app.py

import os
import gradio as gr
import tensorflow as tf
from tensorflow.keras.preprocessing import image as image_processor
import numpy as np
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.models import load_model
from PIL import Image, ImageDraw, ImageFont
from ultralytics import YOLO
import cv2
from huggingface_hub import from_pretrained_keras

class Config:
    ASSETS_DIR = os.path.join(os.path.dirname(__file__), 'assets')
    MODELS_DIR = os.path.join(ASSETS_DIR, 'models')
    FONT_DIR = os.path.join(ASSETS_DIR, 'arial.ttf')
    MODELS = {
        "Calculus and Caries Classification": "classification.h5",
        "Caries Detection": "detection.pt",
        "Dental X-Ray Segmentation": "dental_xray_seg.h5"
    }
    EXAMPLES = {
        "Calculus and Caries Classification": os.path.join(ASSETS_DIR, 'classification'),
        "Caries Detection": os.path.join(ASSETS_DIR, 'detection'),
        "Dental X-Ray Segmentation": os.path.join(ASSETS_DIR, 'segmentation')
    }

class ModelManager:
    @staticmethod
    def load_model(model_name: str):
        model_path = os.path.join(Config.MODELS_DIR, Config.MODELS[model_name])
        if model_name == "Dental X-Ray Segmentation":
            try:
                return from_pretrained_keras("SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net")
            except:
                return tf.keras.models.load_model(model_path)
        elif model_name == "Caries Detection":
            return YOLO(model_path)
        else:
            return load_model(model_path)


class ImageProcessor:

    def process_image(self, image: Image.Image, model_name: str):
        if model_name == "Calculus and Caries Classification":
            return self.classify_image(image, model_name)
        elif model_name == "Caries Detection":
            return self.detect_caries(image)
        elif model_name == "Dental X-Ray Segmentation":
            return self.segment_dental_xray(image)

    def classify_image(self, image: Image.Image, model_name: str):
        model = ModelManager.load_model(model_name)
        img = image.resize((224, 224))
        x = image_processor.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        img_data = preprocess_input(x)
        result = model.predict(img_data)
        if result[0][0] > result[0][1]:
            prediction = 'Calculus'
        else:
            prediction = 'Caries'
        
        # Draw the classification result on the image
        draw = ImageDraw.Draw(image)
        font = ImageFont.truetype(Config.FONT_DIR, 20)
        text = f"Classified as: {prediction}"
        text_width, text_height = draw.textsize(text, font=font)
        draw.rectangle([(0, 0), (text_width, text_height)], fill="black")
        draw.text((0, 0), text, fill="white", font=font)
        
        return image

    def detect_caries(self, image: Image.Image):
        model = ModelManager.load_model("Caries Detection")
        results = model.predict(image)
        result = results[0]
        draw = ImageDraw.Draw(image)
        font = ImageFont.truetype(Config.FONT_DIR, 20)

        for box in result.boxes:
            x1, y1, x2, y2 = [round(x) for x in box.xyxy[0].tolist()]
            class_id = box.cls[0].item()
            prob = round(box.conf[0].item(), 2)
            label = f"{result.names[class_id]}: {prob}"
            draw.rectangle([x1, y1, x2, y2], outline="red", width=2)
            text_width, text_height = draw.textsize(label, font=font)
            draw.rectangle([(x1, y1 - text_height), (x1 + text_width, y1)], fill="red")
            draw.text((x1, y1 - text_height), label, fill="white", font=font)

        return image

    def segment_dental_xray(self, image: Image.Image):
        model = ModelManager.load_model("Dental X-Ray Segmentation")
        img = np.asarray(image)
        img_cv = self.convert_one_channel(img)
        img_cv = cv2.resize(img_cv, (512, 512), interpolation=cv2.INTER_LANCZOS4)
        img_cv = np.float32(img_cv / 255)
        img_cv = np.reshape(img_cv, (1, 512, 512, 1))
        prediction = model.predict(img_cv)
        predicted = prediction[0]
        predicted = cv2.resize(predicted, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
        mask = np.uint8(predicted * 255)
        _, mask = cv2.threshold(mask, thresh=0, maxval=255, type=cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        kernel = np.ones((5, 5), dtype=np.float32)
        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=1)
        cnts, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        
        # Make a writable copy of the image
        img_writable = self.convert_rgb(img).copy()
        output = cv2.drawContours(img_writable, cnts, -1, (255, 0, 0), 3)
        return Image.fromarray(output)

    def convert_one_channel(self, img):
        if len(img.shape) > 2:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        return img

    def convert_rgb(self, img):
        if len(img.shape) == 2:
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
        return img


class GradioInterface:
    def __init__(self):
        self.image_processor = ImageProcessor()
        self.preloaded_examples = self.preload_examples()

