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demo_interface.py
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import streamlit as st
import tifffile
import pydicom
from scipy.ndimage import zoom
import torch
from core.models.dani_model import dani_model
import numpy as np
from PIL import Image
import base64
import time
# Funzione per convertire un'immagine in base64
def image_to_base64(image_path):
with open(image_path, "rb") as img_file:
return base64.b64encode(img_file.read()).decode()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Dati di esempio predefiniti
esempi = {
"FRO -> LAT": {'FRO': 'FtoL.png', 'LAT': 'LfromF.png'},
"FRO -> REP": {'FRO': '31d9847f-987fcf63-704f7496-d2b21eb8-63cd973e.tiff', 'REP': 'Small bilateral pleural effusions, left greater than right.'},
"FRO -> LAT + REP": {'FRO': '81bca127-0c416084-67f8033c-ecb26476-6d1ecf60.tiff', 'LAT': 'd52a0c5c-bb7104b0-b1d821a5-959984c3-33c04ccb.tiff', 'REP': 'No acute intrathoracic process. Heart Size is normal. Lungs are clear. No pneumothorax'},
"LAT -> FRO": {'LAT': 'LtoF.png', 'FRO': 'FfromL.png'},
"LAT -> REP": {'LAT': 'd52a0c5c-bb7104b0-b1d821a5-959984c3-33c04ccb.tiff', 'REP': 'no acute cardiopulmonary process. if concern for injury persists, a dedicated rib series with markers would be necessary to ensure no rib fractures.'},
"LAT -> FRO + REP": {'LAT': 'reald52a0c5c-bb7104b0-b1d821a5-959984c3-33c04ccb.tiff', 'FRO': 'ab37274f-b4c1fc04-e2ff24b4-4a130ba3-cd167968.tiff', 'REP': 'No acute intrathoracic process. If there is strong concern for rib fracture, a dedicated rib series may be performed.'},
"REP -> FRO": {'REP': 'Left lung opacification which may reflect pneumonia superimposed on metastatic disease.', 'FRO': '02aa804e-bde0afdd-112c0b34-7bc16630-4e384014.tiff'},
"REP -> LAT": {'REP': 'Bilateral pleural effusions, cardiomegaly and mild edema suggest fluid overload.', 'LAT': '489faba7-a9dc5f1d-fd7241d6-9638d855-eaa952b1.tiff'},
"REP -> FRO + LAT": {'REP': 'No acute intrathoracic process. The lungs are clean and heart is normal size.', 'FRO': 'f27ba7cd-44486c2e-29f3e890-f2b9f94e-84110448.tiff', 'LAT': 'b20c9570-de77944a-b8604ba0-73305a7b-d608a72b.tiff'},
"FRO + LAT -> REP": {'FRO': '95856dd1-5878b5b1-9c104817-760c0122-6187946f.tiff', 'LAT': '3723d912-71940d69-4fef2dd2-27af5a7b-127ba20c.tiff', 'REP': 'Opacities in the right upper or middle lobe, maybe early pneumonia.'},
"FRO + REP -> LAT": {'FRO': 'e7f21453-7956d79a-44e44614-fae8ff16-d174d1a0.tiff', 'REP': 'No focal consolidation.', 'LAT': '8037e6b9-06367464-a4ccd63a-5c5c5a81-ce3e7ffc.tiff'},
"LAT + REP -> FRO": {'LAT': '02c66644-b1883a91-54aed0e7-62d25460-398f9865.tiff', 'REP': 'No evidence of acute cardiopulmonary process.', 'FRO': 'b1f169f1-12177dd5-2fa1c4b1-7b816311-85d769e9.tiff'}
}
# CSS per personalizzare il tema
st.markdown("""
<style>
/* Sfondo scuro */
body {
background-color: #121212;
color: white;
}
/* Personalizzazione del titolo */
.title {
font-size: 35px !important;
font-weight: bold;
color: #f63366;
}
