Update app.py

app.py

@@ -1,20 +1,472 @@
 import streamlit as st
-from transformers import pipeline
+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
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-st.title("Hot Dog? Or Not?")
+# 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()
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
 
-if file_name is not None:
+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:
+    st.session_state['inference_tester'] = 1
+
+# 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 work 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)
+
+    # Immagine con didascalia migliorata e con dimensione della caption aumentata
+    image_path = "./DEMO/Loghi/model_final.png"  # Sostituisci con il percorso della tua immagine
+    st.image(image_path, caption='', 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)
+
+    # Bottone con testo "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)
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
+    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:
+    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']}")
 
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+    # 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()