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README.md

@@ -1,13 +1 @@
----
-title: NSCLC Classification
-emoji: 😻
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ViTLungMiNi

app.py

@@ -0,0 +1,625 @@
+import numpy as np
+from transformers import BeitImageProcessor, BeitForImageClassification
+from PIL import Image
+import PIL.Image as Image
+import csv
+
+from streamlit_echarts import st_echarts
+from st_on_hover_tabs import on_hover_tabs
+import streamlit as st
+
+st.set_page_config(layout="wide")
+
+import warnings
+warnings.filterwarnings('ignore')
+from torchvision import transforms
+from datasets import load_dataset
+from pytorch_grad_cam import run_dff_on_image, GradCAM
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+from pytorch_grad_cam.utils.image import show_cam_on_image
+import cv2
+import torch
+from torch import nn
+from typing import List, Callable, Optional
+import os
+import pandas as pd
+import pydicom
+
+labels = ["adenocarcinoma","large.cell","normal","squamous.cell"]
+model_name_or_path = 'alicelouis/BeiT_NSCLC_lr2e-5'
+st.markdown('''
+<style>
+    section[data-testid='stSidebar'] {
+        background-color: #111;
+        min-width: unset !important;
+        width: unset !important;
+        flex-shrink: unset !important;
+    }
+
+    button[kind="header"] {
+        background-color: transparent;
+        color: rgb(180, 167, 141);
+    }
+
+    @media (hover) {
+        /* header element to be removed */
+        header["data"-testid="stHeader"] {
+            display: none;
+        }
+
+        /* The navigation menu specs and size */
+        section[data-testid='stSidebar'] > div {
+            height: 100%;
+            width: 95px;
+            position: relative;
+            z-index: 1;
+            top: 0;
+            left: 0;
+            background-color: #111;
+            overflow-x: hidden;
+            transition: 0.5s ease;
+            padding-top: 60px;
+            white-space: nowrap;
+        }
+
+        /* The navigation menu open and close on hover and size */
+        /* section[data-testid='stSidebar'] > div {
+        height: 100%;
+        width: 75px; /* Put some width to hover on. */
+        /* } 
+
+        /* ON HOVER */
+        section[data-testid='stSidebar'] > div:hover{
+        width: 300px;
+        }
+
+        /* The button on the streamlit navigation menu - hidden */
+        button[kind="header"] {
+            display: none;
+        }
+    }
+
+    @media (max-width: 272px) {
+        section["data"-testid='stSidebar'] > div {
+            width: 15rem;
+        }/.
+    }
+</style>
+''', unsafe_allow_html=True)
+
+@st.cache_resource(show_spinner=False,ttl=1800,max_entries=2)
+def FeatureExtractor(model_name_or_path):
+    feature_extractor = BeitImageProcessor.from_pretrained(model_name_or_path)
+    return feature_extractor
+
+
+@st.cache_resource(show_spinner=False,ttl=1800,max_entries=2)
+def LoadModel(model_name_or_path):
+    model = BeitForImageClassification.from_pretrained(
+        model_name_or_path,
+        num_labels=len(labels),
+        id2label={int(i): c for i, c in enumerate(labels)},
+        label2id={c: int(i) for i, c in enumerate(labels)},
+        ignore_mismatched_sizes=True)
+    return model
+
+
+# Model wrapper to return a tensor
+class HuggingfaceToTensorModelWrapper(torch.nn.Module):
+    def __init__(self, model):
+        super(HuggingfaceToTensorModelWrapper, self).__init__()
+        self.model = model
+
+    def forward(self, x):
+        return self.model(x).logits
+
+# """ Translate the category name to the category index.
+#     Some models aren't trained on Imagenet but on even larger "data"sets,
+#     so we can't just assume that 761 will always be remote-control.
+
+# """
+def category_name_to_index(model, category_name):
+    name_to_index = dict((v, k) for k, v in model.config.id2label.items())
+    return name_to_index[category_name]
+    
+# """ Helper function to run GradCAM on an image and create a visualization.
+#     (note to myself: this is probably useful enough to move into the package)
+#     If several targets are passed in targets_for_gradcam,
+#     e.g different categories,
