import gradio as gr import os import numpy as np from cataract import combined_prediction, save_cataract_prediction_to_db, predict_object_detection from glaucoma import combined_prediction_glaucoma, submit_to_db, predict_image from database import get_db_data, format_db_data from chatbot import chatbot, update_patient_history, generate_voice_response from PIL import Image # Define the custom theme theme = gr.themes.Soft( primary_hue="neutral", secondary_hue="neutral", neutral_hue="gray", font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'] ).set( body_background_fill="#ffffff", block_background_fill="#0a2b42", block_border_width="1px", block_title_background_fill="#0a2b42", input_background_fill="#ffffff", button_secondary_background_fill="#0a2b42", border_color_primary="#800080", background_fill_secondary="#ffffff", color_accent_soft="transparent" ) # Define custom CSS css = """ body { color: #0a2b42; /* Dark blue font */ } .light body { color: #0a2b42; /* Dark blue font */ } input, textarea { background-color: #ffffff !important; /* White background for text boxes */ color: #0a2b42 !important; /* Dark blue font for text boxes */ } """ logo_url = "https://huggingface.co/spaces/Nexus-Community/nexus-main/resolve/main/Nexus-Hub.png" db_path_cataract = "cataract_results.db" db_path_glaucoma = "glaucoma_results.db" def display_db_data(): """Fetch and format the data from the database for display.""" glaucoma_data, cataract_data = get_db_data(db_path_glaucoma, db_path_cataract) formatted_data = format_db_data(glaucoma_data, cataract_data) return formatted_data def check_db_status(): """Check the status of the databases and return a status message.""" cataract_status = "Loaded" if os.path.exists(db_path_cataract) else "Not Loaded" glaucoma_status = "Loaded" if os.path.exists(db_path_glaucoma) else "Not Loaded" context_status = "Loaded" if os.path.exists(db_path_context) else "Not Loaded" return f"Cataract Database: {cataract_status}\nGlaucoma Database: {glaucoma_status}\nContext Database: {context_status}" def toggle_input_visibility(input_type): if input_type == "Voice": return gr.update(visible=True), gr.update(visible=False) else: return gr.update(visible=False), gr.update(visible=True) def process_image(image): # Run the analyzer model blended_image, red_quantity, green_quantity, blue_quantity, raw_response, stage, save_message, debug_info = combined_prediction(image) # Run the object detection model predicted_image_od, raw_response_od = predict_object_detection(image) return blended_image, red_quantity, green_quantity, blue_quantity, raw_response, stage, save_message, debug_info, predicted_image_od, raw_response_od with gr.Blocks(theme=theme) as demo: gr.HTML(f"Logo") gr.Markdown("## Wellness-Nexus V.1.0") gr.Markdown("This app helps people to diagnose their cataract and glaucoma, both respectively #1 and #2 cause of blindness in the world") with gr.Tab("Cataract Screener and Analyzer"): with gr.Row(): image_input = gr.Image(type="numpy", label="Upload an Image") submit_btn = gr.Button("Submit") with gr.Row(): segmented_image_cataract = gr.Image(type="numpy", label="Segmented Image") predicted_image_od = gr.Image(type="numpy", label="Predicted Image") with gr.Column(): red_quantity_cataract = gr.Slider(label="Red Quantity", minimum=0, maximum=255, interactive=False) green_quantity_cataract = gr.Slider(label="Green Quantity", minimum=0, maximum=255, interactive=False) blue_quantity_cataract = gr.Slider(label="Blue Quantity", minimum=0, maximum=255, interactive=False) with gr.Row(): cataract_stage = gr.Textbox(label="Cataract Stage", interactive=False) raw_response_cataract = gr.Textbox(label="Raw Response", interactive=False) submit_value_btn_cataract = gr.Button("Submit Values to Database") db_response_cataract = gr.Textbox(label="Database Response") debug_cataract = gr.Textbox(label="Debug Message", interactive=False) submit_btn.click( process_image, inputs=image_input, outputs=[ segmented_image_cataract, red_quantity_cataract, green_quantity_cataract, blue_quantity_cataract, raw_response_cataract, cataract_stage, db_response_cataract, debug_cataract, predicted_image_od ] ) submit_value_btn_cataract.click( lambda img, red, green, blue, stage: save_cataract_prediction_to_db(Image.fromarray(img), red, green, blue, stage), inputs=[segmented_image_cataract, red_quantity_cataract, green_quantity_cataract, blue_quantity_cataract, cataract_stage], outputs=[db_response_cataract, debug_cataract] ) with gr.Tab("Glaucoma Analyzer and Screener"): with gr.Row(): image_input = gr.Image(type="numpy", label="Upload an Image") mask_threshold_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Mask Threshold") with gr.Row(): submit_btn_segmentation = gr.Button("Submit Segmentation") submit_btn_od = gr.Button("Submit Object Detection") with gr.Row(): segmented_image = gr.Image(type="numpy", label="Segmented Image") predicted_image_od = gr.Image(type="numpy", label="Predicted Image") with gr.Row(): raw_response_od = gr.Textbox(label="Raw Result") with gr.Column(): cup_area = gr.Textbox(label="Cup Area") disk_area = gr.Textbox(label="Disk Area") rim_area = gr.Textbox(label="Rim Area") rim_to_disc_ratio = gr.Textbox(label="Rim to Disc Ratio") ddls_stage = gr.Textbox(label="DDLS Stage") with gr.Column(): submit_value_btn = gr.Button("Submit Values to Database") db_response = gr.Textbox(label="Database Response") debug_glaucoma = gr.Textbox(label="Debug Message", interactive=False) def process_segmentation_image(img, mask_thresh): # Run the segmentation model return combined_prediction_glaucoma(img, mask_thresh) def process_od_image(img): # Run the object detection model image_with_boxes, raw_predictions = predict_image(img) return image_with_boxes, raw_predictions submit_btn_segmentation.click( fn=process_segmentation_image, inputs=[image_input, mask_threshold_slider], outputs=[ segmented_image, cup_area, disk_area, rim_area, rim_to_disc_ratio, ddls_stage ] ) submit_btn_od.click( fn=process_od_image, inputs=[image_input], outputs=[ predicted_image_od, raw_response_od ] ) submit_value_btn.click( lambda img, cup, disk, rim, ratio, stage: submit_to_db(img, cup, disk, rim, ratio, stage), inputs=[image_input, cup_area, disk_area, rim_area, rim_to_disc_ratio, ddls_stage], outputs=[db_response, debug_glaucoma] ) with gr.Tab("Chatbot"): with gr.Row(): input_type_dropdown = gr.Dropdown(label="Input Type", choices=["Voice", "Text"], value="Voice") tts_model_dropdown = gr.Dropdown(label="TTS Model", choices=["Ryan (ESPnet)", "Nithu (Custom)"], value="Nithu (Custom)") submit_btn_chatbot = gr.Button("Submit") with gr.Row(): audio_input = gr.Audio(type="filepath", label="Record your voice", visible=True) text_input = gr.Textbox(label="Type your question", visible=False) with gr.Row(): answer_textbox = gr.Textbox(label="Answer") answer_audio = gr.Audio(label="Answer as Speech", type="filepath") generate_voice_btn = gr.Button("Generate Voice Response") with gr.Row(): log_messages_textbox = gr.Textbox(label="Log Messages", lines=10) db_status_textbox = gr.Textbox(label="Database Status", interactive=False) input_type_dropdown.change( fn=toggle_input_visibility, inputs=[input_type_dropdown], outputs=[audio_input, text_input] ) submit_btn_chatbot.click( fn=chatbot, inputs=[audio_input, input_type_dropdown, text_input], outputs=[answer_textbox, db_status_textbox] ) generate_voice_btn.click( fn=generate_voice_response, inputs=[tts_model_dropdown, answer_textbox], outputs=[answer_audio, db_status_textbox] ) fetch_db_btn = gr.Button("Fetch Database") fetch_db_btn.click( fn=update_patient_history, inputs=[], outputs=[db_status_textbox] ) with gr.Tab("Database Upload and View"): gr.Markdown("### Store and Retrieve Context Information") db_display = gr.HTML() load_db_btn = gr.Button("Load Database Content") load_db_btn.click(display_db_data, outputs=db_display) demo.launch()