import gradio as gr
from transformers import LlavaProcessor, LlavaForConditionalGeneration, TextIteratorStreamer
from threading import Thread
import re
import time 
from PIL import Image
import torch
import cv2
import spaces 
model_id = "llava-hf/llava-interleave-qwen-7b-hf"

processor = LlavaProcessor.from_pretrained(model_id)

model = LlavaForConditionalGeneration.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

@spaces.GPU
def bot_streaming(message, history):
  if message["files"]:
    image = message["files"][-1]
    
  else:
    # if there's no image uploaded for this turn, look for images in the past turns
    # kept inside tuples, take the last one
    for hist in history:
      if type(hist[0])==tuple:
        image = hist[0][0]

  txt = message["text"]
  img = message["files"]
  ext_buffer =f"'user\ntext': '{txt}', 'files': '{img}' assistant"

  if image 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()])
    
  if image.endswith(video_extensions):
      image = sample_frames(image, 12)
      image_tokens = "<image>" * 13
      prompt = f"<|im_start|>user {image_tokens}\n{message}<|im_end|><|im_start|>assistant"
      
  elif image.endswith(image_extensions):
      image = Image.open(image).convert("RGB")
      prompt = f"<|im_start|>user <image>\n{message}<|im_end|><|im_start|>assistant"

  inputs = processor(prompt, image, return_tensors="pt").to("cuda", torch.float16)
  streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
  generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=100)
  generated_text = ""

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

  

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


demo = gr.ChatInterface(fn=bot_streaming, title="LLaVA Interleave", examples=[{"text": "What is on the flower?", "files":["./bee.jpg"]},
                                                                      {"text": "How to make this pastry?", "files":["./baklava.png"]},
                                                                      {"text": "What type of cats are these?", "files":["./cats.mp4"]}], 
                        description="Try [LLaVA Interleave](https://huggingface.co/docs/transformers/main/en/model_doc/llava) in this demo (more specifically, the [Qwen-1.5-7B variant](https://huggingface.co/llava-hf/llava-interleave-qwen-7b-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)