app.py

import os
import socket
import gradio as gr
import numpy as np
from PIL import Image, ImageDraw
from pathlib import Path
from loguru import logger
import cv2
import torch
import ultralytics
from ultralytics import YOLO
import time
import base64
import requests
import json

# API for inferences
DL4EO_API_URL = "https://dl4eo--oil-storage-predict.modal.run"

# Auth Token to access API
DL4EO_API_KEY = os.environ['DL4EO_API_KEY']

# width of the boxes on image
LINE_WIDTH = 2

# Load a model if weights are present
WEIGHTS_FILE = './weights/best.pt'
model = None
if os.path.exists(WEIGHTS_FILE):
    model = YOLO(WEIGHTS_FILE)  # previously trained YOLOv8n model
    logger.info(f"Setup for local inference")

    # check if GPU if available
    device = torch.device("cuda:0" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
    logger.info(f"Using device: {device}")

    # Check Ultralytics modules version
    logger.info(f"Ultralytics version: {ultralytics.__version__}")

logger.info(f"Gradio version: {gr.__version__}")

# Define the inference function
def predict_image(image, threshold):
    
    # Resize the image to the new size
    #image = image.resize((image.size[0] * 2, image.size[1] * 2))

    if isinstance(image, Image.Image):
        img = np.array(image)
    
    if not isinstance(img, np.ndarray) or len(img.shape) != 3 or img.shape[2] != 3:
        raise BaseException("predit_image(): input 'img' shoud be single RGB image in PIL or Numpy array format.")

    width, height = img.shape[0], img.shape[1]
    
    if model is None:
        # Encode the image data as base64
        image_base64 = base64.b64encode(np.ascontiguousarray(img)).decode()
        
        # Create a dictionary representing the JSON payload
        payload = {
            'image': image_base64,
            'shape': img.shape,
            'threshold': threshold,
        }

        headers = {
            'Authorization': 'Bearer ' + DL4EO_API_KEY,
            'Content-Type': 'application/json'  # Adjust the content type as needed
        }

        # Send the POST request to the API endpoint with the image file as binary payload
        response = requests.post(DL4EO_API_URL, json=payload, headers=headers)
        
        # Check the response status
        if response.status_code != 200:
            raise Exception(
                f"Received status code={response.status_code} in inference API"
            )
                
        json_data = json.loads(response.content)
        duration = json_data['duration']
        boxes = json_data['boxes']
    else:
        start_time = time.time()
        results = model.predict([img], imgsz=(width, height), conf=threshold)
        end_time = time.time()
        boxes = [box.xyxy.cpu().squeeze().int().tolist() for box in boxes]
        duration = end_time - start_time
        boxes = results[0].boxes

    # drow boxes on image
    draw = ImageDraw.Draw(image)

    for box in boxes:
        left, top, right, bottom = box

        if left <= 0: left = -LINE_WIDTH 
        if top <= 0: top = top - LINE_WIDTH 
        if right >= img.shape[0] - 1: right = img.shape[0] - 1 + LINE_WIDTH
        if bottom >= img.shape[1] - 1: bottom = img.shape[1] - 1 + LINE_WIDTH

        draw.rectangle([left, top, right, bottom], outline="red", width=LINE_WIDTH)
    
    return image, str(image.size), len(boxes), duration


# Define example images and their true labels for users to choose from
example_data = [
    ["./demo/588fc1fb-b86a-4fb4-8161-d9bd3a1556ca.jpg", 0.50],
    ["./demo/605ffac0-69d5-4748-92c2-48d43f51afc1.jpg", 0.50],
    ["./demo/67f7c7ad-11a1-4c7f-9f2a-da7ef50bfdd8.jpg", 0.50],
    ["./demo/b8c0e212-3669-4ff8-81a5-32191d456f86.jpg", 0.50],
    ["./demo/df5ec618-c1f3-4cfe-88b1-86799d23c22d.jpg", 0.50]]

# Define CSS for some elements
css = """
  .image-preview {
    height: 820px !important; 
    width: 800px !important;
  } 
"""
TITLE = "Oil storage detection on SPOT images (1.5 m) with YOLOv8"

# Define the Gradio Interface
demo = gr.Blocks(title=TITLE, css=css).queue()
with demo:
    gr.Markdown(f"<h1><center>{TITLE}<center><h1>")
    #gr.Markdown("<p>This demo is provided by <a href='https://www.linkedin.com/in/faudi/'>Jeff Faudi</a> and <a href='https://www.dl4eo.com/'>DL4EO</a></p>")

    with gr.Row():
        with gr.Column(scale=0):
            input_image = gr.Image(type="pil", interactive=True, scale=1)
            run_button = gr.Button(value="Run", scale=0)
            with gr.Accordion("Advanced options", open=True):
                threshold = gr.Slider(label="Confidence threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.01)
                dimensions = gr.Textbox(label="Image size", interactive=False)
                detections = gr.Number(label="Predicted objects", interactive=False)
                stopwatch = gr.Number(label="Execution time (sec.)", interactive=False, precision=3)

        with gr.Column(scale=2):
            output_image = gr.Image(type="pil", elem_classes='image-preview', interactive=False, width=800, height=800)

    run_button.click(fn=predict_image, inputs=[input_image, threshold], outputs=[output_image, dimensions, detections, stopwatch])
    gr.Examples(
        examples=example_data,
        inputs = [input_image, threshold],
        outputs = [output_image, dimensions, detections, stopwatch],
        fn=predict_image,
        #cache_examples=True,
        label='Try these images! They are not included in the training dataset.'
    )

    gr.Markdown("""<p>This demo is provided by <a href='https://www.linkedin.com/in/faudi/'>Jeff Faudi</a> and <a href='https://www.dl4eo.com/'>DL4EO</a>. 
                The model has been trained with the <a href='https://www.ultralytics.com/yolo'>Ultralytics YOLOv8</a> framework on the 
                <a href='https://www.kaggle.com/datasets/airbusgeo/airbus-oil-storage-detection-dataset'>Airbus Oil Storage Dataset</a>. 
                The associated license is <a href='https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en'>CC-BY-SA-NC</a>. 
                This demonstration CANNOT be used for commercial puposes. Please contact <a href='mailto:jeff@dl4eo.com'>me</a> 
                for more information on how you could get access to a commercial grade model or API. </p>""")


demo.launch(
    inline=False, 
    show_api=False,
    debug=False
)