"""
Hugging Face Space for STARFlow
Text-to-Image and Text-to-Video Generation

This app allows you to run STARFlow inference on Hugging Face GPU infrastructure.
"""

import os
import gradio as gr
import torch
import subprocess
import pathlib
from pathlib import Path

# Try to import huggingface_hub for downloading checkpoints
try:
    from huggingface_hub import hf_hub_download
    HF_HUB_AVAILABLE = True
except ImportError:
    HF_HUB_AVAILABLE = False
    print("⚠️  huggingface_hub not available. Install with: pip install huggingface_hub")

# Check if running on Hugging Face Spaces
HF_SPACE = os.environ.get("SPACE_ID") is not None

# Default checkpoint paths (if uploaded to Space Files)
DEFAULT_IMAGE_CHECKPOINT = "ckpts/starflow_3B_t2i_256x256.pth"
DEFAULT_VIDEO_CHECKPOINT = "ckpts/starflow-v_7B_t2v_caus_480p_v3.pth"

# Model Hub repositories (if using Hugging Face Model Hub)
# Set these to your Model Hub repo IDs if you upload checkpoints there
# Format: "username/repo-name"
IMAGE_CHECKPOINT_REPO = "GlobalStudio/starflow-3b-checkpoint"  # Update this after creating Model Hub repo
VIDEO_CHECKPOINT_REPO = "GlobalStudio/starflow-v-7b-checkpoint"  # Update this after creating Model Hub repo

def get_checkpoint_path(checkpoint_file, default_local_path, repo_id=None, filename=None):
    """Get checkpoint path, downloading from Hub if needed."""
    # If user uploaded a file, use it
    if checkpoint_file is not None:
        if hasattr(checkpoint_file, 'name'):
            return checkpoint_file.name
        return str(checkpoint_file)
    
    # Try local path first
    if os.path.exists(default_local_path):
        return default_local_path
    
    # Try downloading from Model Hub if configured
    if repo_id and filename and HF_HUB_AVAILABLE:
        try:
            print(f"📥 Downloading checkpoint from {repo_id}...")
            checkpoint_path = hf_hub_download(
                repo_id=repo_id,
                filename=filename,
                cache_dir="/tmp/checkpoints",
                local_files_only=False
            )
            print(f"✅ Checkpoint downloaded to: {checkpoint_path}")
            return checkpoint_path
        except Exception as e:
            return None, f"Error downloading checkpoint: {str(e)}"
    
    # No checkpoint found
    return None, f"Checkpoint not found. Please upload a checkpoint file or configure Model Hub repository."

# Verify CUDA availability (will be True on HF Spaces with GPU hardware)
if torch.cuda.is_available():
    print(f"✅ CUDA available! Device: {torch.cuda.get_device_name(0)}")
    print(f"   CUDA Version: {torch.version.cuda}")
    print(f"   PyTorch Version: {torch.__version__}")
else:
    print("⚠️  CUDA not available. Make sure GPU hardware is selected in Space settings.")

def generate_image(prompt, aspect_ratio, cfg, seed, checkpoint_file, config_path):
    """Generate image from text prompt."""
    # Get checkpoint path (from upload, local, or Model Hub)
    result = get_checkpoint_path(
        checkpoint_file, 
        DEFAULT_IMAGE_CHECKPOINT,
        IMAGE_CHECKPOINT_REPO,
        "starflow_3B_t2i_256x256.pth"
    )
    
    if isinstance(result, tuple) and result[0] is None:
        return None, result[1]  # Error message
    
    checkpoint_path = result
    if not os.path.exists(checkpoint_path):
        return None, f"Error: Checkpoint file not found at {checkpoint_path}."
    
    if not config_path or not os.path.exists(config_path):
        return None, "Error: Config file not found. Please ensure config file exists."
    
    try:
        # Create output directory
        output_dir = Path("outputs")
        output_dir.mkdir(exist_ok=True)
        
        # Run sampling command
        cmd = [
            "python", "sample.py",
            "--model_config_path", config_path,
            "--checkpoint_path", checkpoint_path,
            "--caption", prompt,
            "--sample_batch_size", "1",
            "--cfg", str(cfg),
            "--aspect_ratio", aspect_ratio,
            "--seed", str(seed),
            "--save_folder", "1",
            "--finetuned_vae", "none",
            "--jacobi", "1",
            "--jacobi_th", "0.001",
            "--jacobi_block_size", "16"
        ]
        
        result = subprocess.run(cmd, capture_output=True, text=True, cwd=os.getcwd())
        
        if result.returncode != 0:
            return None, f"Error: {result.stderr}"
        
        # Find the generated image
        # The sample.py script saves to logdir/model_name/...
        # We need to find the most recent output
        output_files = list(output_dir.glob("**/*.png")) + list(output_dir.glob("**/*.jpg"))
        if output_files:
            latest_file = max(output_files, key=lambda p: p.stat().st_mtime)
            return str(latest_file), "Success! Image generated."
        else:
            return None, "Error: Generated image not found."
            
    except Exception as e:
        return None, f"Error: {str(e)}"


def generate_video(prompt, aspect_ratio, cfg, seed, target_length, checkpoint_file, config_path, input_image):
    """Generate video from text prompt."""
    # Get checkpoint path (from upload, local, or Model Hub)
    result = get_checkpoint_path(
        checkpoint_file,
        DEFAULT_VIDEO_CHECKPOINT,
        VIDEO_CHECKPOINT_REPO,
        "starflow-v_7B_t2v_caus_480p_v3.pth"
    )
    
    if isinstance(result, tuple) and result[0] is None:
        return None, result[1]  # Error message
    
    checkpoint_path = result
    if not os.path.exists(checkpoint_path):
        return None, f"Error: Checkpoint file not found at {checkpoint_path}."
    
    if not config_path or not os.path.exists(config_path):
        return None, "Error: Config file not found. Please ensure config file exists."
    
