gradio_app.py

import os
import subprocess
from datetime import datetime
from pathlib import Path
import gradio as gr
import numpy as np
import os
from spaces import GPU
# -----------------------------
# Setup paths and env
# -----------------------------
HF_HOME = "/home/user/app/hf_cache"
os.environ["HF_HOME"] = HF_HOME
os.environ["TRANSFORMERS_CACHE"] = HF_HOME
os.makedirs(HF_HOME, exist_ok=True)

# hf_hub_download(repo_id="ai-forever/Real-ESRGAN", filename="RealESRGAN_x4.pth", local_dir="model_real_esran")
# snapshot_download(repo_id="AlexWortega/RIFE", local_dir="model_rife")

PRETRAINED_DIR = "/home/user/app/pretrained"
os.makedirs(PRETRAINED_DIR, exist_ok=True)

# -----------------------------

# Step 1: Optional Model Download
# -----------------------------
def download_models():
    expected_model = os.path.join(PRETRAINED_DIR, "RAFT/raft-things.pth")
    if not Path(expected_model).exists():
        print("⚙️ Downloading pretrained models...")
        try:
            subprocess.check_call(["bash", "download/download_models.sh"])
            print("✅ Models downloaded.")
        except subprocess.CalledProcessError as e:
            print(f"Model download failed: {e}")
    else:
        print("✅ Pretrained models already exist.")


# -----------------------------
# Step 1: Get Anchor Video
# -----------------------------
def get_anchor_video(video_path, fps, num_frames, target_pose, mode,
                       radius_scale, near_far_estimated,
                       sampler_name, diffusion_guidance_scale, diffusion_inference_steps,
                       prompt, negative_prompt, refine_prompt,
                       depth_inference_steps, depth_guidance_scale,
                       window_size, overlap, max_res, sample_size,
                       seed_input, height, width, aspect_ratio_inputs,
                       init_dx, init_dy, init_dz):

    temp_input_path = "/home/user/app/temp_input.mp4"
    output_dir = "/home/user/app/output_anchor"
    video_output_path = f"{output_dir}/masked_videos/output.mp4"

    if video_path:
        os.system(f"cp '{video_path}' {temp_input_path}")

    try:
        theta, phi, r, x, y = target_pose.strip().split()
    except ValueError:
        return f"Invalid target pose format. Use: θ φ r x y", None, None
    logs =  f"Running inference with target pose: θ={theta}, φ={phi}, r={r}, x={x}, y={y}\n"
    w, h = aspect_ratio_inputs.strip().split(",")
    h_s, w_s = sample_size.strip().split(",")
    
    command = [
        "python", "/home/user/app/inference/v2v_data/inference.py",
        "--video_path", temp_input_path,
        "--stride", "1",
        "--out_dir", output_dir,
        "--radius_scale", str(radius_scale),
        "--camera", "target",
        "--mask",
        "--target_pose", theta, phi, r, x, y,
        "--video_length", str(num_frames),
        "--save_name", "output",
        "--mode", mode,
        "--fps", str(fps),
        "--depth_inference_steps", str(depth_inference_steps),
        "--depth_guidance_scale", str(depth_guidance_scale),
        "--near_far_estimated", str(near_far_estimated),
        "--sampler_name", sampler_name,
        "--diffusion_guidance_scale", str(diffusion_guidance_scale),
        "--diffusion_inference_steps", str(diffusion_inference_steps),
        "--prompt", prompt if prompt else "",
        "--negative_prompt", negative_prompt,
        "--refine_prompt", refine_prompt,
        "--window_size", str(window_size),
        "--overlap", str(overlap),
        "--max_res", str(max_res),
        "--sample_size", h_s.strip(), w_s.strip(),
        "--seed", str(seed_input),
        "--height", str(height),
        "--width", str(width),
        "--target_aspect_ratio", w.strip(), h.strip(),
        "--init_dx", str(init_dx),
        "--init_dy", str(init_dy),
        "--init_dz", str(init_dz),
  
