# Imports
import gradio as gr
import spaces
import torch

from PIL import Image
from decord import VideoReader, cpu
from transformers import AutoModel, AutoTokenizer

# Pre-Initialize
DEVICE = "auto"
if DEVICE == "auto":
    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"[SYSTEM] | Using {DEVICE} type compute device.")

# Variables
DEFAULT_INPUT = "Describe in one paragraph."
MAX_FRAMES = 64

repo_name = "openbmb/MiniCPM-V-2_6-int4" # "openbmb/MiniCPM-V-2_6"
repo = AutoModel.from_pretrained(repo_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)

css = '''
.gradio-container{max-width: 560px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

# Functions
def encode_video(video_path):
    def uniform_sample(l, n):
        gap = len(l) / n
        idxs = [int(i * gap + gap / 2) for i in range(n)]
        return [l[i] for i in idxs]

    vr = VideoReader(video_path, ctx=cpu(0))
    sample_fps = round(vr.get_avg_fps() / 1)
    frame_idx = [i for i in range(0, len(vr), sample_fps)]
    
    if len(frame_idx) > MAX_FRAMES:
        frame_idx = uniform_sample(frame_idx, MAX_FRAMES)
        
    frames = vr.get_batch(frame_idx).asnumpy()
    frames = [Image.fromarray(v.astype('uint8')) for v in frames]
    return frames
    
@spaces.GPU(duration=60)
def generate(image, video, instruction=DEFAULT_INPUT, sampling=False, temperature=0.7, top_p=0.8, top_k=100, repetition_penalty=1.05, max_tokens=512):
    # repo.to(DEVICE)

    print(image)
    print(video)
    print(instruction)
    
    if not video:
        image_data = Image.fromarray(image.astype('uint8'), 'RGB')
        inputs = [{"role": "user", "content": [image_data, instruction]}]
    else:
        video_data = encode_video(video)
        inputs = [{"role": "user", "content": video_data + [instruction]}]
    
    parameters = {
        "sampling": sampling,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "repetition_penalty": repetition_penalty,
        "max_new_tokens": max_tokens,
        "use_image_id": False,
        "max_slice_nums": 2,
    }
    
    output = repo.chat(image=None, msgs=inputs, tokenizer=tokenizer, **parameters)
    
    print(output)
    
    return output
    
def cloud():
    print("[CLOUD] | Space maintained.")

# Initialize
with gr.Blocks(css=css) as main:
    with gr.Column():
        gr.Markdown("🪄 Analyze images and caption them using state-of-the-art openbmb/MiniCPM-V-2_6.")
        
    with gr.Column():
        input = gr.Image(label="Image")
        input_2 = gr.Video(label="Video")
        instruction = gr.Textbox(lines=1, value=DEFAULT_INPUT, label="Instruction")
        sampling = gr.Checkbox(value=False, label="Sampling")
        temperature = gr.Slider(minimum=0.01, maximum=1.99, step=0.01, value=0.7, label="Temperature")
        top_p = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.8, label="Top P")
        top_k = gr.Slider(minimum=0, maximum=1000, step=1, value=100, label="Top K")
        repetition_penalty = gr.Slider(minimum=0.01, maximum=1.99, step=0.01, value=1.05, label="Repetition Penalty")
        max_tokens = gr.Slider(minimum=1, maximum=4096, step=1, value=512, label="Max Tokens")
        submit = gr.Button("▶")
        maintain = gr.Button("☁️")
        
    with gr.Column():
        output = gr.Textbox(lines=1, value="", label="Output")

    submit.click(fn=generate, inputs=[input, input_2, instruction, sampling, temperature, top_p, top_k, repetition_penalty, max_tokens], outputs=[output], queue=False)
    maintain.click(cloud, inputs=[], outputs=[], queue=False)

main.launch(show_api=True)