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")

# Function to capture frames from the camera
def capture_camera_frames(num_frames):
    camera = cv2.VideoCapture(0)  # Accessing the camera (0 is the default camera)
    frames = []
    for _ in range(num_frames):
        ret, frame = camera.read()
        if not ret:
            break
        pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        frames.append(pil_img)
    camera.release()
    return frames

@spaces.GPU
def bot_streaming(message, history):
    txt = message.text
    ext_buffer = f"user\n{txt} assistant"
    
    if message.files:
        if len(message.files) == 1:
            image = [message.files[0].path]
        elif len(message.files) > 1:
            image = [msg.path for msg in message.files]
    else:
        image = None

    # Check if we should use the camera
    if txt.lower().startswith("camera"):
        # Capture frames from the camera
        image = capture_camera_frames(5)  # Capture 5 frames

    if message.files is None and not image:
        gr.Error("You need to upload an image or video, or access the camera for LLaVA to work.")
        return

    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 len(image) == 1:
        if image[0].endswith(video_extensions):
            video = sample_frames(image[0], 32)
            image = None
            prompt = f"<|im_start|>user <video>\n{message.text}<|im_end|><|im_start|>assistant"
        elif image[0].endswith(image_extensions):
            image = Image.open(image[0]).convert("RGB")
            video = None
            prompt = f"<|im_start|>user <image>\n{message.text}<|im_end|><|im_start|>assistant"
    elif len(image) > 1:
        image_list = []
        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):        
                frames = sample_frames(img, 6)
                for frame in frames:
                    image_list.append(frame)
        
        toks = "<image>" * len(image_list)
        prompt = "<|im_start|>user" + toks + f"\n{user_prompt}<|im_end|><|im_start|>assistant"

        image = image_list
        video = None

    inputs = processor(text=prompt, images=image, videos=video, return_tensors="pt").to("cuda", torch.float16)
    streamer = TextIteratorStreamer(processor, **{"max_new_tokens": 200, "skip_special_tokens": 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
        generated_text_without_prompt = buffer[len(ext_buffer):]
        time.sleep(0.01)
        yield generated_text_without_prompt


# Integrate camera access into Gradio demo
demo = gr.ChatInterface(fn=bot_streaming, title="LLaVA Onevision with Camera", examples=[
    {"text": "Take a picture with the camera and describe what is in it.", "files":[]},
    {"text": "Do the cats in these two videos have the same breed? What breed is each cat?", "files":["./cats_1.mp4", "./cats_2.mp4"]},
    {"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?", "files":["./step0.png", "./step1.png", "./step2.png", "./step3.png"]}, 
], 
    textbox=gr.MultimodalTextbox(file_count="multiple"), 
    description="Upload an image or video, or try capturing frames with the camera and chat about it.",
    stop_btn="Stop Generation", multimodal=True)

demo.launch(debug=True)