import gradio as gr from pyzbar.pyzbar import decode from lambdas import upload_models, predict import base64 from io import BytesIO, StringIO from PIL import Image import pandas as pd import os.path import numpy as np from datetime import datetime, timedelta from descriptions import prefer_frontal_cam_html, docu_eng, docu_cat, css_image_aspect_ratio DEBUG = True config = {'possible_shifts': {'No shifts': 0}, 'possible_modes': ["waste"]} def login(username, password) -> bool: if os.path.isfile('credentials.csv'): df = pd.read_csv('credentials.csv') else: s = os.environ.get('CREDENTIALS') df = pd.read_csv(StringIO(s)) if not len(df): return False df = df.replace({np.nan: None}) for idx, row in df.iterrows(): if row['username'] == username and row['password'] == password: restaurant_id = int(row['restaurant_id']) restaurant_name = str(row['restaurant_name']) mode = 'waste' possible_modes = str(row.get('modes')).split(':') possible_shifts = {i.split(':')[0]: int(i.split(':')[1]) for i in str(row.get('shifts')).split('-')} \ if row.get('shifts') else {'no shift': None} config_aux = {'restaurant_id': restaurant_id, 'restaurant_name': restaurant_name, 'mode': mode, 'possible_modes': possible_modes, 'possible_shifts': possible_shifts, } config.clear() config.update(config_aux) return True return False def start_app(shift_id, mode, date): try: config_aux = {'date': date, 'shift_id': shift_id, 'mode': mode, 'possible_shifts': {'No shifts': 0}, 'possible_modes': ["waste"]} config.update(config_aux) gr.Info('Loading models. This may take a while') status_code, r = upload_models(**config) if status_code in (201, 200, 204): config.update(r) gr.Info('Models Correctly Loaded. Ready to predict') tab_id = 1 else: raise gr.Error(f'Error loading the models: {r}') except Exception as e: raise gr.Error(f'Error Uploading the models. \n {e}') return gr.Tabs(selected=tab_id, visible=True) def predict_app(image, patient_id=None): gr.Info('Predicting ...') buffered = BytesIO() image.save(buffered, format='JPEG') b64image = base64.b64encode(buffered.getvalue()).decode('utf-8') status_code, r = predict(b64image=b64image, patient_identifier=patient_id, **config) if status_code in (200, 201, 204): gr.Info('Prediction Successful') else: raise gr.Error(f'Error predicting {r}') # APP with (gr.Blocks(head=prefer_frontal_cam_html, css=css_image_aspect_ratio) as block): with gr.Tabs() as tabs: with gr.Tab(label='Welcome', id=0) as welcome_tab: gr.Info('Loading ...') @gr.render() def header(): gr.Markdown(f'# User: {config.get("restaurant_name", "Proppos")}', render=True) @gr.render() def render_dropdowns(): shift_dropdown = gr.Dropdown(label='Meal/Apat/Comida', value=list(config["possible_shifts"].items())[0], choices=tuple(config["possible_shifts"].items())) mode_dropdown = gr.Dropdown(label='Mode', value=config['possible_modes'][0], choices=config["possible_modes"]) days = [datetime.today().strftime('%Y-%m-%d'), (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')] date_chooser = gr.Dropdown(choices=days, label='Date/Data/Fecha', value=days[0]) start_button = gr.Button(value='START ▶️') start_button.click(fn=start_app, inputs=[shift_dropdown, mode_dropdown, date_chooser], outputs=tabs) gr.Markdown(""" * Do you have any doubt? Please, see the documentation tab. \n * Tienes alguna duda? Consulta la pestaña de documentación * Tens un dubte? Consulta la pestanya de documentació. """) logout_button = gr.Button(link='/logout', value='Logout / Exit ↩', size='sm') with gr.Tab(label='📷 Capture', id=1): # MAIN TAB TO PREDICT gr.Markdown(f""" 1. Click to Access Webcam 2. Press the red button to start recording 3. Place the tray so it is centered in the displayed image and 4. Press the grey button 'PRESS TO PREDICT' """) im = gr.Image(sources=['webcam'], mirror_webcam=False, type='pil', show_share_button=False, show_download_button=False, height='720px', width='1280px', min_width=0, elem_id="image-container") ## Patient ID searcher. with gr.Accordion(open=False): def get_barcode_from_image(image): if not image: raise gr.Error('No image has been taken yet') d = decode(image) if not d: raise gr.Error('Cannot find a barcode on the Image') barcode = d[0].data.decode('utf8') if d else None return barcode get_barcode_button = gr.Button('Get Patient ID (Barcode) from Image') eater_id = gr.Textbox(label='Patient ID', interactive=True, value=None, placeholder='Write the patient id or take a photo') get_barcode_button.click(fn=get_barcode_from_image, inputs=[im], outputs=eater_id) ## Patient ID searcher. Uncomment if needed # with gr.Accordion(open=False): # eater_id = gr.Textbox(label='Patient Identification', placeholder='Searching Patient ID') # # current_eater_id = {'value': None} # @gr.on(inputs=im, outputs=eater_id) # def search_eater_id(image): # d = []#decode(image) # default_value = None # current_value = current_eater_id['value'] or default_value # new_value = d[0].data if d else default_value # # If it is really a new value different from the default one, change it. # final_value = new_value if new_value != default_value else current_value # current_eater_id['value'] = final_value # return final_value # eater_id = None b = gr.Button('PRESS TO PREDICT') b.click(fn=predict_app, inputs=[im, eater_id], outputs=gr.Info()) # add eater id to input if needed clear = gr.Button('Clear Image') clear.click(fn=lambda: None, outputs=im) with gr.Tab(label='ℹ️ Status', id=2): gr.Markdown(' Press the button to see the status of the Application and technical information') load_status_button = gr.Button('Load Status') status_json = gr.Json(label='Status') load_status_button.click(fn=lambda: config, outputs=status_json) with gr.Tab(label='📄 Documentation', id=3): choices = [('ENG 🇬🇧', docu_eng), ('CAT', docu_cat)] c = gr.Radio(label='Select the language', choices=choices) @gr.render() def display_docu(): d = gr.Markdown(docu_eng) c.change(fn=lambda x: x, inputs=c, outputs=d) block.launch(show_api=False, auth=login)