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Create app.py
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app.py
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| 1 |
+
import cv2
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| 2 |
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import streamlit as st
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| 3 |
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st.set_page_config(layout="wide")
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| 4 |
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import streamlit.components.v1 as components
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import time
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| 6 |
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import numpy as np
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| 7 |
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import tensorflow as tf
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| 8 |
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import matplotlib.pyplot as plt
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| 9 |
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import matplotlib.cm as cm
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| 10 |
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from PIL import Image
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| 11 |
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from tf_keras_vis.gradcam import Gradcam
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| 12 |
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from io import BytesIO
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| 13 |
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| 14 |
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if "model" not in st.session_state:
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st.session_state.model = tf_model = tf.keras.models.load_model('best_model.h5')
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| 16 |
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import base64
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| 17 |
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import os
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| 18 |
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| 19 |
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#****************************************/
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| 20 |
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# GRAD CAM
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| 21 |
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#*********************************************#
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| 22 |
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| 23 |
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gradcam = Gradcam(st.session_state.model, model_modifier=None, clone=False)
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| 24 |
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| 25 |
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def generate_gradcam(pil_image, target_class):
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| 26 |
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# Convert PIL to array and preprocess
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| 27 |
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img_array = np.array(pil_image)
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| 28 |
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img_preprocessed = tf.keras.applications.vgg16.preprocess_input(img_array.copy())
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| 29 |
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img_tensor = tf.expand_dims(img_preprocessed, axis=0)
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| 30 |
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| 31 |
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# Generate heatmap
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| 32 |
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loss = lambda output: tf.reduce_mean(output[:, target_class])
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| 33 |