+#     a visualization for each of them will be created.
+    
+# """
+def print_top_categories(model, img_tensor, top_k=5):
+    feature_extractor = FeatureExtractor(model_name_or_path)
+    inputs = feature_extractor(images=img_tensor, return_tensors="pt")
+    outputs = model(**inputs)
+    logits  = outputs.logits
+    indices = logits.cpu()[0, :].detach().numpy().argsort()[-top_k :][::-1]
+    probabilities = nn.functional.softmax(logits, dim=-1)
+    topK = dict()
+    for i in indices:
+        topK[model.config.id2label[i]] = probabilities[0][i].item()*100
+    return topK
+
+def reshape_transform_vit_huggingface(x):
+    activations = x[:, 1:, :]
+    
+    activations = activations.view(activations.shape[0],
+                                14, 14, activations.shape[2])
+    
+    activations = activations.transpose(2, 3).transpose(1, 2)
+    
+    return activations
+
+
+def count_system():
+    count_system = []
+    with open('count_class.txt', 'r') as f:
+        for line in f:
+            if line.strip() == '0':
+                continue
+            else:
+                count_system.append(line.strip())
+    f.close()
+    if len(count_system) != 0:
+        return int(len(count_system))
+    elif len(count_system) == 0:
+        return int(0)
+
+
+def count_class(count_classes):
+    a = 0
+    b = 0
+    c = 0
+    d = 0
+    for i in range(len(count_classes)):
+        if count_classes[i] == "Adeno":
+            a += 1
+        elif count_classes[i] == "Normal":
+            b += 1
+        elif count_classes[i] == "Large":
+            c += 1
+        elif count_classes[i] == "Squamous":
+            d += 1 
+    count_classes = []
+    count_classes.append(str(a))
+    count_classes.append(str(b))
+    count_classes.append(str(c))
+    count_classes.append(str(d))
+    with open("count_class.txt", "w") as f:
+        for count in count_classes:
+            f.write(count + "\n")
+
+# Define CSS styling for centering
+centered_style = """
+        display: flex;
+        justify-content: center;
+"""
+
+st.markdown(
+    """
+<div style='border: 2px solid green; border-radius: 5px; padding: 10px; background-color: white;'>
+    <h1 style='text-align: center; color: green;'>
+    🏥 Lung Cancer Classification with Vision Transformer : จำแนกมะเร็งปอด 🫁
+    </h1>
+</div>
+    """, unsafe_allow_html=True)
+
+with open("assets/css/style.css") as f:
+    st.markdown(f"<style> {f.read()} </style>",unsafe_allow_html=True)
+with open("assets/webfonts/font.txt") as f:
+    st.markdown(f.read(),unsafe_allow_html=True)
+# end def
+
+with st.sidebar:
+    tabs = on_hover_tabs(tabName=['Home','Upload', 'Analytics', 'More Information', 'Reset'], 
+    iconName=['home','upload', 'analytics', 'informations', 'refresh'], 
+    styles={'navtab': {'background-color': '#111', 'color': '#818181', 'font-size': '18px', 
+                    'transition': '.3s', 'white-space': 'nowrap', 'text-transform': 'uppercase'}, 
+                    'tabOptionsStyle': 
+                    {':hover :hover': {'color': 'red', 'cursor': 'pointer'}}, 'iconStyle': 
+                    {'position': 'fixed', 'left': '7.5px', 'text-align': 'left'}, 'tabStyle': 
+                    {'list-style-type': 'none', 'margin-bottom': '30px', 'padding-left': '30px'}}, 
+                    key="1",default_choice=0)
+    st.markdown(
+    """
+        <div style='border: 2px solid green; padding: 10px; white; margin-top: 5px; margin-buttom: 5px; margin-right: 20px; bottom: 50;'>
+            <h1 style='text-align: center; color: green; font-size: 100%'> ได้รับทุนสนับสนุน 2,000 บาท </h1>
+            <h1 style='text-align: center; color: green; font-size: 100%'> National Software Contest ครั้งที่ 25 </h1>
+            <h1 style='text-align: center; color: green; font-size: 100%'> ประจำปีงบประมาณ 2566 </h1>
+        </div>
+    """, unsafe_allow_html=True)
+data_base = []
+if tabs == 'Home':
+    st.image('How_to_use.png',use_column_width=True)
+elif tabs == 'Upload': #and count_system () != 1:
+    uploaded_file = st.file_uploader("อัปโหลดไฟล์ภาพ", type=["jpg", "jpeg", "png", "dcm"], accept_multiple_files=True)
+    name_of_files = []
+    name_of_files_new = []
