app.py

import os
import json
import gradio as gr
import tempfile
import torch
import spaces
from pathlib import Path
from transformers import AutoProcessor, AutoModelForImageTextToText
import subprocess
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def load_examples(json_path: str) -> dict:
    with open(json_path, 'r') as f:
        return json.load(f)

def format_duration(seconds: int) -> str:
    hours = seconds // 3600
    minutes = (seconds % 3600) // 60
    secs = seconds % 60
    if hours > 0:
        return f"{hours}:{minutes:02d}:{secs:02d}"
    return f"{minutes}:{secs:02d}"

def get_video_duration_seconds(video_path: str) -> float:
    """Use ffprobe to get video duration in seconds."""
    cmd = [
        "ffprobe",
        "-v", "quiet",
        "-print_format", "json",
        "-show_format",
        video_path
    ]
    result = subprocess.run(cmd, capture_output=True, text=True)
    info = json.loads(result.stdout)
    return float(info["format"]["duration"])

class VideoHighlightDetector:
    def __init__(
        self,
        model_path: str,
        device: str = "cuda",
        batch_size: int = 8
    ):
        self.device = device
        self.batch_size = batch_size
        
        # Initialize model and processor
        self.processor = AutoProcessor.from_pretrained(model_path)
        self.model = AutoModelForImageTextToText.from_pretrained(
            model_path,
            torch_dtype=torch.bfloat16,
            # _attn_implementation="flash_attention_2"
        ).to(device)
        
    def analyze_video_content(self, video_path: str) -> str:
        """Analyze video content to determine its type and description."""
        system_message = "You are a helpful assistant that can understand videos. Describe what type of video this is and what's happening in it."
        messages = [
            {
                "role": "system",
                "content": [{"type": "text", "text": system_message}]
            },
            {
                "role": "user",
                "content": [
                    {"type": "video", "path": video_path},
                    {"type": "text", "text": "What type of video is this and what's happening in it? Be specific about the content type and general activities you observe."}
                ]
            }
        ]
        
        inputs = self.processor.apply_chat_template(
            messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            return_tensors="pt"
        ).to(self.device)
        
        outputs = self.model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.7)
        return self.processor.decode(outputs[0], skip_special_tokens=True).lower().split("assistant: ")[1]

    def determine_highlights(self, video_description: str, prompt_num: int = 1) -> str:
        """Determine what constitutes highlights based on video description with different prompts."""
        system_prompts = {
            1: "You are a highlight editor. List archetypal dramatic moments that would make compelling highlights if they appear in the video. Each moment should be specific enough to be recognizable but generic enough to potentially exist in other videos of this type.",
            2: "You are a helpful visual-language assistant that can understand videos and edit. You are tasked helping the user to create highlight reels for videos. Highlights should be rare and important events in the video in question."
        }
        user_prompts = {
            1: "List potential highlight moments to look for in this video:",
            2: "List dramatic moments that would make compelling highlights if they appear in the video. Each moment should be specific enough to be recognizable but generic enough to potentially exist in any video of this type:"
        }

        
        messages = [
            {
                "role": "system",
                "content": [{"type": "text", "text": system_prompts[prompt_num]}]
            },
            {
                "role": "user",
                "content": [{"type": "text", "text": f"""Here is a description of a video:\n\n{video_description}\n\n{user_prompts[prompt_num]}"""}]
            }
        ]

        print(f"Using prompt {prompt_num} for highlight detection")
        print(messages)
        
        inputs = self.processor.apply_chat_template(
            messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            return_tensors="pt"
        ).to(self.device)
        
        outputs = self.model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
        return self.processor.decode(outputs[0], skip_special_tokens=True).split("Assistant: ")[1]

    def process_segment(self, video_path: str, highlight_types: str) -> bool:
        """Process a video segment and determine if it contains highlights."""
        messages = [
            {
                "role": "system",
                "content": [{"type": "text", "text": "You are a video highlight analyzer. Your role is to identify moments that have high dramatic value, focusing on displays of skill, emotion, personality, or tension. Compare video segments against provided example highlights to find moments with similar emotional impact and visual interest, even if the specific actions differ."}]
            },
            {
                "role": "user",
                "content": [
                    {"type": "video", "path": video_path},
                    {"type": "text", "text": f"""Given these highlight examples:\n{highlight_types}\n\nDoes this video contain a moment that matches the core action of one of the highlights? Answer with:\n'yes' or 'no'\nIf yes, justify it"""}]
            }
        ]


        print(messages)

        
        inputs = self.processor.apply_chat_template(
            messages,
            add_generation_prompt=True,
            tokenize=True,
            return_dict=True,
            return_tensors="pt"
        ).to(self.device)
        