    # Handle input image
    input_image_path = None
    if input_image is not None:
        if hasattr(input_image, 'name'):
            input_image_path = input_image.name
        else:
            input_image_path = str(input_image)
    
    try:
        # Create output directory
        output_dir = Path("outputs")
        output_dir.mkdir(exist_ok=True)
        
        # Run sampling command
        cmd = [
            "python", "sample.py",
            "--model_config_path", config_path,
            "--checkpoint_path", checkpoint_path,
            "--caption", prompt,
            "--sample_batch_size", "1",
            "--cfg", str(cfg),
            "--aspect_ratio", aspect_ratio,
            "--seed", str(seed),
            "--out_fps", "16",
            "--save_folder", "1",
            "--jacobi", "1",
            "--jacobi_th", "0.001",
            "--finetuned_vae", "none",
            "--disable_learnable_denoiser", "0",
            "--jacobi_block_size", "32",
            "--target_length", str(target_length)
        ]
        
        if input_image_path and os.path.exists(input_image_path):
            cmd.extend(["--input_image", input_image_path])
        else:
            cmd.extend(["--input_image", "none"])
        
        result = subprocess.run(cmd, capture_output=True, text=True, cwd=os.getcwd())
        
        if result.returncode != 0:
            return None, f"Error: {result.stderr}"
        
        # Find the generated video
        output_files = list(output_dir.glob("**/*.mp4")) + list(output_dir.glob("**/*.gif"))
        if output_files:
            latest_file = max(output_files, key=lambda p: p.stat().st_mtime)
            return str(latest_file), "Success! Video generated."
        else:
            return None, "Error: Generated video not found."
            
    except Exception as e:
        return None, f"Error: {str(e)}"


# Create Gradio interface
with gr.Blocks(title="STARFlow - Text-to-Image & Video Generation") as demo:
    gr.Markdown("""
    # STARFlow: Scalable Transformer Auto-Regressive Flow
    
    Generate high-quality images and videos from text prompts using STARFlow models.
    
    **Note**: You'll need to upload model checkpoints. Check the README for model download links.
    """)
    
    with gr.Tabs():
        with gr.Tab("Text-to-Image"):
            with gr.Row():
                with gr.Column():
                    image_prompt = gr.Textbox(
                        label="Prompt",
                        placeholder="a film still of a cat playing piano",
                        lines=3
                    )
                    image_checkpoint = gr.File(
                        label="Model Checkpoint (.pth file) - Optional if already uploaded to Space",
                        file_types=[".pth"]
                    )
                    image_config = gr.Textbox(
                        label="Config Path",
                        value="configs/starflow_3B_t2i_256x256.yaml",
                        placeholder="configs/starflow_3B_t2i_256x256.yaml"
                    )
                    image_aspect = gr.Dropdown(
                        choices=["1:1", "2:3", "3:2", "16:9", "9:16", "4:5", "5:4"],
                        value="1:1",
                        label="Aspect Ratio"
                    )
                    image_cfg = gr.Slider(1.0, 10.0, value=3.6, step=0.1, label="CFG Scale")
                    image_seed = gr.Number(value=999, label="Seed", precision=0)
                    image_btn = gr.Button("Generate Image", variant="primary")
                
                with gr.Column():
                    image_output = gr.Image(label="Generated Image")
                    image_status = gr.Textbox(label="Status", interactive=False)
            
            image_btn.click(
                fn=generate_image,
                inputs=[image_prompt, image_aspect, image_cfg, image_seed, image_checkpoint, image_config],
                outputs=[image_output, image_status],
                show_progress=True
            )
        
        with gr.Tab("Text-to-Video"):
            with gr.Row():
                with gr.Column():
                    video_prompt = gr.Textbox(
                        label="Prompt",
                        placeholder="a corgi dog looks at the camera",
                        lines=3
                    )
                    video_checkpoint = gr.File(
                        label="Model Checkpoint (.pth file) - Optional if already uploaded to Space",
                        file_types=[".pth"]
                    )
                    video_config = gr.Textbox(
                        label="Config Path",
                        value="configs/starflow-v_7B_t2v_caus_480p.yaml",
                        placeholder="configs/starflow-v_7B_t2v_caus_480p.yaml"
                    )
                    video_aspect = gr.Dropdown(
                        choices=["16:9", "1:1", "4:3"],
                        value="16:9",
                        label="Aspect Ratio"
                    )
                    video_cfg = gr.Slider(1.0, 10.0, value=3.5, step=0.1, label="CFG Scale")
                    video_seed = gr.Number(value=99, label="Seed", precision=0)
                    video_length = gr.Slider(81, 481, value=81, step=80, label="Target Length (frames)")
                    video_input_image = gr.File(
                        label="Input Image (optional, for image-to-video)",
                        file_types=["image"]
                    )
                    video_btn = gr.Button("Generate Video", variant="primary")
                
                with gr.Column():
                    video_output = gr.Video(label="Generated Video")
                    video_status = gr.Textbox(label="Status", interactive=False)
            
            video_btn.click(
                fn=generate_video,
                inputs=[video_prompt, video_aspect, video_cfg, video_seed, video_length, 
                       video_checkpoint, video_config, video_input_image],
                outputs=[video_output, video_status],
                show_progress=True
            )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)