/* Personalizzazione dei sottotitoli e testi principali */
.stText, .stButton, .stMarkdown {
font-size: 18px !important;
}
</style>
""", unsafe_allow_html=True)
# Sostituisci questo con il link dell'immagine online
logo_1_path = "./DEMO/Loghi/Logo_UCBM.png" # Sostituisci con il percorso del primo logo
logo_2_path = "./DEMO/Loghi/Logo UmU.png" # Sostituisci con il percorso del secondo logo
logo_3_path = "./DEMO/Loghi/Logo COSBI.png" # Sostituisci con il percorso del terzo logo
logo_4_path = "./DEMO/Loghi/logo trasparent.png" # Sostituisci con il percorso del quarto logo
# Converti le immagini in base64
logo_1_base64 = image_to_base64(logo_1_path)
logo_2_base64 = image_to_base64(logo_2_path)
logo_3_base64 = image_to_base64(logo_3_path)
logo_4_base64 = image_to_base64(logo_4_path)
# CSS per posizionare i loghi in basso a destra e renderli piccoli
st.markdown(f"""
<style>
.footer {{
position: fixed;
bottom: 20px;
right: 20px;
z-index: 100;
display: flex;
gap: 10px; /* Spazio tra i loghi */
}}
.footer img {{
height: 60px; /* Altezza dei loghi */
width: auto; /* Mantiene il rapporto di aspetto originale */
}}
</style>
<div class="footer">
<img src="data:image/png;base64,{logo_1_base64}" alt="Logo 1">
<img src="data:image/png;base64,{logo_2_base64}" alt="Logo 2">
<img src="data:image/png;base64,{logo_3_base64}" alt="Logo 3">
<img src="data:image/png;base64,{logo_4_base64}" alt="Logo 4">
</div>
""", unsafe_allow_html=True)
# Inizializzazione dello stato della sessione
if 'step' not in st.session_state:
st.session_state['step'] = 1
if 'selected_option' not in st.session_state:
st.session_state['selected_option'] = None
if 'frontal_file' not in st.session_state:
st.session_state['frontal_file'] = None
if 'lateral_file' not in st.session_state:
st.session_state['lateral_file'] = None
if 'report' not in st.session_state:
st.session_state['report'] = ""
if 'inputs' not in st.session_state:
st.session_state['inputs'] = None
if 'outputs' not in st.session_state:
st.session_state['outputs'] = None
if 'frontal' not in st.session_state:
st.session_state['frontal'] = None
if 'lateral' not in st.session_state:
st.session_state['lateral'] = None
if 'report' not in st.session_state:
st.session_state['report'] = ""
if 'generate' not in st.session_state:
st.session_state['generate'] = False
# Inizializza inference_tester solo una volta
if 'inference_tester' not in st.session_state:
model_load_paths = ['CoDi_encoders.pth', 'CoDi_text_diffuser.pth', 'CoDi_video_diffuser_8frames.pth']
st.session_state['inference_tester'] = dani_model(model='thesis_model',
data_dir='/mimer/NOBACKUP/groups/snic2022-5-277/dmolino/checkpoints/',
pth=model_load_paths, load_weights=True)
inference_tester = st.session_state['inference_tester']
# Caricamento dei pesi Clip, Optimus, Frontal, Lateral e Text una sola volta
if 'weights_loaded' not in st.session_state:
clip_weights = 'Clip_Training/saved_checkpoints/Training_Clip_5e^-5/checkpoint_29_epoch_Training_Clip_5e^-5.pt'
a, b = inference_tester.net.clip.load_state_dict(torch.load(clip_weights, map_location=device), strict=False)
