app.py

import json
import os
import random
import re
import sys
import tempfile
import time
from datetime import datetime
from glob import glob
from pathlib import Path
from typing import List, Optional
from uuid import uuid4
from zipfile import ZipFile

import gradio as gr
import numpy as np
import pandas as pd
import requests
from datasets import load_dataset
from decord import VideoReader, cpu
from huggingface_hub import (
    CommitScheduler,
    HfApi,
    InferenceClient,
    login,
    snapshot_download,
)
from PIL import Image
import concurrent.futures


cached_latest_posts_df = None
cached_top_posts = None
last_fetched = None
last_fetched_top = None


def resize_image(image):
    width, height = image.size
    new_width = width * 0.35
    new_height = height * 0.35
    return image.resize((int(new_width), int(new_height)), Image.BILINEAR)


def get_reddit_id(url):
    # Regular expression pattern for r/GamePhysics URLs and IDs
    pattern = r"https://www\.reddit\.com/r/GamePhysics/comments/([0-9a-zA-Z]+).*|([0-9a-zA-Z]+)"

    # Match the URL or ID against the pattern
    match = re.match(pattern, url)

    if match:
        # Extract the post ID from the URL
        post_id = match.group(1) or match.group(2)
        print(f"Valid GamePhysics post ID: {post_id}")
    else:
        post_id = url

    return post_id


def download_samples(url, video_url, num_frames):
    frames = extract_frames_decord(video_url, num_frames)

    # Create a temporary directory to store the images
    with tempfile.TemporaryDirectory() as temp_dir:
        # Save each frame as a JPEG image in the temporary directory
        for i, frame in enumerate(frames):
            frame_path = os.path.join(temp_dir, f"frame_{i}.jpg")
            frame.save(
                frame_path, format="JPEG", quality=85
            )  # Adjust quality as needed

        # Create a zip file in a persistent location
        post_id = get_reddit_id(url)
        print(f"Creating zip file for post {post_id}")
        zip_path = f"frames-{post_id}.zip"
        with ZipFile(zip_path, "w") as zipf:
            for i in range(num_frames):
                frame_path = os.path.join(temp_dir, f"frame_{i}.jpg")
                zipf.write(frame_path, os.path.basename(frame_path))

    # Return the path of the zip file
    return zip_path


def extract_frames_decord(video_path, num_frames=10):
    try:
        start_time = time.time()

        print(f"Extracting {num_frames} frames from {video_path}")

        # Load the video
        vr = VideoReader(video_path, ctx=cpu(0))

        # Calculate the indices for the frames to be extracted
        total_frames = len(vr)
        frame_indices = np.linspace(
            0, total_frames - 1, num_frames, dtype=int, endpoint=False
        )

        # Extract frames
        batch_frames = vr.get_batch(frame_indices).asnumpy()

        # Convert frames to PIL Images
        frame_images = [
            Image.fromarray(batch_frames[i]) for i in range(batch_frames.shape[0])
        ]

        end_time = time.time()
        print(f"Decord extraction took {end_time - start_time} seconds")

        return frame_images
    except Exception as e:
        raise Exception(f"Error extracting frames from video: {e}")


def extract_frames_decord_preview(video_path, num_frames=10):
    try:
        start_time = time.time()

        print(f"Extracting {num_frames} frames from {video_path}")

        # Load the video
        vr = VideoReader(video_path, ctx=cpu(0))

        # Calculate the indices for the frames to be extracted
        total_frames = len(vr)
        frame_indices = np.linspace(
            0, total_frames - 1, num_frames, dtype=int, endpoint=False
        )

        # Extract frames
        batch_frames = vr.get_batch(frame_indices).asnumpy()

        # Convert frames to PIL Images
        frame_images = [
            Image.fromarray(batch_frames[i]) for i in range(batch_frames.shape[0])
        ]

        end_time = time.time()
        print(f"Decord extraction took {end_time - start_time} seconds")

        # # resize images  to save bandwidth, keep aspect ratio
        # for i, image in enumerate(frame_images):
        #     width, height = image.size
        #     new_width = int(width * 0.35)
        #     new_height = int(height * 0.35)
        #     frame_images[i] = image.resize((new_width, new_height), Image.ANTIALIAS)

        with concurrent.futures.ThreadPoolExecutor() as executor:
            frame_images = list(executor.map(resize_image, frame_images))

        return frame_images
    except Exception as e:
        raise Exception(f"Error extracting frames from video: {e}")


def get_top_posts():
    global cached_top_posts
    global last_fetched_top

    # make sure we don't fetch data too often, limit to 1 request per 10 minutes
    now_time = datetime.now()
    if last_fetched_top is not None and (now_time - last_fetched_top).seconds < 600:
        print("Using cached data")
        return cached_top_posts

    last_fetched_top = now_time
    url = "https://www.reddit.com/r/GamePhysics/top/.json?t=month"
    headers = {"User-Agent": "Mozilla/5.0"}

    response = requests.get(url, headers=headers)
    if response.status_code != 200:
        return []

    data = response.json()