+    for n in uploaded_file:
+        file_name = n.name
+        name_of_files.append(file_name)
+    with open("save_name.txt", "w") as f:
+        for name in name_of_files:
+            f.write(name + "\n")
+    for j in range(len(name_of_files)):
+        if name_of_files[j].endswith('.dcm'):
+            name_of_files_new.append(name_of_files[j][:-4] + '.png')
+        else:
+            name_of_files_new.append(name_of_files[j])  
+    for i in range(len(uploaded_file)):   
+        if name_of_files[i].endswith('.dcm'):
+            ds = pydicom.dcmread(uploaded_file[i])
+            new_image = ds.pixel_array.astype(float)
+            scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0
+            scaled_image = np.uint8(scaled_image)
+            gray_scale = Image.fromarray(scaled_image)
+            final_image = gray_scale.convert('RGB')
+            final_image.resize((200,200))
+            final_image.save(r'.\dcm_png\{}.png'.format(name_of_files[i]))                  
+        feature_extractor = FeatureExtractor(model_name_or_path)
+        model = LoadModel(model_name_or_path)
+        if name_of_files[i].endswith('.dcm'):
+            img = Image.open(r'.\dcm_png\{}.png'.format(name_of_files[i]))
+        else:
+            img = Image.open(uploaded_file[i])
+        img_out = img.resize((224,224))
+        img_out = np.array(img_out)
+        # โหลดโมเดลที่เซฟ
+        image = img.resize((224,224))
+        img_tensor = transforms.ToTensor()(image)
+        def run_grad_cam_on_image(model: torch.nn.Module,
+                        target_layer: torch.nn.Module,
+                        targets_for_gradcam: List[Callable],
+                        reshape_transform: Optional[Callable],
+                        input_tensor: torch.nn.Module=img_tensor,
+                        input_image: Image=image,
+                        method: Callable=GradCAM):
+            with method(model=HuggingfaceToTensorModelWrapper(model),
+                target_layers=[target_layer],
+                reshape_transform=reshape_transform) as cam:
+        # Replicate the tensor for each of the categories we want to create Grad-CAM for:
+                repeated_tensor = input_tensor[None, :].repeat(len(targets_for_gradcam), 1, 1, 1)
+
+                batch_results = cam(input_tensor=repeated_tensor,
+                                    targets=targets_for_gradcam)
+                results = []
+                for grayscale_cam in batch_results:
+                    visualization = show_cam_on_image(np.float32(input_image)/255,
+                                                    grayscale_cam,
+                                                    use_rgb=True)
+                    # Make it weight less in the notebook:
+                    visualization = cv2.resize(visualization,
+                                            (visualization.shape[1]//2, visualization.shape[0]//2))
+                    results.append(visualization)
+                return np.hstack(results)            
+        inputs = feature_extractor(images=image, return_tensors="pt")
+        targets_for_gradcam = [ClassifierOutputTarget(category_name_to_index(model, "adenocarcinoma")),
+                    ClassifierOutputTarget(category_name_to_index(model, "large.cell")),
+                    ClassifierOutputTarget(category_name_to_index(model, "normal")),
+                    ClassifierOutputTarget(category_name_to_index(model, "squamous.cell"))
+                    ]
+        target_layer_dff = model.beit.layernorm
+        target_layer_gradcam = model.beit.encoder.layer[-2].output
+        image_resized = image.resize((224, 224))
+        tensor_resized = transforms.ToTensor()(image_resized)
+        outputs = model(**inputs)
+        logits = outputs.logits
+        # model predicts one of the 4 classes
+        predicted_class_idx = logits.argmax(-1).item()
+        className = labels[predicted_class_idx]
+        # display the images on streamlit
+        dff_image = Image.fromarray(run_dff_on_image(model=model,
+                                    target_layer=target_layer_dff,
+                                    classifier=model.classifier,