optimus_weights = 'Report_Training/saved_checkpoints/VAE/checkpoint_99_epoch_VAE-Training-Prova1.pt'
optimus_weights = torch.load(optimus_weights, map_location='cpu')
a, b = inference_tester.net.optimus.load_state_dict(optimus_weights, strict=False)
frontal_weights = '/mimer/NOBACKUP/groups/snic2022-5-277/dmolino/CXR_Training/saved_checkpoints/Frontal/checkpoint_99_epoch_Training-Frontal-MultiPrompt-New.pt'
frontal_weights = torch.load(frontal_weights, map_location='cpu')
for key in list(frontal_weights.keys()):
if 'unet_image' in key:
value = frontal_weights.pop(key)
new_key = key.replace('unet_image', 'unet_frontal')
frontal_weights[new_key] = value
lateral_weights = '/mimer/NOBACKUP/groups/snic2022-5-277/dmolino/CXR_Training/saved_checkpoints/Lateral/checkpoint_99_epoch_Training-Lateral-MultiPrompt-New.pt'
lateral_weights = torch.load(lateral_weights, map_location='cpu')
for key in list(lateral_weights.keys()):
if 'unet_image' in key:
value = lateral_weights.pop(key)
new_key = key.replace('unet_image', 'unet_lateral')
lateral_weights[new_key] = value
a, b = inference_tester.net.model.load_state_dict(frontal_weights, strict=False)
a, b = inference_tester.net.model.load_state_dict(lateral_weights, strict=False)
text_weights = '/mimer/NOBACKUP/groups/snic2022-5-277/dmolino/Report_Training/saved_checkpoints/checkpoint_99_epoch_Report_Diffusion_Training-MultiPrompt-New.pt'
text_weights = torch.load(text_weights, map_location='cpu')
a, b = inference_tester.net.model.load_state_dict(text_weights, strict=False)
st.session_state['weights_loaded'] = True # Indica che i pesi sono stati caricati
# Usa inference_tester dalla sessione
inference_tester = st.session_state['inference_tester']
st.markdown('<h1 style="text-align: center" class="title">MedCoDi-M</h1>', unsafe_allow_html=True)
if st.session_state['step'] == 1:
# Breve descrizione del lavoro
st.markdown("""
<div style='text-align: justify; font-size: 18px; line-height: 1.6;'>
This thesis introduces MedCoDi-M, a novel multi-prompt vision-language model for multi-modal medical data generation.
In this demo, you will be able to perform various generation tasks including frontal and lateral chest X-rays and clinical report generation.
MedCoDi-M enables flexible, any-to-any generation across different medical data modalities, utilizing contrastive learning and a modular approach for enhanced performance.
</div>
""", unsafe_allow_html=True)
# lasciamo un po' di spazio
st.markdown('<br>', unsafe_allow_html=True)
image_path = "./DEMO/Loghi/model_final.png" # Sostituisci con il percorso della tua immagine
st.image(image_path, caption='Framework of MedCoDi-M', use_column_width=True)
# Caption con dimensione del testo migliorata
st.markdown("""
<div style='text-align: center; font-size: 16px; font-style: italic; margin-top: 10px;'>
Framework of MedCoDi-M: This demo allows you to generate frontal and lateral chest X-rays, as well as medical reports, through the MedCoDi-M model.