    # Extract posts from the data
    posts = data["data"]["children"]

    for post in posts:
        title = post["data"]["title"]
        post_id = post["data"]["id"]
        # print(f"ID: {post_id}, Title: {title}")

    # create [post_id, title] list
    examples = [[post["data"]["id"], post["data"]["title"]] for post in posts]
    # make a dataframe
    examples = pd.DataFrame(examples, columns=["post_id", "title"])
    cached_top_posts = examples
    return examples


def get_latest_posts():
    global cached_latest_posts_df
    global last_fetched

    # make sure we don't fetch data too often, limit to 1 request per 10 minutes
    now_time = datetime.now()
    if last_fetched is not None and (now_time - last_fetched).seconds < 600:
        print("Using cached data")
        return cached_latest_posts_df

    last_fetched = now_time
    url = "https://www.reddit.com/r/GamePhysics/.json"
    headers = {"User-Agent": "Mozilla/5.0"}

    response = requests.get(url, headers=headers)
    if response.status_code != 200:
        return []

    data = response.json()

    # Extract posts from the data
    posts = data["data"]["children"]

    for post in posts:
        title = post["data"]["title"]
        post_id = post["data"]["id"]
        # print(f"ID: {post_id}, Title: {title}")

    # create [post_id, title] list
    examples = [[post["data"]["id"], post["data"]["title"]] for post in posts]
    # make a dataframe
    examples = pd.DataFrame(examples, columns=["post_id", "title"])
    cached_latest_posts_df = examples
    return examples


def row_selected_top(evt: gr.SelectData):
    global cached_top_posts
    string_value = evt.value
    row = evt.index[0]

    post_id = cached_top_posts.iloc[row]["post_id"]
    return post_id


def row_selected_latest(evt: gr.SelectData):
    global cached_latest_posts_df
    string_value = evt.value
    row = evt.index[0]

    post_id = cached_latest_posts_df.iloc[row]["post_id"]
    return post_id


def load_video(url):
    post_id = get_reddit_id(url)
    video_url = f"https://huggingface.co/datasets/asgaardlab/GamePhysicsDailyDump/resolve/main/data/videos/{post_id}.mp4?download=true"
    video_url2 = f"https://huggingface.co/datasets/asgaardlab/GamePhysics-FullResolution/resolve/main/videos/{post_id}/{post_id}.mp4?download=true"

    # make sure file exists before returning, make a request without downloading the file
    r1 = requests.head(video_url)
    r2 = requests.head(video_url2)

    if (
        r2.status_code != 200
        and r2.status_code != 302
        and r1.status_code != 200
        and r1.status_code != 302
    ):
        raise gr.Error(
            f"Video is not in the repo, please try another post. - {r1.status_code  }"
        )

    if r1.status_code == 200 or r1.status_code == 302:
        return video_url
    else:
        return video_url2


with gr.Blocks() as demo:
    gr.Markdown("## Preview GamePhysics")
    dummt_title = gr.Textbox(visible=False)

    with gr.Row():
        with gr.Column():
            reddit_id = gr.Textbox(
                lines=1, placeholder="Post url or id here", label="URL or Post ID"
            )
            load_btn = gr.Button("Load")
            video_player = gr.Video(interactive=False)

            with gr.Column():
                gr.Markdown("## Sample frames")
                num_frames = gr.Slider(minimum=1, maximum=60, step=1, value=10)
                sample_decord_btn = gr.Button("Sample frames")

                sampled_frames = gr.Gallery(label="Sampled frames preview")

                download_samples_btn = gr.Button("Download Samples")
                output_files = gr.File()

                download_samples_btn.click(
                    download_samples,
                    inputs=[reddit_id, video_player, num_frames],
                    outputs=[output_files],
                )

        with gr.Column():
            gr.Markdown("## Reddits Posts")
            with gr.Tab("Latest Posts"):
                latest_post_dataframe = gr.Dataframe()
                latest_posts_btn = gr.Button("Refresh Latest Posts")
            with gr.Tab("Top Monthly Posts"):
                top_posts_dataframe = gr.Dataframe()
                top_posts_btn = gr.Button("Refresh Top Posts")

    sample_decord_btn.click(
        extract_frames_decord_preview,
        inputs=[video_player, num_frames],
        outputs=[sampled_frames],
    )

    load_btn.click(load_video, inputs=[reddit_id], outputs=[video_player])

    latest_posts_btn.click(get_latest_posts, outputs=[latest_post_dataframe])
    top_posts_btn.click(get_top_posts, outputs=[top_posts_dataframe])

    demo.load(get_latest_posts, outputs=[latest_post_dataframe])
    demo.load(get_top_posts, outputs=[top_posts_dataframe])

    latest_post_dataframe.select(fn=row_selected_latest, outputs=[reddit_id]).then(
        load_video, inputs=[reddit_id], outputs=[video_player]
    )
    top_posts_dataframe.select(fn=row_selected_top, outputs=[reddit_id]).then(
        load_video, inputs=[reddit_id], outputs=[video_player]
    )

    demo.launch()