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cam = gradcam(loss, img_tensor, penultimate_layer=-1)
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| 34 |
+
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| 35 |
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# Process heatmap
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| 36 |
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cam = cam
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| 37 |
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if cam.ndim > 2:
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| 38 |
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cam = cam.squeeze()
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| 39 |
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cam = np.maximum(cam, 0)
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| 40 |
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cam = cv2.resize(cam, (224, 224))
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| 41 |
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cam = cam / cam.max() if cam.max() > 0 else cam
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| 42 |
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return cam
|
| 43 |
+
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| 44 |
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def convert_image_to_base64(pil_image):
|
| 45 |
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buffered = BytesIO()
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| 46 |
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pil_image.save(buffered, format="PNG")
|
| 47 |
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return base64.b64encode(buffered.getvalue()).decode()
|
| 48 |
+
|
| 49 |
+
|
| 50 |
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#--------------------------------------------------#
|
| 51 |
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class_labels=[ 'Cyst', 'Normal','Stone', 'Tumor']
|
| 52 |
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def load_tensorflow_model():
|
| 53 |
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tf_model = tf.keras.models.load_model('best_model.h5')
|
| 54 |
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return tf_model
|
| 55 |
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def predict_image(image):
|
| 56 |
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time.sleep(2)
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| 57 |
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image = image.resize((224, 224))
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| 58 |
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image = np.expand_dims(image, axis=0)
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| 59 |
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predictions = st.session_state.model.predict(image)
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| 60 |
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return predictions
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| 61 |
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logo_path = "images/tensorflow.png"
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| 62 |
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main_bg_ext = 'png'
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| 63 |
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main_bg = 'images/bg1.jpg'
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| 64 |
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# Read and encode the logo image
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| 65 |
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with open(logo_path, "rb") as image_file:
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| 66 |
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encoded_logo = base64.b64encode(image_file.read()).decode()
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| 67 |
+
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| 68 |
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# Custom CSS to style the logo above the sidebar
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| 69 |
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st.markdown(
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| 70 |