        outputs = self.model.generate(**inputs, max_new_tokens=64, do_sample=False)
        response = self.processor.decode(outputs[0], skip_special_tokens=True).lower().split("assistant: ")[1]
        print(f"Segment response {response}")
        return "yes" in response

    def _concatenate_scenes(
        self,
        video_path: str,
        scene_times: list,
        output_path: str
    ):
        """Concatenate selected scenes into final video."""
        if not scene_times:
            logger.warning("No scenes to concatenate, skipping.")
            return

        filter_complex_parts = []
        concat_inputs = []
        for i, (start_sec, end_sec) in enumerate(scene_times):
            filter_complex_parts.append(
                f"[0:v]trim=start={start_sec}:end={end_sec},"
                f"setpts=PTS-STARTPTS[v{i}];"
            )
            filter_complex_parts.append(
                f"[0:a]atrim=start={start_sec}:end={end_sec},"
                f"asetpts=PTS-STARTPTS[a{i}];"
            )
            concat_inputs.append(f"[v{i}][a{i}]")

        concat_filter = f"{''.join(concat_inputs)}concat=n={len(scene_times)}:v=1:a=1[outv][outa]"
        filter_complex = "".join(filter_complex_parts) + concat_filter

        cmd = [
            "ffmpeg",
            "-y",
            "-i", video_path,
            "-filter_complex", filter_complex,
            "-map", "[outv]",
            "-map", "[outa]",
            "-c:v", "libx264",
            "-c:a", "aac",
            output_path
        ]

        logger.info(f"Running ffmpeg command: {' '.join(cmd)}")
        subprocess.run(cmd, check=True)

def create_ui(examples_path: str, model_path: str):
    examples_data = load_examples(examples_path)

    with gr.Blocks() as app:
        gr.Markdown("# Video Highlight Generator")
        gr.Markdown("Upload a video and get an automated highlight reel!")
        
        with gr.Row():
            gr.Markdown("## Example Results")

        with gr.Row():
            for example in examples_data["examples"]:
                with gr.Column():
                    gr.Video(
                        value=example["original"]["url"],
                        label=f"Original ({format_duration(example['original']['duration_seconds'])})",
                        interactive=False
                    )
                    gr.Markdown(f"### {example['title']}")
                
                with gr.Column():
                    gr.Video(
                        value=example["highlights"]["url"],
                        label=f"Highlights ({format_duration(example['highlights']['duration_seconds'])})",
                        interactive=False
                    )
                    with gr.Accordion("Chain of thought details", open=False):
                        gr.Markdown(f"### Summary:\n{example['analysis']['video_description']}")
                        gr.Markdown(f"### Highlights to search for:\n{example['analysis']['highlight_types']}")

        gr.Markdown("## Try It Yourself!")
        with gr.Row():
            with gr.Column(scale=1):
                input_video = gr.Video(
                    label="Upload your video (max 30 minutes)",
                    interactive=True
                )
                process_btn = gr.Button("Process Video", variant="primary")
            
            with gr.Column(scale=1):
                output_video = gr.Video(
                    label="Highlight Video",
                    visible=False,
                    interactive=False,
                )

                status = gr.Markdown()
        
                analysis_accordion = gr.Accordion(
                    "Chain of thought details", 
                    open=True, 
                    visible=False
                )
                
                with analysis_accordion:
                    video_description = gr.Markdown("", elem_id="video_desc")
                    highlight_types = gr.Markdown("", elem_id="highlight_types")

        @spaces.GPU
        def on_process(video):
            # Clear all components when starting new processing
            yield [
                "",  # Clear status
                "",  # Clear video description
                "",  # Clear highlight types
                gr.update(value=None, visible=False),  # Clear video
                gr.update(visible=False)  # Hide accordion
            ]
            
            if not video:
                yield [
                    "Please upload a video",
                    "",
                    "",
                    gr.update(visible=False),
                    gr.update(visible=False)
                ]
                return
            
            try:
                duration = get_video_duration_seconds(video)
                if duration > 1800:  # 30 minutes
                    yield [
                        "Video must be shorter than 30 minutes",
                        "",
                        "",
                        gr.update(visible=False),
                        gr.update(visible=False)
                    ]
                    return

                yield [
                    "Initializing video highlight detector...",
                    "",
                    "",
                    gr.update(visible=False),
                    gr.update(visible=False)
                ]

                detector = VideoHighlightDetector(
                    model_path=model_path,
                    batch_size=16
                )

                yield [
                    "Analyzing video content...",
                    "",
                    "",
                    gr.update(visible=False),
                    gr.update(visible=True)
                ]
                
                video_desc = detector.analyze_video_content(video)
                formatted_desc = f"### Summary:\n {video_desc[:500] + '...' if len(video_desc) > 500 else video_desc}"
                
                yield [
                    "Determining highlight types (2 variations)...",
                    formatted_desc,
                    "",
                    gr.update(visible=False),
                    gr.update(visible=True)
                ]
                