</div>
""", unsafe_allow_html=True)
# lasciamo un po' di spazio
st.markdown('<br>', unsafe_allow_html=True)
# mettiamo un bottone con scritto Try it out
if st.button("Try it out!"):
st.session_state['step'] = 2
st.rerun()
# Fase 1: Selezione dell'opzione
if st.session_state['step'] == 2:
# Opzioni disponibili
options = [
"FRO -> LAT", "FRO -> REP", "FRO -> LAT + REP",
"LAT -> FRO", "LAT -> REP", "LAT -> FRO + REP",
"REP -> FRO", "REP -> LAT", "REP -> FRO + LAT",
"FRO + LAT -> REP", "FRO + REP -> LAT", "LAT + REP -> FRO"
]
# Messaggio di selezione con dimensione aumentata
st.markdown(
"<h4 style='text-align: justify'><strong>Select the type of generation you want to perform: (FRO = Frontal, LAT = Lateral, REP = Report)</strong></h4>",
unsafe_allow_html=True)
# Aumentare la dimensione di "Please select an option:"
st.markdown(
"<h4 style='text-align: justify'><strong>Please select an option:</strong></h4>",
unsafe_allow_html=True)
# Reset esplicito del valore di `selectbox` in caso di reset
st.session_state['selected_option'] = st.selectbox(
"", options, key='selectbox_option', index=0) # Rimuoviamo il testo dal selectbox
st.markdown('<br>', unsafe_allow_html=True)
# Creiamo colonne per i pulsanti
col1, col2, col3 = st.columns(3)
# Pulsante per procedere con l'inferenza
with col1:
if st.button("Inference"):
st.session_state['step'] = 3 # Passa al passo 3
st.rerun()
# Pulsante per provare un esempio
with col2:
if st.button("Try an example"):
st.session_state['step'] = 5 # Passa al passo 5
st.rerun()
# Pulsante per tornare all'inizio
with col3:
if st.button("Return to the beginning"):
# Ripristina lo stato della sessione
st.session_state['step'] = 1
st.session_state['selected_option'] = None
st.session_state['selected_option2'] = None
st.session_state['frontal_file'] = None
st.session_state['lateral_file'] = None
st.session_state['report'] = ""
st.rerun()
# Fase 2: Caricamento file
if st.session_state['step'] == 3:
st.markdown(
f"<h4 style='text-align: justify'><strong>You selected: {st.session_state['selected_option']}. Now, please upload the required files below:</strong></h4>",
unsafe_allow_html=True)
# Carica l'immagine frontale
if "FRO" in st.session_state['selected_option'].split(" ->")[0]:
st.markdown("<h5 style='font-size: 18px;'>Load the Frontal X-ray in DICOM format</h5>", unsafe_allow_html=True)
st.session_state['frontal_file'] = st.file_uploader("", type=["dcm"])
# Carica l'immagine laterale
if "LAT" in st.session_state['selected_option'].split(" ->")[0]:
st.markdown("<h5 style='font-size: 18px;'>Load the Lateral X-ray in DICOM format</h5>", unsafe_allow_html=True)
st.session_state['lateral_file'] = st.file_uploader("", type=["dcm"])
# Inserisci il report clinico
if "REP" in st.session_state['selected_option'].split(" ->")[0]:
st.markdown("<h5 style='font-size: 18px;'>Type the clinical report</h5>", unsafe_allow_html=True)
st.session_state['report'] = st.text_area("", value=st.session_state['report'])
# lasciamo un po' di spazio
st.markdown('<br>', unsafe_allow_html=True)
# Creare colonne per allineare i pulsanti in orizzontale
col1, col2 = st.columns(2)
with col1:
if st.button("Start Generation"):
frontal = None
lateral = None
report = None
# Dato che questo step è velocissimo, prima di procedere mettiamo una finta barra di caricamento di 3 secondi
with st.spinner("Preprocessing the data..."):
time.sleep(3)
# Controllo che i file necessari siano stati caricati
if "FRO" in st.session_state['selected_option'].split(" ->")[0] and not st.session_state['frontal_file']:
st.error("Load the Frontal image.")
elif "LAT" in st.session_state['selected_option'].split(" ->")[0] and not st.session_state['lateral_file']:
st.error("Load the Lateral image.")
elif "REP" in st.session_state['selected_option'].split(" ->")[0] and not st.session_state['report']:
st.error("Type the clinical report.")