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f"""
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| 71 |
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<style>
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| 72 |
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/* Container for logo and text */
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| 73 |
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.logo-text-container {{
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| 74 |
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position: fixed;
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| 75 |
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top: 20px; /* Adjust vertical position */
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| 76 |
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left: 30px; /* Align with sidebar */
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| 77 |
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display: flex;
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| 78 |
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align-items: center;
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| 79 |
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gap: 5px;
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| 80 |
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width: 70%;
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| 81 |
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z-index:1000;
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| 82 |
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}}
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| 83 |
+
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| 84 |
+
/* Logo styling */
|
| 85 |
+
.logo-text-container img {{
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| 86 |
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width: 50px; /* Adjust logo size */
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| 87 |
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border-radius: 10px; /* Optional: round edges */
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| 88 |
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margin-top:-10px;
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| 89 |
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margin-left:-5px;
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| 90 |
+
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| 91 |
+
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| 92 |
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}}
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| 93 |
+
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| 94 |
+
/* Bold text styling */
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| 95 |
+
.logo-text-container h1 {{
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| 96 |
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font-family: Nunito;
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| 97 |
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color: #0175C2;
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| 98 |
+
font-size: 28px;
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| 99 |
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font-weight: bold;
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| 100 |
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margin-right :100px;
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| 101 |
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padding:0px;
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| 102 |
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}}
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| 103 |
+
.logo-text-container i{{
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| 104 |
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font-family: Nunito;
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| 105 |
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color: orange;
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| 106 |
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font-size: 15px;
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| 107 |
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margin-right :10px;