    ]   

    try:
        result = subprocess.run(command, capture_output=True, text=True, check=True)
        logs += result.stdout
    except subprocess.CalledProcessError as e:
        logs += f"Inference failed:\n{e.stderr}{e.stdout}"
        return None, logs

    return str(video_output_path), logs
# -----------------------------
# Step 2: Run Inference
# -----------------------------
@GPU
def inference(
    fps, num_frames, controlnet_weights, controlnet_guidance_start,
    controlnet_guidance_end, guidance_scale, num_inference_steps, dtype,
    seed, height, width, downscale_coef, vae_channels,
    controlnet_input_channels, controlnet_transformer_num_layers
):
    model_path = "/home/user/app/pretrained/CogVideoX-5b-I2V"
    ckpt_path = "/home/user/app/out/EPiC_pretrained/checkpoint-500.pt"
    video_root_dir = "/home/user/app/output_anchor"
    out_dir = "/home/user/app/output"

    command = [
        "python", "/home/user/app/inference/cli_demo_camera_i2v_pcd.py",
        "--video_root_dir", video_root_dir,
        "--base_model_path", model_path,
        "--controlnet_model_path", ckpt_path,
        "--output_path", out_dir,
        "--controlnet_weights", str(controlnet_weights),
        "--controlnet_guidance_start", str(controlnet_guidance_start),
        "--controlnet_guidance_end", str(controlnet_guidance_end),
        "--guidance_scale", str(guidance_scale),
        "--num_inference_steps", str(num_inference_steps),
        "--dtype", dtype,
        "--seed", str(seed),
        "--height", str(height),
        "--width", str(width),
        "--num_frames", str(num_frames),
        "--fps", str(fps),
        "--downscale_coef", str(downscale_coef),
        "--vae_channels", str(vae_channels),
        "--controlnet_input_channels", str(controlnet_input_channels),
        "--controlnet_transformer_num_layers", str(controlnet_transformer_num_layers),

    ]

    try:
        result = subprocess.run(command, capture_output=True, text=True, check=True)
        logs = result.stdout
    except subprocess.CalledProcessError as e:
        logs = f"❌ Step 2 Inference Failed:\nSTDERR:\n{e.stderr}\nSTDOUT:\n{e.stdout}"
        return None, logs
    video_output = f"{out_dir}/00000_{seed}_out.mp4"
    return video_output if os.path.exists(video_output) else None, logs

# -----------------------------
# UI
# -----------------------------
demo = gr.Blocks()

with demo:
    gr.Markdown("## 🎬 EPiC: Cinematic Camera Control")

    with gr.Tabs():
        with gr.TabItem("Step 1: Camera Anchor"):
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        near_far_estimated = gr.Checkbox(label="Near Far Estimation", value=True) 
                        pose_input = gr.Textbox(label="Target Pose (θ φ r x y)", placeholder="e.g., 0 30 -0.6 0 0")
                        fps_input = gr.Number(value=24, label="FPS")
                        aspect_ratio_inputs=gr.Textbox(label="Target Aspect Ratio (e.g., 2,3)")

                        init_dx = gr.Number(value=0.0, label="Start Camera Offset X")
                        init_dy = gr.Number(value=0.0, label="Start Camera Offset Y")
                        init_dz = gr.Number(value=0.0, label="Start Camera Offset Z")

