import gradio as gr
import time
import numpy as np
from PIL import Image
import random

# Mock Pipeline Class to simulate the processing stages described
# In a real scenario, this would interface with Diffusers/PyTorch models
class VideoGenerationPipeline:
    def __init__(self):
        self.device = "cpu"  # Mock device

    def preprocess(self, image):
        """Simulate Stage 1: Upscaling and Preprocessing"""
        time.sleep(1)
        return image

    def generate_sequence(self, image, steps):
        """Simulate Stage 2: Frame Generation"""
        time.sleep(2)
        return [image] * steps

    def render_video(self, frames, fps):
        """Simulate Stage 3: Video Rendering"""
        time.sleep(1)
        # Return a dummy video path or placeholder
        return "output_video.mp4"

# Initialize the mock pipeline
pipeline = VideoGenerationPipeline()

def process_video(
    reference_image, 
    pace_slider, 
    motion_slider, 
    num_frames, 
    guidance_scale,
    progress=gr.Progress()
):
    """
    Main inference function simulating a complex multi-stage pipeline.
    Uses gr.Progress to provide user feedback during long operations.
    """
    if reference_image is None:
        raise gr.Error("Please upload a reference image first.")
    
    try:
        # Stage 1: Preprocessing
        progress(0.1, desc="Stage 1: Preprocessing & Upscaling...")
        _ = pipeline.preprocess(reference_image)
        
        # Stage 2: Sequence Generation
        progress(0.4, desc="Stage 2: Generating Frame Sequence...")
        # Simulate variable processing time based on complexity
        time.sleep(1 + (motion_slider * 0.5))
        
        # Stage 3: Choreography/Motion
        progress(0.7, desc="Stage 3: Applying Motion & Choreography...")
        _ = pipeline.generate_sequence(reference_image, num_frames)
        
        # Stage 4: Final Render
        progress(0.9, desc="Stage 4: Rendering Final Video...")
        output_path = pipeline.render_video([], fps=25)
        
        progress(1.0, desc="Complete!")
        
        # In a real app, return the actual video file path
        # Here we return the input as a placeholder for the demo
        return reference_image

    except Exception as e:
        raise gr.Error(f"Pipeline failed: {str(e)}")

# Gradio 6 Application Structure
# CRITICAL: gr.Blocks() takes NO parameters in Gradio 6
with gr.Blocks() as demo:
    
    # Header with required attribution
    gr.HTML("""
    <div style="text-align: center; margin-bottom: 20px;">
        <h1>Pro-Video Img2Vid Pipeline</h1>
        <p>Advanced Image-to-Video Generation Workflow</p>
        <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color: #007bff; text-decoration: none;">Built with anycoder</a>
    </div>
    """)
    
    # Layout: Sidebar for controls, Main area for IO
    with gr.Row():
        
        # Sidebar for Configuration
        with gr.Sidebar(width=320):
            gr.Markdown("## Configuration")
            
            gr.Markdown("### Generation Settings")
            pace_slider = gr.Slider(
                minimum=0.6, 
                maximum=1.4, 
                value=0.9, 
                step=0.05, 
                label="Generation Pace",
                info="Lower is faster processing"
            )
            
            motion_slider = gr.Slider(
                minimum=0.8, 
                maximum=2.0, 
                value=1.2, 
                step=0.1, 
                label="Motion Intensity",
                info="Controls movement magnitude"
            )
            
            num_frames = gr.Slider(
                minimum=16,
                maximum=64,
                value=24,
                step=4,
                label="Frame Count",
                info="Total frames in output"
            )
            
            guidance_scale = gr.Slider(
                minimum=5.0,
                maximum=15.0,
                value=7.5,
                step=0.5,
                label="Guidance Scale",
                info="Adherence to prompt"
            )
            
            gr.Markdown("---")
            gr.Markdown("### System Info")
            system_status = gr.Textbox(
                value="System Ready (CUDA: Available)", 
                label="Status", 
                interactive=False
            )
        
        # Main Content Area
        with gr.Column(scale=1):
            
            gr.Markdown("## Input / Output")
            
            with gr.Row():
                with gr.Column():
                    input_image = gr.Image(
                        type="pil", 
                        label="Reference Image",
                        sources=["upload", "clipboard"],
                        height=400
                    )
                    
                    # Action Buttons
                    with gr.Row():
                        generate_btn = gr.Button("Generate Video", variant="primary", size="lg")
                        clear_btn = gr.ClearButton([input_image], variant="stop")
                
                with gr.Column():
                    output_video = gr.Video(
                        label="Generated Video Output (25fps)", 
                        autoplay=True,
                        height=400
                    )
            
            # Advanced Settings Accordion
            with gr.Accordion("Advanced Settings", open=False):
                seed = gr.Number(label="Seed (Random for -1)", value=-1, precision=0)
                negative_prompt = gr.Textbox(
                    label="Negative Prompt", 
                    placeholder="blur, distortion, low quality...",
                    lines=2
                )
                enable_lora = gr.Checkbox(label="Enable Custom LoRA", value=False)

    # Event Listeners
    # Gradio 6 uses api_visibility instead of just api_name
    generate_btn.click(
        fn=process_video,
        inputs=[
            input_image, 
            pace_slider, 
            motion_slider, 
            num_frames,
            guidance_scale
        ],
        outputs=output_video,
        api_visibility="public"
    )

# Gradio 6 Launch Method
# CRITICAL: All parameters (theme, css, etc.) go here, NOT in gr.Blocks()
demo.launch(
    theme=gr.themes.Soft(
        primary_hue="indigo",
        secondary_hue="blue",
        neutral_hue="slate",
        font=gr.themes.GoogleFont("Inter"),
        text_size="lg",
        spacing_size="lg",
        radius_size="md"
    ),
    # Custom CSS for additional polish
    css="""
    .gradio-container {
        max-width: 1400px !important;
    }
    h1 {
        font-weight: 700 !important;
        color: #1f2937 !important;
    }
    """,
    footer_links=[
        {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}
    ]
)