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| 108 |
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padding:0px;
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| 109 |
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margin-left:-18.5%;
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| 110 |
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margin-top:1%;
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| 111 |
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}}
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| 112 |
+
/* Sidebar styling */
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| 113 |
+
section[data-testid="stSidebar"][aria-expanded="true"] {{
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| 114 |
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margin-top: 100px !important; /* Space for the logo */
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| 115 |
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border-radius: 0 60px 0px 60px !important; /* Top-left and bottom-right corners */
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| 116 |
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width: 200px !important; /* Sidebar width */
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| 117 |
+
background:none; /* Gradient background */
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| 118 |
+
/* box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.2); /* Shadow effect */
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| 119 |
+
/* border: 1px solid #FFD700; /* Shiny golden border */
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| 120 |
+
margin-bottom: 1px !important;
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| 121 |
+
color:white !important;
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| 122 |
+
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| 123 |
+
}}
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| 124 |
+
header[data-testid="stHeader"] {{
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| 125 |
+
/*background: transparent !important;*/
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| 126 |
+
background: white;
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| 127 |
+
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| 128 |
+
/*margin-right: 10px !important;*/
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| 129 |
+
margin-top: 0.5px !important;
|
| 130 |
+
z-index: 1 !important;
|
| 131 |
+
|
| 132 |
+
color: orange; /* White text */
|
| 133 |
+
font-family: "Times New Roman " !important; /* Font */
|
| 134 |
+
font-size: 18px !important; /* Font size */
|
| 135 |
+
font-weight: bold !important; /* Bold text */
|
| 136 |
+
padding: 10px 20px; /* Padding for buttons */
|
| 137 |
+
border: none; /* Remove border */
|
| 138 |
+
border-radius: 1px; /* Rounded corners */
|
| 139 |
+
box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.2); /* Shadow effect */
|
| 140 |
+
transition: all 0.3s ease-in-out; /* Smooth transition */
|
| 141 |
+
align-items: left;
|
| 142 |
+
justify-content: center;
|
| 143 |
+
/*margin: 10px 0;*/
|
| 144 |
+
width:100%;
|
| 145 |
+
height:80px;
|
| 146 |
+
backdrop-filter: blur(10px);
|
| 147 |
+
border: 2px solid rgba(255, 255, 255, 0.4); /* Light border */
|
| 148 |
+
|
| 149 |
+
|
| 150 |
+
}}
|
| 151 |
+
div[data-testid="stDecoration"]{{
|
| 152 |
+
background-image:none;
|
| 153 |
+
}}
|
| 154 |
+
div[data-testid="stApp"]{{
|
| 155 |
+
/*background: grey;*/
|
| 156 |
+
background: rgba(255, 255, 255, 0.5); /* Semi-transparent white background */
|
| 157 |
+
|
| 158 |
+
height: 100vh; /* Full viewport height */
|
| 159 |
+
width: 99.5%;
|
| 160 |
+
border-radius: 2px !important;
|
| 161 |
+
margin-left:5px;
|
| 162 |
+
margin-right:5px;
|
| 163 |
+
margin-top:0px;
|
| 164 |
+
/* box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.2); /* Shadow effect */
|
| 165 |
+
|
| 166 |
+
|
| 167 |
+
background: url(data:image/{main_bg_ext};base64,{base64.b64encode(open(main_bg, "rb").read()).decode()});
|
| 168 |
+
background-size: cover; /* Ensure the image covers the full page */
|
| 169 |
+
background-position: center;
|
| 170 |
+
|
| 171 |
+
overflow: hidden;
|
| 172 |
+
|
| 173 |
+
}}
|
| 174 |
+
.content-container {{
|
| 175 |
+