                        num_frames_input = gr.Number(value=49, label="Number of Frames")
                        radius_input = gr.Number(value = 1.0, label="Radius Scale")
                        mode_input = gr.Dropdown(choices=["gradual"], value="gradual", label="Camera Mode")
                        sampler_input = gr.Dropdown(choices=["Euler", "Euler A", "DPM++", "PNDM", "DDIM_Cog", "DDIM_Origin"], value="DDIM_Origin", label="Sampler")
                        diff_guidance_input = gr.Number(value=6.0, label="Diffusion Guidance")
                        diff_steps_input = gr.Number(value=50, label="Diffusion Steps")
                        depth_steps_input = gr.Number(value=5, label="Depth Steps")
                        depth_guidance_input = gr.Number(value=1.0, label="Depth Guidance")
                        window_input = gr.Number(value=64, label="Window Size")    
                        overlap_input = gr.Number(value=25, label="Overlap")
                        maxres_input = gr.Number(value=1920, label="Max Resolution")
                        sample_size = gr.Textbox(label="Sample Size (height, width)", placeholder="e.g., 384, 672", value="384, 672")
                        seed_input = gr.Number(value=43, label="Seed")
                        height = gr.Number(value=480, label="Height")
                        width = gr.Number(value=720, label="Width")
                        prompt_input = gr.Textbox(label="Prompt")
                        neg_prompt_input = gr.Textbox(label="Negative Prompt", value="The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory.")
                        refine_prompt_input = gr.Textbox(label="Refine Prompt", value=" The video is of high quality, and the view is very clear. ")
                with gr.Column():
                    video_input = gr.Video(label="Upload Video (MP4)")
                    step1_button = gr.Button("▶️ Run Step 1")
                    step1_video = gr.Video(label="[Step 1] Masked Video")
                    step1_logs = gr.Textbox(label="[Step 1] Logs")

        with gr.TabItem("Step 2: CogVideoX Refinement"):
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                 
                        controlnet_weights_input = gr.Number(value=0.5, label="ControlNet Weights")
                        controlnet_guidance_start_input = gr.Number(value=0.0, label="Guidance Start")
                        controlnet_guidance_end_input = gr.Number(value=0.5, label="Guidance End")
                        guidance_scale_input = gr.Number(value=6.0, label="Guidance Scale")
                        inference_steps_input = gr.Number(value=50, label="Num Inference Steps")
                        dtype_input = gr.Dropdown(choices=["float16", "bfloat16"], value="bfloat16", label="Compute Dtype")
                        seed_input2 = gr.Number(value=42, label="Seed")
                        height_input = gr.Number(value=480, label="Height")
                        width_input = gr.Number(value=720, label="Width")
                        num_frames_input2 = gr.Number(value=49, label="Num Frames")
                        fps_input2 = gr.Number(value=24, label="FPS")
                        downscale_coef_input = gr.Number(value=8, label="Downscale Coef")
                        vae_channels_input = gr.Number(value=16, label="VAE Channels")
                        controlnet_input_channels_input = gr.Number(value=6, label="ControlNet Input Channels")
                        controlnet_layers_input = gr.Number(value=8, label="ControlNet Transformer Layers")
                with gr.Column():
                    step2_video = gr.Video(label="[Step 2] Final Refined Video")
                    step2_button = gr.Button("▶️ Run Step 2")
                    step2_logs = gr.Textbox(label="[Step 2] Logs")


    step1_button.click(
        get_anchor_video,
        inputs=[
            video_input, fps_input, num_frames_input, pose_input, mode_input,
            radius_input, near_far_estimated,
            sampler_input, diff_guidance_input, diff_steps_input,
            prompt_input, neg_prompt_input, refine_prompt_input,
            depth_steps_input, depth_guidance_input,
            window_input, overlap_input, maxres_input, sample_size,
            seed_input, height, width, aspect_ratio_inputs,
            init_dx, init_dy, init_dz  # ← NEW INPUTS
        ],
        outputs=[step1_video, step1_logs]
    )

    step2_button.click(
        inference,
        inputs=[
            fps_input2, num_frames_input2,
            controlnet_weights_input, controlnet_guidance_start_input,
            controlnet_guidance_end_input, guidance_scale_input,
            inference_steps_input, dtype_input, seed_input2,
            height_input, width_input, downscale_coef_input,
            vae_channels_input, controlnet_input_channels_input,
            controlnet_layers_input
        ],
        outputs=[step2_video, step2_logs]
    )

if __name__ == "__main__":
    print("Current working directory:", os.getcwd())
    download_models()
    demo.launch(server_name="0.0.0.0", server_port=7860)