import torch
import cv2
import gradio as gr
import numpy as np
import requests
from PIL import Image
from io import BytesIO
from transformers import OwlViTProcessor, OwlViTForObjectDetection
import os


# Use GPU if available
if torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")

model = OwlViTForObjectDetection.from_pretrained("google/owlvit-large-patch14").to(device)
model.eval()
processor = OwlViTProcessor.from_pretrained("google/owlvit-large-patch14")


def query_image(img, text_queries, score_threshold):
    text_queries = text_queries.split(",")

    img = np.array(img)

    target_sizes = torch.Tensor([img.shape[:2]])
    inputs = processor(text=text_queries, images=img, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = model(**inputs)

    outputs.logits = outputs.logits.cpu()
    outputs.pred_boxes = outputs.pred_boxes.cpu()
    results = processor.post_process(outputs=outputs, target_sizes=target_sizes)
    boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]

    font = cv2.FONT_HERSHEY_SIMPLEX

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

        if score >= score_threshold:
            img = cv2.rectangle(img, box[:2], box[2:], (255,0,0), 5)
            if box[3] + 25 > 768:
                y = box[3] - 10
            else:
                y = box[3] + 25

            img = cv2.putText(
                img, text_queries[label], (box[0], y), font, 1, (255,0,0), 2, cv2.LINE_AA
            )
    return img


description = """
\n\nYou can use OWL-ViT to query images with text descriptions of any object.
To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for. You
can also use the score threshold slider to set a threshold to filter out low probability predictions.
"""

with gr.Blocks() as demo:
    with gr.Column():            
        with gr.Tab("Upload image"):
            with gr.Row():
                with gr.Column():
                    inputs_file=[gr.Image(source="webcam"), gr.Textbox(),  gr.Slider(0, 1, value=0.1)]
                    submit_btn = gr.Button("Submit")

                im_output = gr.Image()


        with gr.Tab("Capture image with webcam"):
            with gr.Row():
                with gr.Column():
                    inputs_web=[gr.Image(source="webcam"),  gr.Textbox(),  gr.Slider(0, 1, value=0.1)]
                    submit_btn_web = gr.Button("Submit")

                web_output = gr.Image()


    submit_btn.click(fn=query_image, inputs= inputs_file, outputs = im_output)
    submit_btn_web.click(fn=query_image, inputs= inputs_web, outputs = web_output)

    gr.Examples(
                        examples= os.path.join("examples", "IMGP0178.jpg"),
                        fn=query_image,
                        inputs=inputs_file,
                        outputs=im_output,
                        cache_examples=False
                        )

demo.launch()