background-color: rgba(173, 216, 230, 0.5); /* Light blue with 50% transparency */
|
| 176 |
+
backdrop-filter: blur(10px); /* Adds a slight blur effect */ border-radius: 1px;
|
| 177 |
+
width: 28%;
|
| 178 |
+
margin-left: 150px;
|
| 179 |
+
/* margin-top: -60px;*/
|
| 180 |
+
margin-bottom: 10px;
|
| 181 |
+
margin-right:10px;
|
| 182 |
+
padding:0;
|
| 183 |
+
/* border-radius:0px 0px 15px 15px ;*/
|
| 184 |
+
border:1px solid transparent;
|
| 185 |
+
overflow-y: auto; /* Enable vertical scrolling for the content */
|
| 186 |
+
position: fixed; /* Fix the position of the container */
|
| 187 |
+
top: 10%; /* Adjust top offset */
|
| 188 |
+
left: 60%; /* Adjust left offset */
|
| 189 |
+
height: 89.5vh; /* Full viewport height */
|
| 190 |
+
|
| 191 |
+
}}
|
| 192 |
+
.content-container2 {{
|
| 193 |
+
background-color: rgba(0, 0, 0, 0.1); /* Light blue with 50% transparency */
|
| 194 |
+
backdrop-filter: blur(10px); /* Adds a slight blur effect */ border-radius: 1px;
|
| 195 |
+
width: 90%;
|
| 196 |
+
margin-left: 10px;
|
| 197 |
+
/* margin-top: -10px;*/
|
| 198 |
+
margin-bottom: 160px;
|
| 199 |
+
margin-right:10px;
|
| 200 |
+
padding:0;
|
| 201 |
+
border-radius:1px ;
|
| 202 |
+
border:1px solid transparent;
|
| 203 |
+
overflow-y: auto; /* Enable vertical scrolling for the content */
|
| 204 |
+
position: fixed; /* Fix the position of the container */
|
| 205 |
+
top: 3%; /* Adjust top offset */
|
| 206 |
+
left: 2.5%; /* Adjust left offset */
|
| 207 |
+
height: 78vh; /* Full viewport height */
|
| 208 |
+
|
| 209 |
+
}}
|
| 210 |
+
.content-container4 {{
|
| 211 |
+
background-color: rgba(0, 0, 0, 0.1); /* Light blue with 50% transparency */
|
| 212 |
+
backdrop-filter: blur(10px); /* Adds a slight blur effect */ width: 40%;
|
| 213 |
+
margin-left: 10px;
|
| 214 |
+
margin-bottom: 160px;
|
| 215 |
+
margin-right:10px;
|
| 216 |
+
padding:0;
|
| 217 |
+
overflow-y: auto; /* Enable vertical scrolling for the content */
|
| 218 |
+
position: fixed; /* Fix the position of the container */
|
| 219 |
+
top: 60%; /* Adjust top offset */
|
| 220 |
+
left: 2.5%; /* Adjust left offset */
|
| 221 |
+
height: 10vh; /* Full viewport height */
|
| 222 |
+
|
| 223 |
+
}}
|
| 224 |
+
.content-container4 h3 ,p {{
|
| 225 |
+
font-family: "Times New Roman" !important; /* Elegant font for title */
|
| 226 |
+
font-size: 1rem;
|
| 227 |
+
font-weight: bold;
|
| 228 |
+
text-align:center;
|
| 229 |
+
}}
|
| 230 |
+
.content-container5 h3 ,p {{
|
| 231 |
+
font-family: "Times New Roman" !important; /* Elegant font for title */
|
| 232 |
+
font-size: 1rem;
|
| 233 |
+
font-weight: bold;
|
| 234 |
+
text-align:center;
|
| 235 |
+
}}
|
| 236 |
+
.content-container6 h3 ,p {{
|
| 237 |
+
font-family: "Times New Roman" !important; /* Elegant font for title */
|
| 238 |
+
font-size: 1rem;
|
| 239 |
+
font-weight: bold;
|
| 240 |
+
text-align:center;
|
| 241 |
+
}}
|
| 242 |
+
.content-container7 h3 ,p {{
|
| 243 |
+
font-family: "Times New Roman" !important; /* Elegant font for title */
|
| 244 |
+
font-size: 1rem;
|
| 245 |
+
font-weight: bold;
|
| 246 |
+
text-align:center;
|
| 247 |
+
}}
|
| 248 |
+
.content-container5 {{
|
| 249 |
+
background-color: rgba(0, 0, 0, 0.1); /* Light blue with 50% transparency */
|
| 250 |
+
backdrop-filter: blur(10px); /* Adds a slight blur effect */ width: 40%;
|
| 251 |
+
margin-left: 180px;
|
| 252 |
+
margin-bottom: 130px;
|
| 253 |
+
margin-right:10px;
|
| 254 |
+
padding:0;
|
| 255 |
+
overflow-y: auto; /* Enable vertical scrolling for the content */
|
| 256 |
+
position: fixed; /* Fix the position of the container */
|
| 257 |
+
top: 60%; /* Adjust top offset */
|
| 258 |
+
left: 5.5%; /* Adjust left offset */
|
| 259 |
+
height: 10vh; /* Full viewport height */
|
| 260 |
+
|
| 261 |
+
}}
|
| 262 |
+
.content-container3 {{
|
| 263 |
+
background-color: rgba(216, 216, 230, 0.5); /* Light blue with 50% transparency */
|
| 264 |
+
backdrop-filter: blur(10px); /* Adds a slight blur effect */ border-radius: 1px;
|
| 265 |
+
width: 92%;
|
| 266 |
+
margin-left: 10px;
|
| 267 |
+
/* margin-top: -10px;*/
|
| 268 |
+
margin-bottom: 160px;
|
| 269 |
+
margin-right:10px;
|
| 270 |
+
padding:0;
|
| 271 |
+
border: 10px solid white;
|
| 272 |
+
overflow-y: auto; /* Enable vertical scrolling for the content */
|
| 273 |
+