+                                    img_pil=image_resized,
+                                    img_tensor=tensor_resized,
+                                    reshape_transform=reshape_transform_vit_huggingface,
+                                    n_components=4,
+                                    top_k=4))
+        # dff_image.save(r".\save_images\dff_image.png")
+        # gradcam_image.save(r".\save_images\gradcam_image.png")
+        topK = print_top_categories(model, tensor_resized)
+        df = pd.DataFrame.from_dict(topK, orient='index')
+        list_to_be_sorted= []
+        for x, y in topK.items():
+            dic = dict()
+            dic["value"] = y
+            dic["name"] = x
+            list_to_be_sorted.append(dic)
+            data_base.append(y)
+        if list_to_be_sorted[0]['name'] == "adenocarcinoma":
+            dff_image.save(r".\Adenocarcinoma\{}".format(name_of_files_new[i]))
+            image_path = name_of_files_new[i]
+            with Image.open(r".\Adenocarcinoma\{}".format(image_path)) as image:
+                width, height = image.size
+                new_width = 2 * width // 3
+                cropped_image = image.crop((0, 0, new_width, height))
+                cropped_image.save(r".\Adenocarcinoma\{}".format(image_path))
+        elif list_to_be_sorted[0]['name'] == "large.cell":
+            dff_image.save(r".\Large cell carcinoma\{}".format(name_of_files_new[i]))
+            image_path = name_of_files_new[i]
+            with Image.open(r".\Large cell carcinoma\{}".format(image_path)) as image:
+                width, height = image.size
+                new_width = 2 * width // 3
+                cropped_image = image.crop((0, 0, new_width, height))
+                cropped_image.save(r".\Large cell carcinoma\{}".format(image_path))
+            #dff_image.save(r".\Large cell carcinoma\{}".format(name_of_files_new[i]))
+        elif list_to_be_sorted[0]['name'] == "normal":
+            dff_image.save(r".\Normal\{}".format(name_of_files_new[i]))
+            image_path = name_of_files_new[i]
+            with Image.open(r".\Normal\{}".format(image_path)) as image:
+                width, height = image.size
+                new_width = 2 * width // 3
+                cropped_image = image.crop((0, 0, new_width, height))
+                cropped_image.save(r".\Normal\{}".format(image_path))
+            #dff_image.save(r".\Normal\{}".format(name_of_files_new[i]))
+        elif list_to_be_sorted[0]['name'] == "squamous.cell":
+            dff_image.save(r".\Squamous cell carcinoma\{}".format(name_of_files_new[i]))
+            image_path = name_of_files_new[i]
+            with Image.open(r".\Squamous cell carcinoma\{}".format(image_path)) as image:
+                width, height = image.size
+                new_width = 2 * width // 3
+                cropped_image = image.crop((0, 0, new_width, height))
+                cropped_image.save(r".\Squamous cell carcinoma\{}".format(image_path))
+            #dff_image.save(r".\Squamous cell carcinoma\{}".format(name_of_files_new[i]))
+        # st.image(dff_image, use_column_width=True)
+        # st.image(gradcam_image, use_column_width=True)
+        st.balloons()
+
+    # Create a container for the two columns
+    container = st.container()
+    # Create two columns within the container
+    col1, col2 = container.columns(2)
+    col3, col4 = container.columns(2)
+    col5, col6 = container.columns(2)
+    # Add the first subheader to the first column
+    count_classes = [] #Adenocarcinoma, Normal, Large cell carcinoma, Squamous cell carcinoma
+    with col1:
+        st.markdown("<h2 style='text-align: center; border: 2px solid #5370c6; border-radius: 5px; padding: 15px; background-color: white; color: black;' > Adenocarcinoma </h2>".format(centered_style), unsafe_allow_html=True)
+        # Add the second subheader to the second column
+        folder_path = r".\Adenocarcinoma"
+        image_files = [f for f in os.listdir(folder_path) if f.endswith('.png') or f.endswith('.jpg')]
+        # Display the images in a loop
+        for i in range(0, len(image_files), 2):
+            col7, col8 = st.columns([1, 1])
