dvtiendat/Lungs-Radiography-Analysis

1import gradio as gr2import torch.nn.functional as F3import albumentations as A4from pipeline import *5 6def get_css(css_path):7    with open(css_path, 'r') as f:8        custom = f.read()9    10    return custom11 12def create_interface():13    custom = get_css('design/design.css')14    processor = Pipeline()15 16    with gr.Blocks(css=custom, theme=gr.themes.Soft(primary_hue='teal', secondary_hue='blue')) as interface:17        with gr.Column(variant="compact"):18            gr.Markdown("# Lungs Radiography Analysis", elem_classes='heading')19            gr.Markdown("""20                Upload/ Drop a chest X-ray image for COVID-19 diagnosis and analysis. 21            """)22        with gr.Row(equal_height=True):23            # [UPLOAD IMAGE SECTION]24            with gr.Column():25                input_image = gr.Image(26                    label="Upload Chest X-ray",27                    height=400,28                    elem_classes="upload-image"29                )30 31                # [BUTTON]32                with gr.Row():33                    submit_btn = gr.Button("Analyze Image", variant="primary", elem_classes='primary-button', scale=2)34                    clear_btn = gr.Button('Clear', variant='secondary', scale=1)35                    36            with gr.Column():37                with gr.Group(elem_classes='results-container'):                    38                    output_image = gr.Image(39                        label="COVID-19 Analysis",40                        visible=False,41                        height=40042                    )43 44                with gr.Row(equal_height=True):45                    diagnosis_label = gr.Label(label="Diagnosis Conclusion", elem_classes='results-container')46                    confidence_label = gr.Label(label="Confidence Score", elem_classes='results-container')47                48                with gr.Row():49                    diagnosis_text = gr.Textbox(50                                label="Diagnosis Details",51                                visible=False,52                                container=False53                            )54        55        # [HELP SECTION]    56        with gr.Accordion("Information", open=False):57                    gr.Markdown("""58                ### Tutorial59                1. Click the upload button/ Drag and drop a chest X-ray image.60                2. Choose 'Analyze Image'.61                3. Review the results:62                   - For COVID cases: View highlighted infection regions.63                   - For Non-COVID/Healthy cases: Review detailed diagnosis text.64            """)65                    66        def clear_inputs():67            return {68                input_image: None,69                output_image: gr.update(visible=False),70                diagnosis_text: gr.update(visible=False),71                diagnosis_label: None,72                confidence_label: None73            }74        75        def handle_prediction(image, opacity=0.4):            76            prediction, confidence, output_img, analysis_text = processor.process_image(77                image, overlay_opacity=opacity78            )79            80            confidence_class = (81                "confidence-high" if confidence > 9082                else "confidence-medium" if confidence > 7083                else "confidence-low"84            )85            print(confidence_class)86            87            is_covid = output_img is not None88            89            return {90                diagnosis_label: prediction,91                confidence_label: gr.update(92                    value=f"Confidence: {confidence:.2f}%",93                    elem_classes=[confidence_class]94                ),95                output_image: gr.update(value=output_img, visible=is_covid),96                diagnosis_text: gr.update(value=analysis_text, visible=True)97            }98 99        submit_btn.click(100            fn=handle_prediction,101            inputs=[input_image],102            outputs=[103                diagnosis_label,104                confidence_label,105                output_image,106                diagnosis_text,107            ]108        )109        110        clear_btn.click(111            fn=clear_inputs,112            inputs=[],113            outputs=[114                input_image,115                output_image,116                diagnosis_text,117                diagnosis_label,118                confidence_label119            ]120        )121        122    return interface123 124if __name__ == "__main__":125    interface = create_interface()126    interface.launch(share=True)