else:
st.write(f"Execution of: {st.session_state['selected_option']}")
# Carica l'immagine e avvia l'inferenza
if st.session_state['frontal_file']:
dicom = pydicom.dcmread(st.session_state['frontal_file'])
image = dicom.pixel_array
if dicom.PhotometricInterpretation == 'MONOCHROME1':
image = (2 ** dicom.BitsStored - 1) - image
if dicom.ImagerPixelSpacing != [0.139, 0.139]:
zoom_factor = [0.139 / dicom.ImagerPixelSpacing[0], 0.139 / dicom.ImagerPixelSpacing[1]]
image = zoom(image, zoom_factor)
image = image / (2 ** dicom.BitsStored - 1)
# Se l'immagine non è quadrata, facciamo padding
if image.shape[0] != image.shape[1]:
diff = abs(image.shape[0] - image.shape[1])
pad_size = diff // 2
if image.shape[0] > image.shape[1]:
padded_image = np.pad(image, ((0, 0), (pad_size, pad_size)))
else:
padded_image = np.pad(image, ((pad_size, pad_size), (0, 0)))
# Resizing a 256x256 e a 512x512
zoom_factor = [256 / padded_image.shape[0], 256 / padded_image.shape[1]]
image_256 = zoom(padded_image, zoom_factor)
frontal = image_256
if frontal.dtype != np.uint8:
frontal2 = (255 * (frontal - frontal.min()) / (frontal.max() - frontal.min())).astype(np.uint8)
frontal = torch.tensor(frontal, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
frontal2 = Image.fromarray(frontal2)
st.write("Frontal Image loaded successfully!")
# Mostra l'immagine caricata
st.image(frontal2, caption="Frontal Image Loaded", use_column_width=True)
if st.session_state['lateral_file']:
dicom = pydicom.dcmread(st.session_state['lateral_file'])
image = dicom.pixel_array
if dicom.PhotometricInterpretation == 'MONOCHROME1':
image = (2 ** dicom.BitsStored - 1) - image
if dicom.ImagerPixelSpacing != [0.139, 0.139]:
zoom_factor = [0.139 / dicom.ImagerPixelSpacing[0], 0.139 / dicom.ImagerPixelSpacing[1]]
image = zoom(image, zoom_factor)
image = image / (2 ** dicom.BitsStored - 1)
# Se l'immagine non è quadrata, facciamo padding
if image.shape[0] != image.shape[1]:
diff = abs(image.shape[0] - image.shape[1])
pad_size = diff // 2
if image.shape[0] > image.shape[1]:
padded_image = np.pad(image, ((0, 0), (pad_size, pad_size)))
else:
padded_image = np.pad(image, ((pad_size, pad_size), (0, 0)))
# Resizing a 256x256 e a 512x512
zoom_factor = [256 / padded_image.shape[0], 256 / padded_image.shape[1]]
image_256 = zoom(padded_image, zoom_factor)
lateral = image_256
if lateral.dtype != np.uint8:
lateral2 = (255 * (lateral - lateral.min()) / (lateral.max() - lateral.min())).astype(np.uint8)
lateral = torch.tensor(lateral, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
lateral2 = Image.fromarray(lateral2)
st.write("Lateral Image loaded successfully!")
