import gradio as gr
import subprocess
from deep_translator import GoogleTranslator
import torch
from llava.model.builder import load_pretrained_model
from llava.mm_utils import tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from llava.conversation import conv_templates
from decord import VideoReader, cpu
import numpy as np
import copy

# Gerekli kütüphanelerin kurulumu
subprocess.run(
    "pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)
subprocess.run("pip install deep_translator", shell=True)

# Çevirmen nesnesi oluştur
translator = GoogleTranslator(source='tr', target='en')
translator_reverse = GoogleTranslator(source='en', target='tr')

title = "# 🙋🏻‍♂️🌟Tonic'in 🌋📹LLaVA-Video'suna Hoş Geldiniz!"
description1 = """**🌋📹LLaVA-Video-7B-Qwen2**, ...
"""
description2 = """
...
"""

join_us = """
## Bize Katılın:
...
"""

def load_video(video_path, max_frames_num, fps=1, force_sample=False):
    if max_frames_num == 0:
        return np.zeros((1, 336, 336, 3))
    
    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
    total_frame_num = len(vr)
    fps = round(vr.get_avg_fps()/fps)
    frame_idx = [i for i in range(0, len(vr), fps)]
    frame_time = [i/vr.get_avg_fps() for i in frame_idx]
    
    if len(frame_idx) > max_frames_num or force_sample:
        sample_fps = max_frames_num
        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int)
        frame_idx = uniform_sampled_frames.tolist()
        frame_time = [i/vr.get_avg_fps() for i in frame_idx]

    frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
    spare_frames = vr.get_batch(frame_idx).asnumpy()
    
    return spare_frames, frame_time, total_frame_num / vr.get_avg_fps()

# Model yükleme
pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2"
model_name = "llava_qwen"
device = "cuda" if torch.cuda.is_available() else "cpu"
device_map = "auto"

print("Model yükleniyor...")
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map)
model.eval()
print("Model başarıyla yüklendi!")

def process_video(video_path, question):
    try:
        max_frames_num = 64
        video, frame_time, video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
        video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].to(device).bfloat16()
        video = [video]

        conv_template = "qwen_1_5"
        time_instruction = f"Video {video_time:.2f} saniye sürmektedir ve {len(video[0])} kare uniform olarak örneklenmiştir. Bu kareler {frame_time} konumlarında bulunmaktadır. Lütfen bu videoyla ilgili aşağıdaki soruları cevaplayın."
        
        # Soruyu İngilizce'ye çevir
        question_en = translator.translate(question)
        full_question = DEFAULT_IMAGE_TOKEN + f"{time_instruction}\n{question_en}"
        
        conv = copy.deepcopy(conv_templates[conv_template])
        conv.append_message(conv.roles[0], full_question)
        conv.append_message(conv.roles[1], None)
        prompt_question = conv.get_prompt()
        
        input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").to(device)

        with torch.no_grad():
            output = model.generate(
                input_ids,
                images=video,
                modalities=["video"],
                do_sample=False,
                temperature=0,
                max_new_tokens=4096,
            )
        
        response = tokenizer.batch_decode(output, skip_special_tokens=True)[0].strip()
        
        # Cevabı Türkçe'ye çevir
        response_tr = translator_reverse.translate(response)
        return response_tr
    except Exception as e:
        return f"Bir hata oluştu: {str(e)}"

def gradio_interface(video_file, question):
    if video_file is None:
        return "Lütfen bir video dosyası yükleyin."
    response = process_video(video_file, question)
    return response

with gr.Blocks() as demo:
    gr.Markdown(title)
    with gr.Row():
        with gr.Group():
            gr.Markdown(description1)
        with gr.Group():
            gr.Markdown(description2)
    with gr.Accordion("Bize Katılın", open=False):
        gr.Markdown(join_us)
    with gr.Row():
        with gr.Column():
            video_input = gr.Video()
            question_input = gr.Textbox(label="🙋🏻‍♂️Kullanıcı Sorusu", placeholder="Video hakkında bir soru sorun...")
            submit_button = gr.Button("🌋📹LLaVA-Video'ya Sor")
        output = gr.Textbox(label="🌋📹LLaVA-Video")
    
    submit_button.click(
        fn=gradio_interface,
        inputs=[video_input, question_input],
        outputs=output
    )

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