import subprocess

subprocess.run(
    "pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)
import uuid

import gradio as gr
import spaces
import torch
from qwen_vl_utils import process_vision_info
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from voyager_index import Voyager

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


# Initialize the model and processor
model = (
    Qwen2VLForConditionalGeneration.from_pretrained(
        "Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16
    )
    .to(device)
    .eval()
)

processor = AutoProcessor.from_pretrained(
    "Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True
)


def create_index(session_id):
    return Voyager(embedding_size=1536, override=True, index_name=f"{session_id}")


def add_to_index(files, index):
    index.add_documents([file.name for file in files], batch_size=1)
    return f"Added {len(files)} files to the index."


@spaces.GPU
def generate_answer(query, retrieved_image):
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": retrieved_image,
                },
                {"type": "text", "text": query},
            ],
        }
    ]
    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to(device)
    generated_ids = model.generate(**inputs, max_new_tokens=200)
    generated_ids_trimmed = [
        out_ids[len(in_ids) :]
        for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False,
    )
    return output_text


def query_index(query, index):
    res = index(query, k=1)
    retrieved_image = res["documents"][0][0]["image"]
    output_text = generate_answer(query, retrieved_image)

    return output_text[0], retrieved_image


# Define the Gradio interface
with gr.Blocks() as demo:
    session_id = gr.State(lambda: str(uuid.uuid4()))
    index = gr.State(lambda: create_index(session_id.value))

    gr.Markdown("# Full vision pipeline demo")

    with gr.Tab("Add to Index"):
        file_input = gr.File(file_count="multiple", label="Upload Files")
        add_button = gr.Button("Add to Index")
        add_output = gr.Textbox(label="Result")

        add_button.click(add_to_index, inputs=[file_input, index], outputs=add_output)

    with gr.Tab("Query Index"):
        query_input = gr.Textbox(label="Enter your query")
        query_button = gr.Button("Submit Query")
        with gr.Row():
            query_output = gr.Textbox(label="Answer")
            image_output = gr.Image(label="Retrieved Image")

        query_button.click(
            query_index,
            inputs=[query_input, index],
            outputs=[query_output, image_output],
        )

# Launch the interface
demo.launch()