position: fixed; /* Fix the position of the container */
|
| 274 |
+
top: 3%; /* Adjust top offset */
|
| 275 |
+
left: 1.5%; /* Adjust left offset */
|
| 276 |
+
height: 40vh; /* Full viewport height */
|
| 277 |
+
|
| 278 |
+
}}
|
| 279 |
+
.content-container6 {{
|
| 280 |
+
background-color: rgba(0, 0, 0, 0.1); /* Light blue with 50% transparency */
|
| 281 |
+
backdrop-filter: blur(10px); /* Adds a slight blur effect */ width: 40%;
|
| 282 |
+
margin-left: 10px;
|
| 283 |
+
margin-bottom: 160px;
|
| 284 |
+
margin-right:10px;
|
| 285 |
+
padding:0;
|
| 286 |
+
overflow-y: auto; /* Enable vertical scrolling for the content */
|
| 287 |
+
position: fixed; /* Fix the position of the container */
|
| 288 |
+
top: 80%; /* Adjust top offset */
|
| 289 |
+
left: 2.5%; /* Adjust left offset */
|
| 290 |
+
height: 10vh; /* Full viewport height */
|
| 291 |
+
|
| 292 |
+
}}
|
| 293 |
+
.content-container7 {{
|
| 294 |
+
background-color: rgba(0, 0, 0, 0.1); /* Light blue with 50% transparency */
|
| 295 |
+
backdrop-filter: blur(10px); /* Adds a slight blur effect */ width: 40%;
|
| 296 |
+
margin-left: 180px;
|
| 297 |
+
margin-bottom: 130px;
|
| 298 |
+
margin-right:10px;
|
| 299 |
+
padding:0;
|
| 300 |
+
overflow-y: auto; /* Enable vertical scrolling for the content */
|
| 301 |
+
position: fixed; /* Fix the position of the container */
|
| 302 |
+
top: 80%; /* Adjust top offset */
|
| 303 |
+
left: 5.5%; /* Adjust left offset */
|
| 304 |
+
height: 10vh; /* Full viewport height */
|
| 305 |
+
|
| 306 |
+
}}
|
| 307 |
+
.content-container2 img {{
|
| 308 |
+
width:99%;
|
| 309 |
+
height:50%;
|
| 310 |
+
|
| 311 |
+
}}
|
| 312 |
+
.content-container3 img {{
|
| 313 |
+
width:100%;
|
| 314 |
+
height:100%;
|
| 315 |
+
|
| 316 |
+
}}
|
| 317 |
+
div.stButton > button {{
|
| 318 |
+
background: rgba(255, 255, 255, 0.2);
|
| 319 |
+
color: blue; /* White text */
|
| 320 |
+
font-family: "Times New Roman " !important; /* Font */
|
| 321 |
+
font-size: 18px !important; /* Font size */
|
| 322 |
+
font-weight: bold !important; /* Bold text */
|
| 323 |
+
padding: 10px 20px; /* Padding for buttons */
|
| 324 |
+
border: none; /* Remove border */
|
| 325 |
+
border-radius: 15px; /* Rounded corners */
|
| 326 |
+
box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.2); /* Shadow effect */
|
| 327 |
+
transition: all 0.3s ease-in-out; /* Smooth transition */
|
| 328 |
+
display: flex;
|
| 329 |
+
align-items: center;
|
| 330 |
+
justify-content: center;
|
| 331 |
+
margin: 10px 0;
|
| 332 |
+
width:170px;
|
| 333 |
+
height:60px;
|
| 334 |
+
backdrop-filter: blur(10px);
|
| 335 |
+
|
| 336 |
+
}}
|
| 337 |
+
|
| 338 |
+
/* Hover effect */
|
| 339 |
+
div.stButton > button:hover {{
|
| 340 |
+
background: rgba(255, 255, 255, 0.2);
|
| 341 |
+
box-shadow: 0px 6px 12px rgba(0, 0, 0, 0.4); /* Enhanced shadow on hover */
|
| 342 |
+
transform: scale(1.05); /* Slightly enlarge button */
|
| 343 |
+
transform: scale(1.1); /* Slight zoom on hover */
|
| 344 |
+
box-shadow: 0px 4px 12px rgba(255, 255, 255, 0.4); /* Glow effect */
|
| 345 |
+
}}
|
| 346 |
+
.titles{{
|
| 347 |
+
margin-top:50px !important;
|
| 348 |
+
}}
|
| 349 |
+
/* Title styling */
|
| 350 |
+
.titles h1{{
|
| 351 |
+
/*font-family: "Times New Roman" !important; /* Elegant font for title */
|
| 352 |
+
/* font-size: 2.9rem;*/
|
| 353 |
+
/*font-weight: bold;*/
|
| 354 |
+
margin-left: 5px;
|
| 355 |
+
/* margin-top:-50px;*/
|
| 356 |
+
margin-bottom:50px;
|
| 357 |
+
padding: 0;
|
| 358 |
+
color: black; /* Neutral color for text */
|
| 359 |
+
}}
|
| 360 |
+
.titles > div{{
|
| 361 |
+
font-family: "Times New Roman" !important; /* Elegant font for title */
|
| 362 |
+
font-size: 1.2rem;
|
| 363 |
+
margin-left: 5px;
|
| 364 |
+
margin-bottom:1px;
|
| 365 |
+
padding: 0;
|
| 366 |
+
color:black; /* Neutral color for text */
|
| 367 |
+
}}
|
| 368 |
+
/* Recently viewed section */
|
| 369 |
+
.recently-viewed {{
|
| 370 |
+
display: flex;
|
| 371 |
+
align-items: center;
|
| 372 |
+
justify-content: flex-start; /* Align items to the extreme left */
|
| 373 |
+
margin-bottom: 10px;
|
| 374 |
+
margin-top: 20px;
|
| 375 |
+
gap: 10px; /* Add spacing between the elements */
|
| 376 |
+
padding-left: 20px; /* Add some padding if needed */
|
| 377 |
+
margin-left:35px;
|
| 378 |
+
height:100px;
|
| 379 |
+
|
| 380 |
+
}}
|
| 381 |
+
|