+            with col7:
+                if i < len(image_files):
+                    image1 = Image.open(os.path.join(folder_path, image_files[i]))
+                    st.image(image1, use_column_width=True)
+                    st.write(f"<p style='text-align: center; color: black; border: 2px solid white; border-radius: 10px; padding: 10px; background-color: #5370c6; font-size: 32px;'>{image_files[i]}</p>", unsafe_allow_html=True)
+                    count_classes.append("Adeno")
+            with col8:
+                if i+1 < len(image_files):
+                    image2 = Image.open(os.path.join(folder_path, image_files[i+1]))
+                    st.image(image2, use_column_width=True)
+                    st.write(f"<p style='text-align: center; color: black; border: 2px solid white; border-radius: 10px; padding: 10px; background-color: #5370c6; font-size: 32px;'>{image_files[i+1]}</p>", unsafe_allow_html=True) 
+                    count_classes.append("Adeno")
+    with col2:
+        st.markdown("<h2 style='text-align: center; border: 2px solid green; border-radius: 5px; padding: 15px; background-color: white; color: black;' > Normal </h2>".format(centered_style), unsafe_allow_html=True)
+        folder_path = r".\Normal"
+        image_files = [f for f in os.listdir(folder_path) if f.endswith('.png') or f.endswith('.jpg')]
+        # Display the images in a loop
+        for i in range(0, len(image_files), 2):
+            col9, col10 = st.columns([1, 1])
+            with col9:
+                if i < len(image_files):
+                    image1 = Image.open(os.path.join(folder_path, image_files[i]))
+                    st.image(image1, use_column_width=True)
+                    st.write(f"<p style='text-align: center; color: black; border: 2px solid white; border-radius: 10px; padding: 10px; background-color: green; font-size: 32px;'>{image_files[i]}</p>", unsafe_allow_html=True)
+                    count_classes.append("Normal")
+            with col10:
+                if i+1 < len(image_files):
+                    image2 = Image.open(os.path.join(folder_path, image_files[i+1]))
+                    st.image(image2, use_column_width=True) 
+                    st.write(f"<p style='text-align: center; color: black; border: 2px solid white; border-radius: 10px; padding: 10px; background-color: green; font-size: 32px;'>{image_files[i+1]}</p>", unsafe_allow_html=True)   
+                    count_classes.append("Normal")
+    with col3:
+        st.markdown("")
+    with col4:
+        st.markdown("")
+
+    with col5:
+        st.markdown("<h2 style='text-align: center; border: 2px solid orange; border-radius: 5px; padding: 15px; background-color: white; color: black;' > Large cell carcinoma </h2>".format(centered_style), unsafe_allow_html=True)
+        folder_path = r".\Large cell carcinoma"
+        image_files = [f for f in os.listdir(folder_path) if f.endswith('.png') or f.endswith('.jpg')]
+        # Display the images in a loop
+        for i in range(0, len(image_files), 2):
+            col11, col12 = st.columns([1, 1])
+            with col11:
+                if i < len(image_files):
+                    image1 = Image.open(os.path.join(folder_path, image_files[i]))
+                    st.image(image1, use_column_width=True)
+                    st.write(f"<p style='text-align: center; color: black; border: 2px solid white; border-radius: 10px; padding: 10px; background-color: orange; font-size: 32px;'>{image_files[i]}</p>", unsafe_allow_html=True)   
+                    count_classes.append("Large")
+            with col12:
+                if i+1 < len(image_files):
+                    image2 = Image.open(os.path.join(folder_path, image_files[i+1]))
+                    st.image(image2, use_column_width=True)
+                    st.write(f"<p style='text-align: center; color: black; border: 2px solid white; border-radius: 10px; padding: 10px; background-color: orange; font-size: 32px;'>{image_files[i+1]}</p>", unsafe_allow_html=True)   
+                    count_classes.append("Large")
+    with col6:
+        st.markdown("<h2 style='text-align: center; border: 2px solid #f16565; border-radius: 5px; padding: 15px; background-color: white; color: black;' >  Squamous cell carcinoma </h2>".format(centered_style), unsafe_allow_html=True)
+        folder_path = r".\Squamous cell carcinoma"
+        image_files = [f for f in os.listdir(folder_path) if f.endswith('.png') or f.endswith('.jpg')]
+        # Display the images in a loop
+        for i in range(0, len(image_files), 2):
+            col13, col14 = st.columns([1, 1])
+            with col13:
+                if i < len(image_files):
+                    image1 = Image.open(os.path.join(folder_path, image_files[i]))
+                    st.image(image1, use_column_width=True)
+                    st.write(f"<p style='text-align: center; color: black; border: 2px solid white; border-radius: 10px; padding: 10px; background-color: #f16565; font-size: 32px;'>{image_files[i]}</p>", unsafe_allow_html=True)   
+                    count_classes.append("Squamous")
+            with col14:
+                if i+1 < len(image_files):
+                    image2 = Image.open(os.path.join(folder_path, image_files[i+1]))
+                    st.image(image2, use_column_width=True)
+                    st.write(f"<p style='text-align: center; color: black; border: 2px solid white; border-radius: 10px; padding: 10px; background-color: #f16565; font-size: 32px;'>{image_files[i+1]}</p>", unsafe_allow_html=True)   
+                    count_classes.append("Squamous")
+    count_class(count_classes)
+
+elif tabs == 'Analytics' and count_system() > 0:
+    data_base = []
+    data_base_max = []
+    #max_value = max(data_base)
+    #max_index = data_base.index(max_value)
+    with open('count_class.txt', 'r') as f:
+        for line in f:
+            data_base.append(line.strip()) 
+            data_base_max.append(int(line.strip()))
+    max_value = max(data_base_max)  # Find the maximum value in the list
+    max_index = data_base_max.index(max_value)
+    max_indices = [i for i, value in enumerate(data_base_max) if value == max_value]
+    if len(max_indices) > 1:
+        max_index = 4
+    option = {
+    "tooltip": {
+    "trigger": 'axis',
+    "axisPointer": {
+    # Use axis to trigger tooltip
+    "type": 'shadow' # 'shadow' as default; can also be 'line' or 'shadow'
+        }
+    },
+    "legend": {},
+    "grid": {
+    "left": '3%',
+    "right": '4%',
+    "bottom": '3%',
+    "containLabel": True
+    },
+        "xAxis": {
+        "type": 'value'
+    },
+        "yAxis": {
+        "type": 'category',
+        "data": ['Results']
+        },
+        "series": [
+    {
+        "name": 'Adenocarcinoma',
+        "type": 'bar',
+        "stack": 'total',
+        "label": {
+        "show": True
+    },
+        "emphasis": {
+            "focus": 'series'
+        },
+            "data": [data_base[0]]
+        },
+            {
+            "name": 'Normal',
+            "type": 'bar',
+            "stack": 'total',
+            "label": {
+            "show": True
+        },
+        "emphasis": {
+            "focus": 'series'
+        },
+        "data": [data_base[1]]
+        },
+        {
+        "name": 'Large.Cell',
+        "type": 'bar',
+        "stack": 'total',
+        "label": {
+            "show": True
+        },
+        "emphasis": {
+            "focus": 'series'
+        },
+        "data": [data_base[2]]
+        },
+        {
+        "name": 'Squamous.Cell',
+        "type": 'bar',
+        "stack": 'total',
+        "label": {
+            "show": True
+        },
+        "emphasis": {
+        "focus": 'series'
+        },
+        "data": [data_base[3]]
+        },
+    ]
+}
+    st_echarts(options=option)
+    if max_index == 0:
+        st.markdown("<h2 style='text-align: center; border: 2px solid #5370c6; border-radius: 5px; padding: 15px; background-color: white; color: black;' > Adenocarcinoma </h2>".format(centered_style), unsafe_allow_html=True)
+    elif max_index == 1:
+        st.markdown("<h2 style='text-align: center; border: 2px solid green; border-radius: 5px; padding: 15px; background-color: white; color: black;' > Normal </h2>".format(centered_style), unsafe_allow_html=True)
+    elif max_index == 2:
