app.py

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


with gr.Blocks() as demo:
    with gr.Column():            
        with gr.Tab("Upload image"):
            gr.Markdown(""" 
                         \n OWL-ViT(https://huggingface.co/docs/transformers/model_doc/owlvit) is a vision transformer architecture that can be used for image inputs with text queries. This is achieved by adding a text embedding layer to the model, which allows it to process both image and text inputs.
                         \n  You can use to query images with text descriptions of any object. To use it, simply upload an image or capture one with the webcam and enter comma separated text descriptions of objects you want to query the image for. 
                        """)
            with gr.Row():
                with gr.Column():

                    gr.Markdown("""Insert an image below and add text descriptions of what you are looking for. 
                                If you wish for assistance to find the right text queries you can ask for help from [ChatBRD](https://chatbrd.novonordisk.com/#/) but remember you need to log on novos VPN before you can use it.""")
                    inputf1 = gr.Image(source="upload")
                    inputf2 = gr.Textbox()
                    gr.Markdown("""
                                \n You can also use the score threshold slider to set a threshold to filter out lower probability predictions.
                                """)
                    inputf3 = gr.Slider(0, 1, value=0.1)

                    inputs_file = [inputf1, inputf2, inputf3]
                    submit_btn = gr.Button("Submit")

                im_output = gr.Image()

        with gr.Tab("Capture image with webcam"):
            with gr.Row():
                with gr.Column():
                    gr.Markdown("""Insert an image below and add text descriptions of what you are looking for. 
                                If you wish for assistance to find the right text queries you can ask for help from [ChatBRD](https://chatbrd.novonordisk.com/#/) but remember you need to log on novos VPN before you can use it.""")
                    inputweb1 = gr.Image(source="webcam")
                    inputweb2 = gr.Textbox()
                    gr.Markdown("""
                                \n You can also use the score threshold slider to set a threshold to filter out lower probability predictions.
                                """)
                    inputweb3 = gr.Slider(0, 1, value=0.1)

                    inputs_web = [inputweb1, inputweb2, inputweb3]
                    submit_btn_web = gr.Button("Submit")

                web_output = gr.Image()

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

    #gr.Markdown("## Image Examples")
    #examples= [os.path.join(os.path.dirname(__file__), "IMGP0178.jpg")]    
    #gr.Examples(postprocess=False,
    #              examples= examples, 
    #              inputs=[inputs_file], 
    #              outputs=[im_output], 
    #              fn=query_image
    #              )
if __name__ == "__main__":

    demo.queue(
                    concurrency_count=40, # When you increase the concurrency_count parameter in queue(), max_threads() in launch() is automatically increased as well.
                    max_size=25, # Maximum number of requests that the queue processes
                    api_open = False # When creating a Gradio demo, you may want to restrict all traffic to happen through the user interface as opposed to the programmatic API that is automatically created for your Gradio demo.  
                        )
    demo.launch(auth=("novouser", "bstad2023"))