                # Get two different sets of highlights
                highlights1 = detector.determine_highlights(video_desc, prompt_num=1)
                highlights2 = detector.determine_highlights(video_desc, prompt_num=2)
                formatted_highlights = f"### Highlights to search for:\nSet 1:\n{highlights1[:500] + '...' if len(highlights1) > 500 else highlights1}\n\nSet 2:\n{highlights2[:500] + '...' if len(highlights2) > 500 else highlights2}"

                # Split video into segments
                temp_dir = "temp_segments"
                os.makedirs(temp_dir, exist_ok=True)
                
                segment_length = 10.0
                duration = get_video_duration_seconds(video)
                kept_segments1 = []
                kept_segments2 = []
                segments_processed = 0
                total_segments = int(duration / segment_length)

                for start_time in range(0, int(duration), int(segment_length)):
                    progress = int((segments_processed / total_segments) * 100)
                    
                    yield [
                        f"Processing segments... {progress}% complete",
                        formatted_desc,
                        formatted_highlights,
                        gr.update(visible=False),
                        gr.update(visible=True)
                    ]

                    # Create segment
                    segment_path = f"{temp_dir}/segment_{start_time}.mp4"
                    end_time = min(start_time + segment_length, duration)
                    
                    cmd = [
                        "ffmpeg",
                        "-y",
                        "-i", video,
                        "-ss", str(start_time),
                        "-t", str(segment_length),
                        "-c:v", "libx264",
                        "-preset", "ultrafast",  # Use ultrafast preset for speed
                        "-pix_fmt", "yuv420p",   # Ensure compatible pixel format
                        segment_path
                    ]
                    subprocess.run(cmd, check=True)

                    # Process segment with both highlight sets
                    if detector.process_segment(segment_path, highlights1):
                        print("KEEPING SEGMENT FOR SET 1")
                        kept_segments1.append((start_time, end_time))
                    
                    if detector.process_segment(segment_path, highlights2):
                        print("KEEPING SEGMENT FOR SET 2")
                        kept_segments2.append((start_time, end_time))
                    
                    # Clean up segment file
                    os.remove(segment_path)
                    segments_processed += 1


                # Remove temp directory
                os.rmdir(temp_dir)

                # Calculate percentages of video kept for each highlight set
                total_duration = duration
                duration1 = sum(end - start for start, end in kept_segments1)
                duration2 = sum(end - start for start, end in kept_segments2)
                
                percent1 = (duration1 / total_duration) * 100
                percent2 = (duration2 / total_duration) * 100
                
                print(f"Highlight set 1: {percent1:.1f}% of video")
                print(f"Highlight set 2: {percent2:.1f}% of video")

                # Choose the set with lower percentage unless it's zero
                final_segments = kept_segments2 if (0 < percent2 <= percent1 or percent1 == 0) else kept_segments1

                # Create final video
                if final_segments:
                    with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as tmp_file:
                        temp_output = tmp_file.name
                        detector._concatenate_scenes(video, final_segments, temp_output)
                    
                    selected_set = "2" if final_segments == kept_segments2 else "1"
                    percent_used = percent2 if final_segments == kept_segments2 else percent1
                    
                    completion_message = f"Processing complete! Used highlight set {selected_set} ({percent_used:.1f}% of video)"
                    
                    yield [
                        completion_message,
                        formatted_desc,
                        formatted_highlights,
                        gr.update(value=temp_output, visible=True),
                        gr.update(visible=True)
                    ]
                else:
                    yield [
                        "No highlights detected in the video with either set of criteria.",
                        formatted_desc,
                        formatted_highlights,
                        gr.update(visible=False),
                        gr.update(visible=True)
                    ]

            except Exception as e:
                logger.exception("Error processing video")
                yield [
                    f"Error processing video: {str(e)}",
                    "",
                    "",
                    gr.update(visible=False),
                    gr.update(visible=False)
                ]
            finally:
                # Clean up
                torch.cuda.empty_cache()


        process_btn.click(
            on_process,
            inputs=[input_video],
            outputs=[
                status,
                video_description,
                highlight_types,
                output_video,
                analysis_accordion
            ],
            queue=True,
        )

    return app

if __name__ == "__main__":
    # subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

    # Initialize CUDA
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    app = create_ui("video_spec.json", "HuggingFaceTB/SmolVLM2-2.2B-Instruct")
    app.launch()