import gradio as gr
import PIL.Image
import transformers
from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
import torch
import os
import string
import functools
import re
import numpy as np
import spaces
from PIL import Image, ImageDraw

model_id = "mattraj/curacel-autodamage-1"
COLORS = ['#4285f4', '#db4437', '#f4b400', '#0f9d58', '#e48ef1']
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).eval().to(device)
processor = PaliGemmaProcessor.from_pretrained(model_id)

###### Transformers Inference
@spaces.GPU
def infer(
        image: PIL.Image.Image,
        text: str,
        max_new_tokens: int = 2048
) -> tuple:
    inputs = processor(text=text, images=image, return_tensors="pt", padding="longest", do_convert_rgb=True).to(device).to(dtype=model.dtype)
    with torch.no_grad():
        generated_ids = model.generate(
            **inputs,
            max_length=max_new_tokens
        )
    result = processor.decode(generated_ids[0], skip_special_tokens=True)

    # Placeholder to extract bounding box info from the result (you should replace this with actual bounding box extraction)
    bounding_boxes = extract_bounding_boxes(result)
    
    # Draw bounding boxes on the image
    annotated_image = image.copy()
    draw = ImageDraw.Draw(annotated_image)
    
    # Example of drawing bounding boxes (replace with actual coordinates)
    for idx, (box, label) in enumerate(bounding_boxes):
        color = COLORS[idx % len(COLORS)]
        draw.rectangle(box, outline=color, width=3)
        draw.text((box[0], box[1]), label, fill=color)
    
    return result, annotated_image

def extract_bounding_boxes(result):
    """
    Extract bounding boxes and labels from the model result.
    Placeholder logic - replace this with actual parsing logic from model output.
    
    Example return: [((x1, y1, x2, y2), "Label")]
    """
    # Example static bounding box and label
    return [((50, 50, 200, 200), "Damage"), ((300, 300, 400, 400), "Dent")]

######## Demo

INTRO_TEXT = """## Curacel Auto Damage demo\n\n
Finetuned from: google/paligemma-3b-pt-448
"""

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(INTRO_TEXT)
    with gr.Tab("Text Generation"):
        with gr.Column():
            image = gr.Image(type="pil")
            text_input = gr.Text(label="Input Text")

            text_output = gr.Text(label="Text Output")
            output_image = gr.Image(label="Annotated Image")
            chat_btn = gr.Button()

        chat_inputs = [image, text_input]
        chat_outputs = [text_output, output_image]

        chat_btn.click(
            fn=infer,
            inputs=chat_inputs,
            outputs=chat_outputs,
        )

        examples = [["./car-1.png", "detect Front-Windscreen-Damage ; Headlight-Damage ; Major-Rear-Bumper-Dent ; Rear-windscreen-Damage ; RunningBoard-Dent ; Sidemirror-Damage ; Signlight-Damage ; Taillight-Damage ; bonnet-dent ; doorouter-dent ; doorouter-scratch ; fender-dent ; front-bumper-dent ; front-bumper-scratch ; medium-Bodypanel-Dent ; paint-chip ; paint-trace ; pillar-dent ; quaterpanel-dent ; rear-bumper-dent ; rear-bumper-scratch ; roof-dent"]]
        gr.Markdown("")

        gr.Examples(
            examples=examples,
            inputs=chat_inputs,
        )

#########

if __name__ == "__main__":
    demo.queue(max_size=10).launch(debug=True)