import spaces
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
import torch
import gradio as gr

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

dino_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
dino_model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-base").to(device)

def generate_colors(labels):
    import random
    random.seed(42)
    colors = {}
    for label in labels:
        colors[label] = "#{:06x}".format(random.randint(0, 0xFFFFFF))
    return colors

@spaces.GPU
def infer(img, text_queries, score_threshold, model):
    if model == "dino":
        queries = ""
        for query in text_queries:
            queries += f"{query}. "

        height, width = img.shape[:2]
        target_sizes = [(height, width)]
        inputs = dino_processor(text=queries, images=img, return_tensors="pt").to(device)

        with torch.no_grad():
            outputs = dino_model(**inputs)
            outputs.logits = outputs.logits.cpu()
            outputs.pred_boxes = outputs.pred_boxes.cpu()
            results = dino_processor.post_process_grounded_object_detection(outputs=outputs, input_ids=inputs.input_ids,
                                                                            box_threshold=score_threshold,
                                                                            target_sizes=target_sizes)
      
        boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
        result_labels = []

        for box, score, label in zip(boxes, scores, labels):
            box = [int(i) for i in box.tolist()]
            if score < score_threshold:
                continue

            # Only include the labels that are part of the input text_queries
            if model == "dino" and label in text_queries:
                result_labels.append((box, label))
        return result_labels

def query_image(img, text_queries, dino_threshold):
    text_queries = [query.strip() for query in text_queries.split(",")]
    dino_output = infer(img, text_queries, dino_threshold, "dino")
    annotations = []
    for box, label in dino_output:
        annotations.append({"label": label, "coordinates": {"x": box[0], "y": box[1], "width": box[2] - box[0], "height": box[3] - box[1]}})
    
    colors = generate_colors(text_queries)
    return (img, {"boxes": annotations, "colors": colors})

dino_threshold = gr.Slider(0, 1, value=0.12, label="Grounding DINO Threshold")
dino_output = gr.AnnotatedImage(label="Grounding DINO Output")
demo = gr.Interface(
    query_image,
    inputs=[gr.Image(label="Input Image"), gr.Textbox(label="Candidate Labels"), dino_threshold],
    outputs=[dino_output],
    title="OWLv2 ⚔ Grounding DINO",
    description="Evaluate state-of-the-art [Grounding DINO](https://huggingface.co/IDEA-Research/grounding-dino-base) zero-shot object detection models. Simply enter an image and the objects you want to find with comma, or try one of the examples. Play with the threshold to filter out low confidence predictions in the model.",
    examples=[["./warthog.jpg", "zebra, warthog", 0.16], ["./zebra.jpg", "zebra, lion", 0.16]]
)
demo.launch(debug=True)