import gradio as gr
from inference import inference_and_run
import spaces
import os
import shutil
from PIL import Image
from gradio_image_prompter import ImagePrompter

model_name = 'Ferret-UI'
cur_dir = os.path.dirname(os.path.abspath(__file__))

@spaces.GPU()
def inference_with_gradio(chatbot, image_data, prompt, model_path, temperature=0.2, top_p=0.7, max_new_tokens=512):
    if image_data is None:
        raise gr.Error("Please upload an image and draw a bounding box if needed.")
    
    # Handle the image and bounding box data
    image = image_data["image"]
    box = None
    if "points" in image_data and image_data["points"] and len(image_data["points"]) > 0:
        points = image_data["points"][0]
        # Convert points to [x1, y1, x2, y2] format
        box = f"{points[0]}, {points[1]}, {points[3]}, {points[4]}"
    
    # Convert numpy array to a PIL Image
    pil_image = Image.fromarray(image)
    
    # Save the image
    filename = "temp_image.png"
    dir_path = "./"
    image_path = os.path.join(dir_path, filename)
    pil_image.save(image_path)  # Save the PIL image to the file system
    
    if "gemma" in model_path.lower():
        conv_mode = "ferret_gemma_instruct"
    else:
        conv_mode = "ferret_llama_3"
    
    print("the box: ", box)
    # Call the main inference function with the model and mask (if applicable)
    inference_text = inference_and_run(
        image_path=filename,
        image_dir=dir_path,
        prompt=prompt,
        model_path=model_path,
        conv_mode=conv_mode,
        temperature=temperature, 
        top_p=top_p,
        box=box,
        max_new_tokens=max_new_tokens,
    )
    
    if isinstance(inference_text, (list, tuple)):
        inference_text = str(inference_text[0])
    
    # Update chatbot history
    new_history = chatbot.copy() if chatbot else []
    new_history.append((prompt, inference_text))
    return new_history

def submit_chat(chatbot, text_input):
    return chatbot, ''

def clear_chat():
    return [], None, "", 0.2, 0.7, 512

html = f"""
<div style="text-align: center; padding: 20px;">
    <div style="display: inline-block; background-color: #f5f5f7; padding: 20px; border-radius: 20px; box-shadow: 0px 6px 20px rgba(0, 0, 0, 0.1);">
        <div style="display: flex; align-items: center;">
            <img src='https://github.com/apple/ml-ferret/blob/main/ferretui/figs/ferretui_icon.png?raw=true' alt='Ferret-UI' 
                style='width: 80px; height: 80px; border-radius: 20px; box-shadow: 0px 8px 16px rgba(0, 0, 0, 0.2);'/>
            <div style="margin-left: 15px;">
                <h1 style="font-size: 2.8em; font-family: -apple-system, BlinkMacSystemFont, sans-serif; color: #1D1D1F; 
                font-weight: bold; margin-bottom: 0;"> {model_name}</h1>
                <p style="font-size: 1.2em; color: #6e6e73; font-family: -apple-system, BlinkMacSystemFont, sans-serif; margin-top: 5px;">
                    📱 Grounded Mobile UI Understanding with Multimodal LLMs.<br>
                    A new MLLM tailored for enhanced understanding of mobile UI screens, equipped with referring, grounding, and reasoning capabilities.
                </p>
                <a href='https://huggingface.co./jadechoghari/Ferret-UI-Gemma2b' style='text-decoration: none;'>
                    <button style="background-color: #007aff; color: white; font-size: 1.2em; padding: 10px 20px; border-radius: 10px; border: none; margin-top: 10px; box-shadow: 0px 4px 12px rgba(0, 122, 255, 0.4); cursor: pointer;">
                        🤗 Try on Hugging Face
                    </button>
                </a>
            </div>
        </div>
    </div>
    <p style="font-size: 1.2em; color: #86868B; font-family: -apple-system, BlinkMacSystemFont, sans-serif; margin-top: 30px;">
        We release two Ferret-UI checkpoints, built on gemma-2b and Llama-3-8B models respectively, for public exploration. 🚀
    </p>
</div>
"""

latex_delimiters_set = [{
    "left": "\\(",
    "right": "\\)",
    "display": False 
}, {
    "left": "\\begin{equation}",
    "right": "\\end{equation}",
    "display": True 
}, {
    "left": "\\begin{align}",
    "right": "\\end{align}",
    "display": True
}]

with gr.Blocks(title=model_name) as demo:
    gr.HTML(html)
    with gr.Row():
        with gr.Column(scale=3):
            # Replace image_input with ImagePrompter
            image_input = ImagePrompter(label="Upload Image & Draw Bounding Box")
            text_input = gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt")
            model_dropdown = gr.Dropdown(
                choices=[
                    "jadechoghari/Ferret-UI-Gemma2b",
                    "jadechoghari/Ferret-UI-Llama8b",
                ],
                label="Model Path",
                value="jadechoghari/Ferret-UI-Gemma2b"
            )
            temperature_input = gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=0.2, label="Temperature")
            top_p_input = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.7, label="Top P")
            max_new_tokens_input = gr.Slider(minimum=1, maximum=1024, step=1, value=512, label="Max New Tokens")

            gr.Examples(
                examples=[
                    [{"image": "appstore_reminders.png"}, "Describe the contents inside the box"],
                    [{"image": "appstore_reminders.png"}, "What is the text shown inside the highlighted area"]
                ],
                inputs=[image_input, text_input],
                label="Try these examples"
            )
        
        with gr.Column(scale=7):
            chatbot = gr.Chatbot(
                label="Chat with Ferret-UI",
                height=400,
                show_copy_button=True,
                latex_delimiters=latex_delimiters_set,
                type="tuples"
            )
            with gr.Row():
                send_btn = gr.Button("Send", variant="primary")
                clear_btn = gr.Button("Clear", variant="secondary")

    send_click_event = send_btn.click(
        inference_with_gradio,
        [chatbot, image_input, text_input, model_dropdown, temperature_input, top_p_input, max_new_tokens_input],
        chatbot
    ).then(
        submit_chat,
        [chatbot, text_input],
        [chatbot, text_input]
    )
    
    submit_event = text_input.submit(
        inference_with_gradio,
        [chatbot, image_input, text_input, model_dropdown, temperature_input, top_p_input, max_new_tokens_input],
        chatbot
    ).then(
        submit_chat,
        [chatbot, text_input],
        [chatbot, text_input]
    )
    
    clear_btn.click(
        clear_chat,
        outputs=[chatbot, image_input, text_input, temperature_input, top_p_input, max_new_tokens_input]
    )

demo.launch()