import os

import gradio as gr
import numpy as np
import torch
from lavis.models import load_model_and_preprocess
from PIL import Image

device = torch.device("cuda") if torch.cuda.is_available() else "cpu"

model, vis_processors, _ = load_model_and_preprocess(
    name="blip2_opt", model_type="pretrain_opt2.7b", is_eval=True, device=device
)


def generate_caption(image, caption_type):
    image = vis_processors["eval"](image).unsqueeze(0).to(device)

    if caption_type == "Beam Search":
        caption = model.generate({"image": image})
    else:
        caption = model.generate(
            {"image": image}, use_nucleus_sampling=True, num_captions=3
        )

    caption = "\n".join(caption)

    return caption


def chat(input_image, question, history):
    history = history or []
    question = question.lower()

    image = vis_processors["eval"](input_image).unsqueeze(0).to(device)

    clean = lambda x: x.replace("<p>", "").replace("</p>", "").replace("\n", "")
    clean_h = lambda x: (clean(x[0]), clean(x[1]))
    context = list(map(clean_h, history))
    template = "Question: {} Answer: {}."
    prompt = (
        " ".join(
            [template.format(context[i][0], context[i][1]) for i in range(len(context))]
        )
        + " Question: "
        + question
        + " Answer:"
    )

    response = model.generate({"image": image, "prompt": prompt})
    history.append((question, response[0]))

    return history, history


def clear_chat(history):
    return [], []


with gr.Blocks() as demo:
    gr.Markdown("# BLIP-2")
    gr.Markdown(
        "## Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models"
    )
    gr.Markdown(
        "This demo uses `OPT2.7B` weights. For more information please see [Github](https://github.com/salesforce/LAVIS/tree/main/projects/blip2) or [Paper](https://arxiv.org/abs/2301.12597)."
    )

    with gr.Row():
        with gr.Column():
            input_image = gr.Image(label="Image", type="pil")
            caption_type = gr.Radio(
                ["Beam Search", "Nucleus Sampling"],
                label="Caption Type",
                value="Beam Search",
            )
            btn_caption = gr.Button("Generate Caption")
            output_text = gr.Textbox(label="Answer", lines=5)

        with gr.Column():
            chatbot = gr.Chatbot().style(color_map=("green", "pink"))
            chat_state = gr.State()

            question_txt = gr.Textbox(label="Question", lines=1)
            btn_answer = gr.Button("Generate Answer")
            btn_clear = gr.Button("Clear Chat")

    btn_caption.click(
        generate_caption, inputs=[input_image, caption_type], outputs=[output_text]
    )

    btn_answer.click(
        chat,
        inputs=[input_image, question_txt, chat_state],
        outputs=[chatbot, chat_state],
    )

    btn_clear.click(clear_chat, inputs=[chat_state], outputs=[chatbot, chat_state])

    gr.Examples(
        [["./merlion.png", "Beam Search", "which city is this?", None, None]],
        inputs=[input_image, caption_type, question_txt, chat_state, chatbot],
    )

demo.launch()