app.py

import netron
import threading
import gradio as gr
import os
from PIL import Image
import cv2
import numpy as np
from yolov5 import xai_yolov5
from yolov8 import xai_yolov8s

# Sample images directory
sample_images = {
    "Sample 1": os.path.join(os.getcwd(), "data/xai/sample1.jpeg"),
    "Sample 2": os.path.join(os.getcwd(), "data/xai/sample2.jpg"),
}

def load_sample_image(sample_name):
    """Load a sample image based on user selection."""
    image_path = sample_images.get(sample_name)
    if image_path and os.path.exists(image_path):
        return Image.open(image_path)
    return None

def process_image(sample_choice, uploaded_image, yolo_versions, target_lyr = -5, n_components = 8):
    """Process the image using selected YOLO models."""
    # Load sample or uploaded image
    if uploaded_image is not None:
        image = uploaded_image
    else:
        image = load_sample_image(sample_choice)

    # Preprocess image
    image = np.array(image)
    image = cv2.resize(image, (640, 640))
    result_images = []

    # Apply selected models
    for yolo_version in yolo_versions:
        if yolo_version == "yolov5":
            result_images.append(xai_yolov5(image, target_lyr = -5, n_components = 8)) 
        elif yolo_version == "yolov8s":
            result_images.append(xai_yolov8s(image))
        else:
            result_images.append((Image.fromarray(image), f"{yolo_version} not implemented."))
    return result_images

def view_model(selected_models):
    """Generate Netron visualization for the selected models."""
    netron_html = ""
    for model in selected_models:
        if model == "yolov5":
            netron_html = f"""
            <iframe 
                src="https://netron.app/?url=https://huggingface.co/FFusion/FFusionXL-BASE/blob/main/vae_encoder/model.onnx" 
                width="100%" 
                height="800" 
                frameborder="0">
            </iframe>
            """
    return netron_html if netron_html else "<p>No valid models selected for visualization.</p>"

custom_css = """

#custom-row {
    margin: 0 !important;
    padding: 0 !important;
    height: fit-content !important;
    display: flex !important;
    justify-content: center !important;
}
#highlighted-text {
    color: blue !important;
    font-size: 32px !important;
    font-weight: bold !important;
}
"""

# Then in the Gradio interface:

with gr.Blocks(css=custom_css) as interface:
    gr.Markdown("""
    ## NeuralVista
    <p>Welcome to <span class="highlighted-text">NeuralVista</span>, a powerful tool designed to help you visualize object detection models in action. 
    With the integration of state-of-the-art YOLO models, you can explore the performance of object detection algorithms on various images.</p>
    <p>Whether you're looking to analyze pre-existing samples or upload your own images, NeuralVista allows you to experiment with different YOLO versions, 
    providing you with valuable insights into how these models interpret and detect objects. Additionally, you can view deep feature factorization outputs 
    and gain a deeper understanding of model behavior at different layers, all within an intuitive interface.</p>
    """)
    
    # Default sample
    default_sample = "Sample 1"

    with gr.Row():
        # Left side: Sample selection and image upload
        with gr.Column():
            sample_selection = gr.Radio(
                choices=list(sample_images.keys()),
                label="Select a Sample Image",
                value=default_sample,
            )

            upload_image = gr.Image(
                label="Upload an Image",
                type="pil",  
            )

            selected_models = gr.CheckboxGroup(
                choices=["yolov5", "yolov8s"],
                value=["yolov5"],
                label="Select Model(s)",
            )

            run_button = gr.Button("Run", elem_id="run_button")

        with gr.Column():
            sample_display = gr.Image(
                value=load_sample_image(default_sample),  
                label="Selected Sample Image",
            )

    # Results and visualization
    with gr.Row(elem_classes="custom-row"):
        result_gallery = gr.Gallery(
            label="Results",
            rows=1, 
            height="auto",       # Adjust height automatically based on content
            columns=1 ,
            object_fit="contain"

        ) 
        netron_display = gr.HTML(label="Netron Visualization")

    # Update sample image
    sample_selection.change(
        fn=load_sample_image,
        inputs=sample_selection,
        outputs=sample_display,
    )
    with gr.Row(elem_classes="custom-row"):
        dff_gallery = gr.Gallery(
            label="Deep Feature Factorization",
            rows=2,          # 8 rows
            columns=4,       # 1 image per row
            object_fit="fit",
            height="auto"    # Adjust as needed
        ) 


    # Multi-threaded processing
    def run_both(sample_choice, uploaded_image, selected_models):
        results = []
        netron_html = ""

        # Thread to process the image
        def process_thread():
            nonlocal results
            target_lyr = -5 
            n_components = 8
            results = process_image(sample_choice, uploaded_image, selected_models, target_lyr = -5, n_components = 8)

        # Thread to generate Netron visualization
        def netron_thread():
            nonlocal netron_html
            netron_html = view_model(selected_models)

        # Launch threads
        t1 = threading.Thread(target=process_thread)
        t2 = threading.Thread(target=netron_thread)
        t1.start()
        t2.start()
        t1.join()
        t2.join()
        image1, text, image2 = results[0]
        if isinstance(image2, list):
            # Check if image2 contains exactly 8 images
            if len(image2) == 8:
                print("image2 contains 8 images.")
            else:
                print("Warning: image2 does not contain exactly 8 images.")
        else:
            print("Error: image2 is not a list of images.")
        return [(image1, text)], netron_html, image2

    # Run button click

    run_button.click(
        fn=run_both,
        inputs=[sample_selection, upload_image, selected_models],
        outputs=[result_gallery, netron_display, dff_gallery],
    )

# Launch Gradio interface
if __name__ == "__main__":
    interface.launch(share=True)