    def preload_examples(self):
        preloaded = {}
        for model_name, example_dir in Config.EXAMPLES.items():
            examples = [os.path.join(example_dir, img) for img in os.listdir(example_dir)]
            preloaded[model_name] = examples
        return preloaded

    def create_interface(self):
        app_styles = """
        <style>
            /* Global Styles */
            body, #root {
                font-family: Helvetica, Arial, sans-serif;
                background-color: #1a1a1a;
                color: #fafafa;
            }
            /* Header Styles */
            .app-header {
                background: linear-gradient(45deg, #1a1a1a 0%, #333333 100%);
                padding: 24px;
                border-radius: 8px;
                margin-bottom: 24px;
                text-align: center;
            }
            .app-title {
                font-size: 48px;
                margin: 0;
                color: #fafafa;
            }
            .app-subtitle {
                font-size: 24px;
                margin: 8px 0 16px;
                color: #fafafa;
            }
            .app-description {
                font-size: 16px;
                line-height: 1.6;
                opacity: 0.8;
                margin-bottom: 24px;
            }
            /* Button Styles */
            .publication-links {
                display: flex;
                justify-content: center;
                flex-wrap: wrap;
                gap: 8px;
                margin-bottom: 16px;
            }
            .publication-link {
                display: inline-flex;
                align-items: center;
                padding: 8px 16px;
                background-color: #333;
                color: #fff !important;
                text-decoration: none !important;
                border-radius: 20px;
                font-size: 14px;
                transition: background-color 0.3s;
            }
            .publication-link:hover {
                background-color: #555;
            }
            .publication-link i {
                margin-right: 8px;
            }
            /* Content Styles */
            .content-container {
                background-color: #2a2a2a;
                border-radius: 8px;
                padding: 24px;
                margin-bottom: 24px;
            }
            /* Image Styles */
            .image-preview img {
                max-width: 512px;
                max-height: 512px;  
                margin: 0 auto;
                border-radius: 4px;
                display: block;
                object-fit: contain;  
            }
            /* Control Styles */
            .control-panel {
                background-color: #333;
                padding: 16px;
                border-radius: 8px;
                margin-top: 16px;
            }
            /* Gradio Component Overrides */
            .gr-button {
                background-color: #4a4a4a;
                color: #fff;
                border: none;
                border-radius: 4px;
                padding: 8px 16px;
                cursor: pointer;
                transition: background-color 0.3s;
            }
            .gr-button:hover {
                background-color: #5a5a5a;
            }
            .gr-input, .gr-dropdown {
                background-color: #3a3a3a;
                color: #fff;
                border: 1px solid #4a4a4a;
                border-radius: 4px;
                padding: 8px;
            }
            .gr-form {
                background-color: transparent;
            }
            .gr-panel {
                border: none;
                background-color: transparent;
            }
            /* Override any conflicting styles from Bulma */
            .button.is-normal.is-rounded.is-dark {
                color: #fff !important;
                text-decoration: none !important;
            }
        </style>
        """

        header_html = f"""
        <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/css/bulma.min.css">
        <link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.15.4/css/all.css">
        {app_styles}
        <div class="app-header">
            <h1 class="app-title">AI in Dentistry</h1>
            <h2 class="app-subtitle"> Advancing Imaging and Clinical Transcription</h2>
            <p class="app-description">
                This application demonstrates the use of AI in dentistry for tasks such as classification, detection, and segmentation.
            </p>
        </div>
        """

        def process_image(image, model_name):
            result = self.image_processor.process_image(image, model_name)
            return result

        def update_examples(model_name):
            examples = self.preloaded_examples[model_name]
            return gr.Dataset(samples=[[example] for example in examples])

        with gr.Blocks() as demo:
            gr.HTML(header_html)
            with gr.Row(elem_classes="content-container"):
                with gr.Column():
                    input_image = gr.Image(label="Input Image", type="pil", format="png", elem_classes="image-preview")
                    with gr.Row(elem_classes="control-panel"):
                        model_name = gr.Dropdown(
                            label="Model",
                            choices=list(Config.MODELS.keys()),
                            value="Calculus and Caries Classification",
                        )
                    examples = gr.Examples(
                        inputs=input_image,
                        examples=self.preloaded_examples["Calculus and Caries Classification"],
                    )
                with gr.Column():
                    result = gr.Image(label="Result", elem_classes="image-preview")
                    run_button = gr.Button("Run", elem_classes="gr-button")

            model_name.change(
                fn=update_examples,
                inputs=model_name,
                outputs=examples.dataset,
            )

            run_button.click(
                fn=process_image,
                inputs=[input_image, model_name],
                outputs=result,
            )

        return demo

def main():
    interface = GradioInterface()
    demo = interface.create_interface()
    demo.launch(share=False)

if __name__ == "__main__":
    main()