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| 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, clear_database | |
| from chatbot import chatbot, toggle_visibility, 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" | |
| return f"Cataract Database: {cataract_status}\nGlaucoma Database: {glaucoma_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"<img src='{logo_url}' alt='Logo' width='150'/>") | |
| 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. You could try our diagnostic model by downloading test image on our Huggingface Repo File.") | |
| gr.Markdown("We were sorry that we provide the demo in different file because we misunderstood the 5 minutes rule. we thought that 5 minutes are excluding demo. you can see our demo here : https://youtu.be/yknUdPnbXFg") | |
| 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") | |
| gr.Examples([["test-cataract-image.jpg"], ["test-cataract-image2.jpg"], ["test-cataract-image3.jpg"], ["test-cataract-image4.jpg"]], inputs=[image_input],label='Or use one of these examples:') | |
| 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") | |
| gr.Examples([["test-glaucoma-image.jpg"], ["test-glaucoma-image2.jpg"], ["test-glaucoma-image3.jpg"], ["test-glaucoma-image4.jpg"]], inputs=[image_input],label='Or use one of these examples:') | |
| 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) | |
| # Buttons to clear databases | |
| clear_cataract_db_btn = gr.Button("Clear Cataract Database") | |
| clear_glaucoma_db_btn = gr.Button("Clear Glaucoma Database") | |
| clear_cataract_db_btn.click( | |
| fn=clear_database, | |
| inputs=[gr.State(value=db_path_cataract), gr.State(value="cataract_results")], | |
| outputs=db_display | |
| ) | |
| clear_glaucoma_db_btn.click( | |
| fn=clear_database, | |
| inputs=[gr.State(value=db_path_glaucoma), gr.State(value="results")], | |
| outputs=db_display | |
| ) | |
| demo.launch() |