+        st.markdown("<h2 style='text-align: center; border: 2px solid orange; border-radius: 5px; padding: 15px; background-color: white; color: black;' > Large cell carcinoma </h2>".format(centered_style), unsafe_allow_html=True)
+    elif max_index == 3:   
+        st.markdown("<h2 style='text-align: center; border: 2px solid #f16565; border-radius: 5px; padding: 15px; background-color: white; color: black;' >  Squamous cell carcinoma </h2>".format(centered_style), unsafe_allow_html=True)
+
+elif tabs == 'Analytics' and count_system() == 0:
+        st.markdown(
+                """
+            <div style='border: 2px solid red; border-radius: 5px; padding: 5px; background-color: white;'>
+                <h3 style='text-align: center; color: red; font-size: 180%'> 🖼️ Image Analytics Not Detected ❌ </h3>
+            </div>
+                """, unsafe_allow_html=True)
+        
+elif tabs == 'More Information':
+        st.markdown(
+                    """
+                <div style='border: 2px dashed blue; border-radius: 5px; padding: 5px; background-color: white;'>
+                    <h3 style='text-align: center; color: black; font-size: 180%'> 💻 Organizers 🖱️ </h3>
+                </div>
+                    """, unsafe_allow_html=True)          
+        st.markdown(
+            """
+                <div style="display:flex; justify-content:center; align-items:center;">
+                <img src="https://drive.google.com/uc?export=view&id=1xupbYYXQZzjwMQiVGwT636oCXMga2ETF" style="width:300px; height:200px; margin: 10px;">
+                <img src="https://drive.google.com/uc?export=view&id=1evDy9sDtJ1T_WVR1bUnfyZkeSMjT9pfr" style="width:300px; height:200px; margin: 10px;">
+                <img src="https://drive.google.com/uc?export=view&id=1Sebh31aX8vdNe8P7oyBL714J_0qA5WYt" style="width:300px; height:200px; margin: 10px;">
+                </div>
+            """, unsafe_allow_html=True)
+        st.markdown(
+            """
+                <div style="display:flex; justify-content:center; align-items:center;">
+                <h3 style="width:300px; height:200px; margin: 10px; font-size: 50% text-align: center;' "> 👑 Santipab Tongchan\nCall : 090-2471512 \n "[email protected]" </h3>
+                <h3 style="width:300px; height:200px; margin: 10px; font-size: 50% text-align: center;' "> Phakkhaphon Artburai\nCall : 091-0197314 \n "[email protected]" </h3>
+                <h3 style="width:300px; height:200px; margin: 10px; font-size: 50% text-align: center;' "> Natthawee Naewkumpol\nCall : 061-9487722 \n "[email protected]" </h3>
+                </div>
+            """, unsafe_allow_html=True)
+        st.markdown(
+                    """
+                <div style='border: 2px solid orange; border-radius: 5px; padding: 5px; background-color: white;'>
+                    <h3 style='text-align: center; color: blue; font-size: 200%'> Princess Chulabhorn Science High School Buriram </h3>
+                </div>
+                    """, unsafe_allow_html=True)    
+
+elif tabs == 'Reset':
+    def clear_folder(folder_name):
+        # Check if the folder exists
+        if not os.path.exists(folder_name):
+            print(f"{folder_name} does not exist.")
+            return
+        # Get a list of all files in the folder and its subdirectories
+        files = []
+        for dirpath, dirnames, filenames in os.walk(folder_name):
+            for filename in filenames:
+                files.append(os.path.join(dirpath, filename))
+
+        # Delete all files in the list
+        for file in files:
+            os.remove(file)
+    clear_folder('Adenocarcinoma')
+    clear_folder('Large cell carcinoma')
+    clear_folder('Normal')
+    clear_folder('Squamous cell carcinoma')
+    clear_folder('dcm_png')
+    #clear data in count_class
+    with open('count_class.txt', 'w') as file:
+        file.write('')
+    st.markdown(
+            """
+        <div style='border: 2px solid #00FFFF; border-radius: 5px; padding: 5px; background-color: white;'>
+            <h3 style='text-align: center; color: blue; font-size: 180%'> 🔃 The information has been cleared. ✅ </h3>
+        </div>
+            """, unsafe_allow_html=True)

