7,
                    0.1,
                    list(MODELS.keys())[0],
                ],
                [
                    [
                        "./examples/cat1.jpeg",
                        "./examples/cat2.jpeg",
                        "./examples/cat3.jpeg",
                        "./examples/cat4.jpeg",
                        "./examples/cat5.jpeg",
                        "./examples/cat6.jpeg",
                        "./examples/heat-wave.mp3",
                    ],
                    "Create a 3x2 grid of the cat images with the audio as background music. Make the video duration match the audio duration.",
                    0.7,
                    0.1,
                    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)
            """,
        )

# Launch MCP interface for tool access
mcp_interface.queue(default_concurrency_limit=200)

# Launch main demo
demo.queue(default_concurrency_limit=200)
demo.launch(show_api=False, ssr_mode=False, mcp_server=True)