app.py

import spaces
import os
import gradio as gr
import torch
from PIL import Image
from pathlib import Path
import io
import sys
import traceback
from huggingface_hub import hf_hub_download
# For live system monitoring
import psutil
import GPUtil

# =========================================
# 1. Define Hugging Face dataset + weights
# =========================================

HF_DATASET_REPO = "roll-ai/FloVD-weights"

WEIGHT_FILES = {
    "ckpt/FVSM/FloVD_FVSM_Controlnet.pt": "FVSM/FloVD_FVSM_Controlnet.pt",
    "ckpt/OMSM/selected_blocks.safetensors": "OMSM/selected_blocks.safetensors",
    "ckpt/OMSM/pytorch_lora_weights.safetensors": "OMSM/pytorch_lora_weights.safetensors",
    "ckpt/others/depth_anything_v2_metric_hypersim_vitb.pth": "others/depth_anything_v2_metric_hypersim_vitb.pth"
}

print("\nDownloading model...", flush=True)

def download_weights():
    print("🔄 Downloading model weights via huggingface_hub...")
    for hf_path, local_rel_path in WEIGHT_FILES.items():
        local_path = Path("ckpt") / local_rel_path
        if not local_path.exists():
            print(f"📥 Downloading {hf_path}")
            hf_hub_download(
                repo_id=HF_DATASET_REPO,
                repo_type="dataset",
                filename=hf_path,
                local_dir="./"
            )
        else:
            print(f"✅ Already exists: {local_path}")

download_weights()

def print_ckpt_structure(base_path="ckpt"):
    print(f"📂 Listing structure of: {base_path}", flush=True)
    for root, dirs, files in os.walk(base_path):
        level = root.replace(base_path, '').count(os.sep)
        indent = ' ' * 2 * level
        print(f"{indent}📁 {os.path.basename(root)}/", flush=True)
        sub_indent = ' ' * 2 * (level + 1)
        for f in files:
            print(f"{sub_indent}📄 {f}", flush=True)

print_ckpt_structure()

# =========================================
# 2. Import FloVD generation pipeline
# =========================================

from inference.flovd_demo import generate_video

def run_inference(prompt, image, pose_type, speed, use_flow_integration, cam_pose_name):
    log_buffer = io.StringIO()
    sys_stdout = sys.stdout
    sys.stdout = log_buffer

    video_path = None
    try:
        print("🚀 Starting inference...", flush=True)
        os.makedirs("input_images", exist_ok=True)
        image_path = "input_images/input_image.png"

        if not isinstance(image, Image.Image):
            image = Image.fromarray(image.astype("uint8"))

        image.save(image_path)
        print(f"📸 Saved input image to {image_path}", flush=True)

        generate_video(
            prompt=prompt,
            image_path=image_path,
            fvsm_path="./ckpt/FVSM/FloVD_FVSM_Controlnet.pt",
            omsm_path="./ckpt/OMSM",
            output_path="./outputs",
            num_frames=49,
            fps=16,
            width=None,
            height=None,
            seed=42,
            guidance_scale=6.0,
            dtype=torch.float16,
            controlnet_guidance_end=0.4,
            use_dynamic_cfg=False,
            pose_type=pose_type,
            speed=float(speed),
            use_flow_integration=use_flow_integration,
            cam_pose_name=cam_pose_name,
            depth_ckpt_path="./ckpt/others/depth_anything_v2_metric_hypersim_vitb.pth"
        )

        video_name = f"{prompt[:30].strip().replace(' ', '_')}_{cam_pose_name or 'default'}.mp4"
        video_path = f"./outputs/generated_videos/{video_name}"
        print(f"✅ Inference complete. Video saved to {video_path}")

    except Exception:
        print("🔥 Inference failed with exception:")
        traceback.print_exc()

    sys.stdout = sys_stdout
    logs = log_buffer.getvalue()
    log_buffer.close()

    return (video_path if video_path and os.path.exists(video_path) else None), logs


# =========================================
# 3. Define FloVD Gradio Interface
# =========================================
with gr.Blocks() as video_tab:
    gr.Markdown("## 🎥 FloVD: Optical Flow + CogVideoX Video Generation")

    prompt = gr.Textbox(label="Prompt", value="A girl riding a bicycle through a park.")
    image = gr.Image(label="Input Image")
    pose_type = gr.Radio(choices=["manual", "re10k"], value="manual", label="Camera Pose Type")
    speed = gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=0.5, label="Camera Speed")
    use_flow_integration = gr.Checkbox(label="Use Flow Integration", value=False)
    cam_pose_name = gr.Textbox(label="Camera Trajectory", placeholder="e.g., zoom_in, custom_motion, etc.", lines=1)

    generate_btn = gr.Button("🎬 Generate Video")

    video_output = gr.Video(label="Generated Video")
    log_output = gr.Textbox(label="Logs", lines=20, interactive=False)

    generate_btn.click(
        fn=run_inference,
        inputs=[prompt, image, pose_type, speed, use_flow_integration, cam_pose_name],
        outputs=[video_output, log_output]
    )

# =========================================
# 4. Live System Monitor (Fixed)
# =========================================

def get_system_stats():
    cpu = psutil.cpu_percent()
    mem = psutil.virtual_memory()
    disk = psutil.disk_usage('/')
    try:
        gpus = GPUtil.getGPUs()
        gpu_info = "\n".join([
            f"GPU {i}: {gpu.name}, {gpu.memoryUsed}MB / {gpu.memoryTotal}MB, Util: {gpu.load * 100:.1f}%"
            for i, gpu in enumerate(gpus)
        ]) if gpus else "No GPU detected"
    except Exception as e:
        gpu_info = f"GPU info error: {e}"

    return (
        f"🧠 CPU Usage: {cpu}%\n"
        f"💾 RAM: {mem.used / 1e9:.2f} GB / {mem.total / 1e9:.2f} GB ({mem.percent}%)\n"
        f"🗄️ Disk: {disk.used / 1e9:.2f} GB / {disk.total / 1e9:.2f} GB ({disk.percent}%)\n"
        f"🎮 {gpu_info}"
    )

with gr.Blocks() as monitor_tab:
    gr.Markdown("## 📊 Live System Resource Monitor")
    stats_box = gr.Textbox(label="Live Stats", lines=10, interactive=False)

    def update_stats():
        return gr.update(value=get_system_stats())

    stats_btn = gr.Button("🔄 Refresh Stats")
    stats_btn.click(fn=update_stats, outputs=stats_box)

# =========================================
# 5. Combine Tabs: FloVD + Monitor
# =========================================

with gr.Blocks() as app:
    with gr.Tab("🎥 Video Generator"):
        video_tab.render()
    with gr.Tab("📊 System Monitor"):
        monitor_tab.render()

# =========================================
# 6. Launch App
# =========================================

if __name__ == "__main__":
    app.launch(server_name="0.0.0.0", server_port=7860, debug=True, show_error=True)