st.image(lateral2, caption="Lateral Image Loaded", use_column_width=True)
if st.session_state['report']:
report = st.session_state['report']
st.write(f"Loaded Report: {report}")
inputs = []
if "FRO" in st.session_state['selected_option'].split(" ->")[0]:
inputs.append('frontal')
if "LAT" in st.session_state['selected_option'].split(" ->")[0]:
inputs.append('lateral')
if "REP" in st.session_state['selected_option'].split(" ->")[0]:
inputs.append('text')
# Ora vediamo cosa c'è dopo la freccia
outputs = []
if "FRO" in st.session_state['selected_option'].split(" ->")[1]:
outputs.append('frontal')
if "LAT" in st.session_state['selected_option'].split(" ->")[1]:
outputs.append('lateral')
if "REP" in st.session_state['selected_option'].split(" ->")[1]:
outputs.append('text')
# Ultima cosa che va fatta è passare allo step 4, prima di farlo però, tutte le variabili che ci servono
# devono essere salvate nello stato della sessione
st.session_state['inputs'] = inputs
st.session_state['outputs'] = outputs
st.session_state['frontal'] = frontal
st.session_state['lateral'] = lateral
st.session_state['report'] = report
st.session_state['generate'] = True
st.session_state['step'] = 4
st.rerun()
with col2:
if st.button("Return to the beginning"):
# Ripristina lo stato della sessione
st.session_state['step'] = 1
st.session_state['selected_option'] = None
st.session_state['selected_option2'] = None
st.session_state['frontal_file'] = None
st.session_state['lateral_file'] = None
st.session_state['report'] = ""
st.rerun()
if st.session_state['step'] == 4:
# Costruzione del prompt
if st.session_state['generate'] is True:
conditioning = []
for inp in st.session_state['inputs']:
if inp == 'frontal':
cim = inference_tester.net.clip_encode_vision(st.session_state['frontal'], encode_type='encode_vision').to(device)
uim = inference_tester.net.clip_encode_vision(torch.zeros_like(st.session_state['frontal']).to(device),
encode_type='encode_vision').to(device)
conditioning.append(torch.cat([uim, cim]))
elif inp == 'lateral':
cim = inference_tester.net.clip_encode_vision(st.session_state['lateral'], encode_type='encode_vision').to(device)
uim = inference_tester.net.clip_encode_vision(torch.zeros_like(st.session_state['lateral']).to(device),
encode_type='encode_vision').to(device)
conditioning.append(torch.cat([uim, cim]))
elif inp == 'text':
ctx = inference_tester.net.clip_encode_text(1 * [st.session_state['report']], encode_type='encode_text').to(device)
utx = inference_tester.net.clip_encode_text(1 * [""], encode_type='encode_text').to(device)
conditioning.append(torch.cat([utx, ctx]))
# Costruzione delle shapes
shapes = []
for out in st.session_state['outputs']:
if out == 'frontal' or out == 'lateral':
shape = [1, 4, 256 // 8, 256 // 8]
shapes.append(shape)
elif out == 'text':
shape = [1, 768]
shapes.append(shape)
progress_bar = st.progress(0)
# Inferenza
z, _ = inference_tester.sampler.sample(
steps=50,
shape=shapes,
condition=conditioning,
unconditional_guidance_scale=7.5,
xtype=st.session_state['outputs'],
condition_types=st.session_state['inputs'],
eta=1,
verbose=False,
mix_weight={'lateral': 1, 'text': 1, 'frontal': 1},
progress_bar=progress_bar)
# Decoder e visualizzazione dei risultati
output_cols = st.columns(len(st.session_state['outputs']))
# Definire due colonne per le immagini
col1, col2 = st.columns(2)
# Iterare sugli output e assegnare le immagini alle colonne corrispondenti
for i, out in enumerate(st.session_state['outputs']):
if out == 'frontal':
x = inference_tester.net.autokl_decode(z[i])
x = torch.clamp((x[0] + 1.0) / 2.0, min=0.0, max=1.0)
im = x[0].cpu().numpy()
with col1: # Mostrare la frontal image nella prima colonna
st.image(im, caption="Generated Frontal Image")
elif out == 'lateral':
x = inference_tester.net.autokl_decode(z[i])
x = torch.clamp((x[0] + 1.0) / 2.0, min=0.0, max=1.0)
im = x[0].cpu().numpy()
with col2: # Mostrare la lateral image nella seconda colonna
st.image(im, caption="Generated Lateral Image")
elif out == 'text':
x = inference_tester.net.optimus_decode(z[i], max_length=100)
x = [a.tolist() for a in x]
rec_text = [inference_tester.net.optimus.tokenizer_decoder.decode(a) for a in x]
rec_text = rec_text[0].replace('<BOS>', '').replace('<EOS>', '')
st.write(f"Generated Report: {rec_text}")
st.write("Generation completed successfully!")
