import gradio as gr
from llm_inference_video import VideoLLMInferenceNode
import random

title = """<h1 align="center">CineGen: AI Video Prompt Architect</h1>
<p align="center">Generate cinematic video prompts with technical specifications</p>"""

def create_video_interface():
    llm_node = VideoLLMInferenceNode()

    with gr.Blocks(theme='bethecloud/storj_theme') as demo:
        gr.HTML(title)
        
        with gr.Row():
            with gr.Column(scale=2):
                input_concept = gr.Textbox(label="Core Concept/Thematic Input", lines=3)
                style = gr.Dropdown(
                    choices=["Cinematic", "Documentary", "Animation", "Action", "Experimental"],
                    value="Cinematic",
                    label="Video Style"
                )

                camera_style = gr.Dropdown(
                    choices=["Steadicam flow", "Drone aerials", "Handheld urgency", "Crane elegance", 
                           "Dolly precision", "VR 360", "Multi-angle rig"],
                    value="Steadicam flow",
                    label="Camera Movement Style"
                )
                
            with gr.Column(scale=2):
                pacing = gr.Dropdown(
                    choices=["Slow burn", "Rhythmic pulse", "Frantic energy", "Ebb and flow", "Hypnotic drift"],
                    value="Rhythmic pulse",
                    label="Pacing Rhythm"
                )
                special_effects = gr.Dropdown(
                    choices=["Practical effects", "CGI enhancement", "Analog glitches", 
                           "Light painting", "Projection mapping", "Nanosecond exposures"],
                    value="Practical effects",
                    label="SFX Approach"
                )
                custom_elements = gr.Textbox(label="Custom Technical Elements", 
                                           placeholder="e.g., Infrared hybrid, Datamosh transitions")
                
        with gr.Row():
            custom_elements = gr.Textbox(label="Custom Elements", scale=3)
            with gr.Column(scale=1):
                provider = gr.Dropdown(
                    choices=["SambaNova", "Groq"],
                    value="SambaNova",
                    label="LLM Provider"
                )
                model = gr.Dropdown(
                    choices=[
                        "Meta-Llama-3.1-70B-Instruct",
                        "Meta-Llama-3.1-405B-Instruct",
                        "Meta-Llama-3.1-8B-Instruct"
                    ],
                    value="Meta-Llama-3.1-70B-Instruct",
                    label="Model"
                )

        generate_btn = gr.Button("Generate Video Prompt", variant="primary")
        output = gr.Textbox(label="Generated Prompt", lines=12, show_copy_button=True)

        def update_models(provider):
            models = {
                "Groq": ["llama-3.3-70b-versatile"],
                "SambaNova": [
                    "Meta-Llama-3.1-70B-Instruct",
                    "Meta-Llama-3.1-405B-Instruct",
                    "Meta-Llama-3.1-8B-Instruct"
                ]
            }
            return gr.Dropdown(choices=models[provider], value=models[provider][0])

        provider.change(update_models, inputs=provider, outputs=model)

        generate_btn.click(
            llm_node.generate_video_prompt,
            inputs=[input_concept, style, camera_style, pacing, special_effects, custom_elements, provider, model],
            outputs=output
        )


    return demo

if __name__ == "__main__":
    demo = create_video_interface()
    demo.launch(share=True)