app.py

import os
import re
import random
from dataclasses import dataclass
from functools import partial

import torch
import gradio as gr
import spaces
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from PIL import Image, ImageDraw


# --- Configuration ---
@dataclass
class Configuration:
    dataset_id: str = "ariG23498/license-detection-paligemma"
    model_id: str = "google/gemma-3-4b-pt"
    checkpoint_id: str = "ariG23498/gemma-3-4b-pt-object-detection"
    device: str = "cuda" if torch.cuda.is_available() else "cpu"
    dtype: torch.dtype = torch.bfloat16
    batch_size: int = 4
    learning_rate: float = 2e-05
    epochs: int = 1


# --- Utils ---
def parse_paligemma_label(label, width, height):
    # Extract location codes
    loc_pattern = r"<loc(\d{4})>"
    locations = [int(loc) for loc in re.findall(loc_pattern, label)]

    # Extract category (everything after the last location code)
    category = label.split(">")[-1].strip()

    # Order in PaliGemma format is: y1, x1, y2, x2
    y1_norm, x1_norm, y2_norm, x2_norm = locations

    # Convert normalized coordinates to image coordinates
    x1 = (x1_norm / 1024) * width
    y1 = (y1_norm / 1024) * height
    x2 = (x2_norm / 1024) * width
    y2 = (y2_norm / 1024) * height

    return category, [x1, y1, x2, y2]


def visualize_bounding_boxes(image, label, width, height):
    # Copy image for drawing
    draw_image = image.copy()
    draw = ImageDraw.Draw(draw_image)

    category, bbox = parse_paligemma_label(label, width, height)

    draw.rectangle(bbox, outline="red", width=2)
    draw.text((bbox[0], max(0, bbox[1] - 10)), category, fill="red")

    return draw_image


def test_collate_function(batch_of_samples, processor, dtype):
    images = []
    prompts = []
    for sample in batch_of_samples:
        images.append([sample["image"]])
        prompts.append(f"{processor.tokenizer.boi_token} detect \n\n")

    batch = processor(images=images, text=prompts, return_tensors="pt", padding=True)
    batch["pixel_values"] = batch["pixel_values"].to(dtype)
    return batch, images


# --- Initialize ---
cfg = Configuration()

processor = AutoProcessor.from_pretrained(cfg.checkpoint_id)
model = Gemma3ForConditionalGeneration.from_pretrained(
    cfg.checkpoint_id,
    torch_dtype=cfg.dtype,
    device_map="cpu",
)
model.eval()

test_dataset = load_dataset(cfg.dataset_id, split="test")


def get_sample():
    sample = random.choice(test_dataset)
    images = [[sample["image"]]]
    prompts = [f"{processor.tokenizer.boi_token} detect \n\n"]

    batch = processor(images=images, text=prompts, return_tensors="pt", padding=True)
    batch["pixel_values"] = batch["pixel_values"].to(cfg.dtype)

    return batch, sample["image"]


# --- Prediction Logic ---
@spaces.GPU
def run_prediction():
    model.to(cfg.device)

    batch, raw_image = get_sample()
    batch = {k: v.to(cfg.device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}

    with torch.no_grad():
        generation = model.generate(**batch, max_new_tokens=100)
    decoded = processor.batch_decode(generation, skip_special_tokens=True)[0]

    image = raw_image  # ✅ FIXED: raw_image is already a PIL.Image
    width, height = image.size

    result_image = visualize_bounding_boxes(image, decoded, width, height)
    return result_image


# --- Gradio Interface ---
demo = gr.Interface(
    fn=run_prediction,
    inputs=[],
    outputs=gr.Image(type="pil", label="Detected Bounding Box"),
    title="Gemma3 Object Detector",
    description="Click 'Generate' to visualize a prediction from a randomly sampled test image.",
)

if __name__ == "__main__":
    demo.launch()