st.session_state['generate'] = False
if st.button("Return to the beginning"):
# Ripristina lo stato della sessione
st.session_state['generate'] = False
st.session_state['step'] = 1
st.session_state['selected_option'] = None
st.session_state['frontal_file'] = None
st.session_state['lateral_file'] = None
st.session_state['report'] = ""
st.session_state['inputs'] = None
st.session_state['outputs'] = None
st.session_state['frontal'] = None
st.session_state['lateral'] = None
st.session_state['report'] = ""
st.rerun()
if st.session_state['step'] == 5:
st.markdown(
f"<h4 style='text-align: justify'><strong>You selected: {st.session_state['selected_option']}</strong></h4>",
unsafe_allow_html=True)
inputs = []
if "FRO" in st.session_state['selected_option'].split(" ->")[0]:
inputs.append('FRO')
if "LAT" in st.session_state['selected_option'].split(" ->")[0]:
inputs.append('LAT')
if "REP" in st.session_state['selected_option'].split(" ->")[0]:
inputs.append('REP')
outputs = []
if "FRO" in st.session_state['selected_option'].split(" ->")[1]:
outputs.append('FRO')
if "LAT" in st.session_state['selected_option'].split(" ->")[1]:
outputs.append('LAT')
if "REP" in st.session_state['selected_option'].split(" ->")[1]:
outputs.append('REP')
esempio = esempi[st.session_state['selected_option']]
# Mostra i file associati all'esempio
st.markdown(
"<h3 style='text-align: justify'><strong>INPUTS</strong></h3>",
unsafe_allow_html=True)
# Colonne per gli INPUTS
input_cols = st.columns(len(inputs))
for idx, inp in enumerate(inputs):
with input_cols[idx]:
if inp == 'FRO':
path = "./DEMO/ESEMPI/" + esempio['FRO']
print(path)
if path.endswith(".tiff"):
im = tifffile.imread(path)
im = np.clip(im, 0, 1)
elif path.endswith(".png"):
im = Image.open(path)
st.image(im, caption="Frontal Image")
elif inp == 'LAT':
path = "./DEMO/ESEMPI/" + esempio['LAT']
if path.endswith(".tiff"):
im = tifffile.imread(path)
im = np.clip(im, 0, 1)
elif path.endswith(".png"):
im = Image.open(path)
st.image(im, caption="Lateral Image")
elif inp == 'REP':
st.write(f"Report: {esempio['REP']}")
st.markdown(
"<h3 style='text-align: justify'><strong>OUTPUTS</strong></h3>",
unsafe_allow_html=True)
# Colonne per gli OUTPUTS
output_cols = st.columns(len(outputs))
for idx, out in enumerate(outputs):
with output_cols[idx]:
if out == 'FRO':
path = "./DEMO/ESEMPI/" + esempio['FRO']
if path.endswith(".tiff"):
im = tifffile.imread(path)
# facciamo clamp tra 0 e 1
im = np.clip(im, 0, 1)
elif path.endswith(".png"):
im = Image.open(path)
st.image(im, caption="Frontal Image")
elif out == 'LAT':
path = "./DEMO/ESEMPI/" + esempio['LAT']
if path.endswith(".tiff"):
im = tifffile.imread(path)
# facciamo clamp tra 0 e 1
im = np.clip(im, 0, 1)
elif path.endswith(".png"):
im = Image.open(path)
st.image(im, caption="Lateral Image")
elif out == 'REP':
st.write(f"Report: {esempio['REP']}")
# Pulsante per tornare all'inizio
if st.button("Return to the beginning"):
# Ripristina lo stato della sessione
st.session_state['step'] = 1
st.session_state['selected_option'] = None
st.session_state['selected_option2'] = None
st.session_state['frontal_file'] = None
st.session_state['lateral_file'] = None
st.session_state['report'] = ""
st.rerun()