assets/css/style.css

@@ -0,0 +1,37 @@
+*{
+    font-family: 'Kanit', sans-serif !important;
+}
+
+
+
+
+.stTextArea{
+    height: auto;
+    
+}
+
+div[class="css-keje6w e1tzin5v2"]{
+    column-gap: 100px;
+}
+
+h2{
+    color: #5ba56e;
+}
+
+h3{
+    color:#007a7a;
+}
+
+label[class="css-16huue1 effi0qh3"]{
+    
+    font-size: 16px;
+}
+
+p{
+    color:#78701d;
+    font-size: 16px;
+}
+
+textarea{
+    color:#007a7a;
+}

assets/webfonts/font.txt

@@ -0,0 +1,3 @@
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=Kanit:wght@200&display=swap" rel="stylesheet">

className.txt

@@ -0,0 +1 @@
+adenocarcinoma

css/style.css

@@ -0,0 +1,93 @@
+section[data-testid='stSidebar'] {
+    background-color: #111;
+    min-width:unset !important;
+    width: unset !important;
+    flex-shrink: unset !important;
+    
+}
+
+button[kind="header"] {
+    background-color: transparent;
+    color:rgb(180, 167, 141)
+}
+
+@media(hover){
+    /* header element to be removed */
+    header[data-testid="stHeader"] {
+        display:none;
+    }
+
+    /* The navigation menu specs and size */
+    section[data-testid='stSidebar'] > div {
+        height: 100%;
+        width: 95px;
+        position: relative;
+        z-index: 1;
+        top: 0;
+        left: 0;
+        background-color: #111;
+        overflow-x: hidden;
+        transition: 0.5s ease;
+        padding-top: 60px;
+        white-space: nowrap;
+    }
+
+    /* The navigation menu open and close on hover and size */
+    /* section[data-testid='stSidebar'] > div {
+    height: 100%;
+    width: 75px; /* Put some width to hover on. */
+    /* } 
+
+    /* ON HOVER */
+    section[data-testid='stSidebar'] > div:hover{
+    width: 300px;
+    }
+
+    /* The button on the streamlit navigation menu - hidden */
+    button[kind="header"] {
+        display: none;
+    }
+}
+     
+@media(max-width: 272px){
+    
+    section[data-testid='stSidebar'] > div {
+        width:15rem;
+    }
+}
+ 
+*{
+    font-family: 'Kanit', sans-serif !important;
+}
+
+
+.stTextArea{
+    height: auto;
+    
+}
+
+div[class="css-keje6w e1tzin5v2"]{
+    column-gap: 100px;
+}
+
+h2{
+    color: #5ba56e;
+}
+
+h3{
+    color:#007a7a;
+}
+
+label[class="css-16huue1 effi0qh3"]{
+    
+    font-size: 16px;
+}
+
+p{
+    color:#78701d;
+    font-size: 16px;
+}
+
+textarea{
+    color:#007a7a;
+}

packages.txt

@@ -0,0 +1 @@
+libgl1

requirements.txt

@@ -0,0 +1,10 @@
+datasets
+huggingface-hub
+streamlit
+torch
+torchaudio
+torchvision
+transformers
+grad-cam
+streamlit_echarts
+streamlit-on-Hover-tabs

save_name.txt

@@ -0,0 +1,24 @@
+tra_ade_0.png
+tra_ade_1.png
+tra_ade_2.png
+tra_ade_3.png
+tra_ade_4.png
+tra_ade_5.png
+tra_ade_6.png
+tra_ade_7.png
+tra_ade_8.png
+tra_ade_9.png
+tra_ade_10.png
+tra_ade_11.png
+tra_ade_12.png
+tra_ade_13.png
+tra_ade_14.png
+tra_ade_15.png
+tra_ade_16.png
+tra_ade_17.png
+tra_ade_18.png
+tra_ade_19.png
+tra_ade_20.png
+tra_ade_21.png
+tra_ade_23.png
+tra_ade_25.png

system.txt

@@ -0,0 +1 @@
+{'adenocarcinoma': 99.79050159454346, 'normal': 0.10952663142234087, 'large.cell': 0.05803077365271747, 'squamous.cell': 0.04194618377368897}