import gradio as gr
import torch
import random
from diffusers import DiffusionPipeline
from transformers import pipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
MAX_SEED = 2**32 - 1

# --- Model lists ordered by size (light to heavy) ---
image_models = {
    "Stable Diffusion 1.5 (light)": "runwayml/stable-diffusion-v1-5",
    "Stable Diffusion 2.1": "stabilityai/stable-diffusion-2-1",
    "Dreamlike 2.0": "dreamlike-art/dreamlike-photoreal-2.0",
    "Playground v2": "playgroundai/playground-v2-1024px-aesthetic",
    "Muse 512": "amused/muse-512-finetuned",
    "PixArt": "PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
    "Kandinsky 3": "kandinsky-community/kandinsky-3",
    "BLIP Diffusion": "Salesforce/blipdiffusion",
    "SDXL Base 1.0 (heavy)": "stabilityai/stable-diffusion-xl-base-1.0",
    "OpenJourney (heavy)": "prompthero/openjourney"
}

text_models = {
    "GPT-2 (light)": "gpt2",
    "GPT-Neo 1.3B": "EleutherAI/gpt-neo-1.3B",
    "BLOOM 1.1B": "bigscience/bloom-1b1",
    "GPT-J 6B": "EleutherAI/gpt-j-6B",
    "Falcon 7B": "tiiuae/falcon-7b",
    "XGen 7B": "Salesforce/xgen-7b-8k-base",
    "BTLM 3B": "cerebras/btlm-3b-8k-base",
    "MPT 7B": "mosaicml/mpt-7b",
    "StableLM 2": "stabilityai/stablelm-2-1_6b",
    "LLaMA 2 7B (heavy)": "meta-llama/Llama-2-7b-hf"
}

# Cache
image_pipes = {}
text_pipes = {}

def generate_image(prompt, model_name, seed, randomize_seed, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.manual_seed(seed)

    progress(0, desc="Loading model...")
    if model_name not in image_pipes:
        image_pipes[model_name] = DiffusionPipeline.from_pretrained(
            image_models[model_name],
            torch_dtype=torch_dtype
        ).to(device)
    pipe = image_pipes[model_name]

    progress(25, desc="Running inference (step 1/3)...")
    result = pipe(prompt=prompt, generator=generator, num_inference_steps=30, width=512, height=512)

    progress(100, desc="Done.")
    return result.images[0], seed

def generate_text(prompt, model_name, progress=gr.Progress(track_tqdm=True)):
    progress(0, desc="Loading model...")
    if model_name not in text_pipes:
        text_pipes[model_name] = pipeline("text-generation", model=text_models[model_name], device=0 if device == "cuda" else -1)
    pipe = text_pipes[model_name]

    progress(50, desc="Generating text...")
    result = pipe(prompt, max_length=100, do_sample=True)[0]['generated_text']
    progress(100, desc="Done.")
    return result

# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 Multi-Model AI Playground with Progress")

    with gr.Tabs():
        # 🖼️ Image Gen Tab
        with gr.Tab("🖼️ Image Generation"):
            img_prompt = gr.Textbox(label="Prompt")
            img_model = gr.Dropdown(choices=list(image_models.keys()), value="Stable Diffusion 1.5 (light)", label="Image Model")
            img_seed = gr.Slider(0, MAX_SEED, value=42, label="Seed")
            img_rand = gr.Checkbox(label="Randomize seed", value=True)
            img_btn = gr.Button("Generate Image")
            img_out = gr.Image()
            img_btn.click(fn=generate_image, inputs=[img_prompt, img_model, img_seed, img_rand], outputs=[img_out, img_seed])

        # 📝 Text Gen Tab
        with gr.Tab("📝 Text Generation"):
            txt_prompt = gr.Textbox(label="Prompt")
            txt_model = gr.Dropdown(choices=list(text_models.keys()), value="GPT-2 (light)", label="Text Model")
            txt_btn = gr.Button("Generate Text")
            txt_out = gr.Textbox(label="Output Text")
            txt_btn.click(fn=generate_text, inputs=[txt_prompt, txt_model], outputs=txt_out)

        # 🎥 Video Gen Tab (placeholder)
        with gr.Tab("🎥 Video Generation (Placeholder)"):
            gr.Markdown("⚠️ Video generation is placeholder only. Models require special setup.")
            vid_prompt = gr.Textbox(label="Prompt")
            vid_btn = gr.Button("Pretend to Generate")
            vid_out = gr.Textbox(label="Result")
            vid_btn.click(lambda x: f"Fake video output for: {x}", inputs=[vid_prompt], outputs=[vid_out])

demo.launch(show_error=True)