import gradio as gr
from transformers import LlavaOnevisionProcessor, LlavaOnevisionForConditionalGeneration, TextIteratorStreamer
from threading import Thread
import re
import time 
from PIL import Image
import torch
import cv2
import spaces

model_id = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"

processor = LlavaOnevisionProcessor.from_pretrained(model_id)

model = LlavaOnevisionForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16)
model.to("cuda")

def sample_frames(video_file, num_frames):
    video = cv2.VideoCapture(video_file)
    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
    interval = total_frames // num_frames
    frames = []
    for i in range(total_frames):
        ret, frame = video.read()
        pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        if not ret:
            continue
        if i % interval == 0:
            frames.append(pil_img)
    video.release()
    return frames

def bot_streaming(message, history):

  txt = message["text"]
  ext_buffer = f"USER: {txt} ASSISTANT: "

  if message["files"]:
    if len(message["files"]) == 1:
      image = [message.files[0].path]
    # interleaved images or video
    elif len(message["files"]) > 1:
      image = [msg["path"] for msg in message["files"]]
  else:
      
    def has_file_data(lst):
      return any(isinstance(item, FileData) for sublist in lst if isinstance(sublist, tuple) for item in sublist)

    def extract_paths(lst):
        return [item["path"] for sublist in lst if isinstance(sublist, tuple) for item in sublist if isinstance(item, FileData)]

    latest_text_only_index = -1

    for i, item in enumerate(history):
        if all(isinstance(sub_item, str) for sub_item in item):
            latest_text_only_index = i

    image = [path for i, item in enumerate(history) if i < latest_text_only_index and has_file_data(item) for path in extract_paths(item)]

  if message["files"] is None:
      gr.Error("You need to upload an image or video for LLaVA to work.")
      
  video_extensions = ("avi", "mp4", "mov", "mkv", "flv", "wmv", "mjpeg")
  image_extensions = Image.registered_extensions()
  image_extensions = tuple([ex for ex, f in image_extensions.items()])
  image_list = []
  video_list = []

  print("media", image)
  if len(image) == 1:
    if image[0].endswith(video_extensions):
        
        video_list = sample_frames(image[0], 12)
        
        prompt = f"USER: <video> {message.text} ASSISTANT:"
    elif image[0].endswith(image_extensions):
        image_list.append(Image.open(image[0]).convert("RGB"))
        msg = message["text"]
        prompt =  f"USER: <image> {message.text} ASSISTANT:"

  elif len(image) > 1:
    user_prompt = message["text"]

    for img in image:
      if img.endswith(image_extensions):
        img = Image.open(img).convert("RGB")
        image_list.append(img)

      elif img.endswith(video_extensions):
        video_list.append(sample_frames(img, 7))
        #for frame in sample_frames(img, 6):
          #video_list.append(frame)
        
    image_tokens = ""
    video_tokens = ""

    if image_list != []:
      image_tokens = "<image>" * len(image_list)
    if video_list != []:   
      
      toks = len(video_list) 
      video_tokens = "<video>" * toks
      
    

    prompt = f"USER: {image_tokens}{video_tokens} {user_prompt} ASSISTANT:"

  if image_list != [] and video_list != []:
    inputs = processor(text=prompt, images=image_list, videos=video_list, padding=True, return_tensors="pt").to("cuda",torch.float16)
  elif image_list != [] and video_list == []:
    inputs = processor(text=prompt, images=image_list, padding=True, return_tensors="pt").to("cuda", torch.float16)
  elif image_list == [] and video_list != []:
    inputs = processor(text=prompt, videos=video_list, padding=True, return_tensors="pt").to("cuda", torch.float16)
  
  
  streamer = TextIteratorStreamer(processor, **{"max_new_tokens": 200, "skip_special_tokens": True, "clean_up_tokenization_spaces":True})
  generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=200)
  generated_text = ""

  thread = Thread(target=model.generate, kwargs=generation_kwargs)
  thread.start()

  

  buffer = ""
  for new_text in streamer:
    
    buffer += new_text
    print("new_text", new_text)
    #generated_text_without_prompt = buffer[len(ext_buffer):][:-1]
    time.sleep(0.01)
    yield buffer #generated_text_without_prompt


demo = gr.ChatInterface(fn=bot_streaming, title="LLaVA Onevision", examples=[
     {"text": "Do the cats in these two videos have same breed? What breed is each cat?", "files":["./cats_1.mp4", "./cats_2.mp4"]},
    {"text": "These are the tech specs of two laptops I am choosing from. Which one should I choose for office work?", "files":["./dell-tech-specs.jpeg", "./asus-tech-specs.png"]},
     {"text": "Here are several images from a cooking book, showing how to prepare a meal step by step. Can you write a recipe for the meal, describing each step in details?", "files":["./step0.png", "./step1.png", "./step2.png", "./step3.png", "./step4.png", "./step5.png"]}, 

    {"text": "What is on the flower?", "files":["./bee.jpg"]},
    {"text": "This is a video explaining how to create a Presentation in GoogleSlides. Can you write down what I should do step by step, following the video?", "files":["./tutorial.mp4"]}], 
      textbox=gr.MultimodalTextbox(file_count="multiple"), 
      description="Try [LLaVA Onevision](https://huggingface.co/docs/transformers/main/en/model_doc/llava_onevision) in this demo (more specifically, the [Qwen-2-0.5B-Instruct variant](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-ov-hf)). Upload an image or a video, and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error. ",
      stop_btn="Stop Generation", multimodal=True)
demo.launch(debug=True)