| 382 |
+
|
| 383 |
+
|
| 384 |
+
|
| 385 |
+
|
| 386 |
+
/* Style for the upload button */
|
| 387 |
+
[class*="st-key-upload-btn"] {{
|
| 388 |
+
position: absolute;
|
| 389 |
+
top: 100%; /* Position from the top of the inner circle */
|
| 390 |
+
left: -3%; /* Position horizontally at the center */
|
| 391 |
+
padding: 10px 20px;
|
| 392 |
+
color: red;
|
| 393 |
+
border: none;
|
| 394 |
+
border-radius: 20px;
|
| 395 |
+
cursor: pointer;
|
| 396 |
+
font-size: 35px !important;
|
| 397 |
+
width:30px;
|
| 398 |
+
height:20px;
|
| 399 |
+
}}
|
| 400 |
+
|
| 401 |
+
.upload-btn:hover {{
|
| 402 |
+
background-color: rgba(0, 123, 255, 1);
|
| 403 |
+
}}
|
| 404 |
+
div[data-testid="stFileUploader"] label > div > p {{
|
| 405 |
+
display:none;
|
| 406 |
+
color:white !important;
|
| 407 |
+
}}
|
| 408 |
+
section[data-testid="stFileUploaderDropzone"] {{
|
| 409 |
+
width:200px;
|
| 410 |
+
height: 60px;
|
| 411 |
+
background-color: white;
|
| 412 |
+
border-radius: 40px;
|
| 413 |
+
display: flex;
|
| 414 |
+
justify-content: center;
|
| 415 |
+
align-items: center;
|
| 416 |
+
margin-top:-10px;
|
| 417 |
+
box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.3);
|
| 418 |
+
margin:20px;
|
| 419 |
+
background-color: rgba(255, 255, 255, 0.7); /* Transparent blue background */
|
| 420 |
+
color:white;
|
| 421 |
+
}}
|
| 422 |
+
div[data-testid="stFileUploaderDropzoneInstructions"] div > small{{
|
| 423 |
+
color:white !important;
|
| 424 |
+
display:none;
|
| 425 |
+
}}
|
| 426 |
+
div[data-testid="stFileUploaderDropzoneInstructions"] span{{
|
| 427 |
+
margin-left:65px;
|
| 428 |
+
color:orange;
|
| 429 |
+
}}
|
| 430 |
+
div[data-testid="stFileUploaderDropzoneInstructions"] div{{
|
| 431 |
+
display:none;
|
| 432 |
+
}}
|
| 433 |
+
section[data-testid="stFileUploaderDropzone"] button{{
|
| 434 |
+
display:none;
|
| 435 |
+
}}
|
| 436 |
+
div[data-testid="stMarkdownContainer"] p {{
|
| 437 |
+
font-family: "Times New Roman" !important; /* Elegant font for title */
|
| 438 |
+
color:white !important;
|
| 439 |
+
}}
|
| 440 |
+
.highlight {{
|
| 441 |
+
border: 4px solid lime;
|
| 442 |
+
font-weight: bold;
|
| 443 |
+
background: radial-gradient(circle, rgba(0,255,0,0.3) 0%, rgba(0,0,0,0) 70%);
|
| 444 |
+
box-shadow: 0px 0px 30px 10px rgba(0, 255, 0, 0.9),
|
| 445 |
+
0px 0px 60px 20px rgba(0, 255, 0, 0.6),
|
| 446 |
+
inset 0px 0px 15px rgba(0, 255, 0, 0.8);
|
| 447 |
+
transition: all 0.3s ease-in-out;
|
| 448 |
+
|
| 449 |
+
}}
|
| 450 |
+
.highlight:hover {{
|
| 451 |
+
transform: scale(1.05);
|
| 452 |
+
background: radial-gradient(circle, rgba(0,255,0,0.6) 0%, rgba(0,0,0,0) 80%);
|
| 453 |
+
box-shadow: 0px 0px 40px 15px rgba(0, 255, 0, 1),
|
| 454 |
+
0px 0px 70px 30px rgba(0, 255, 0, 0.7),
|
| 455 |
+
inset 0px 0px 20px rgba(0, 255, 0, 1);
|
| 456 |
+
}}
|
| 457 |
+
</style>
|
| 458 |
+
<div class="logo-text-container">
|
| 459 |
+
<img src="data:image/png;base64,{encoded_logo}" alt="Logo">
|
| 460 |
+
<h1>KidneyScan AI<br>
|
| 461 |
+
|
| 462 |
+
</h1>
|
| 463 |
+
<i>Empowering Early Diagnosis with AI</ai>
|
| 464 |
+
|
| 465 |
+
|
| 466 |
+
</div>
|
| 467 |
+
""", unsafe_allow_html=True
|
| 468 |
+
)
|
| 469 |
+
loading_html = """
|
| 470 |
+
<style>
|
| 471 |
+
.loader {
|
| 472 |
+
border: 8px solid #f3f3f3;
|
| 473 |
+
border-top: 8px solid #0175C2; /* Blue color */
|
| 474 |
+
border-radius: 50%;
|
| 475 |
+
width: 50px;
|
| 476 |
+
height: 50px;
|
| 477 |
+
animation: spin 1s linear infinite;
|
| 478 |
+
margin: auto;
|
| 479 |
+
}
|
| 480 |
+
@keyframes spin {
|
| 481 |
+
0% { transform: rotate(0deg); }
|
| 482 |
+
100% { transform: rotate(360deg); }
|
| 483 |
+
}
|
| 484 |
+
|
| 485 |
+
</style>
|
| 486 |
+
<div class="loader"></div>
|
| 487 |
+
"""
|
| 488 |
+
|
| 489 |
+
page = "Home"
|
| 490 |
+
|
| 491 |
+
# Display content based on the selected page
|
| 492 |
+
# Define the page content dynamically
|
| 493 |
+
if page == "Home":
|
| 494 |
+
|
| 495 |
+
|
| 496 |
+
#components.html(html_string) # JavaScript works
|
| 497 |
+
#st.markdown(html_string, unsafe_allow_html=True)
|
| 498 |
+
image_path = "images/download.jfif"
|
| 499 |
+
|
| 500 |
+
st.container()
|
| 501 |
+
st.markdown(f"""
|
| 502 |
+
|
| 503 |
+
<div class="titles">
|
| 504 |
+
<h1>Kidney Disease Classfication</br> Using Transfer learning</h1>
|
| 505 |
+
<div> This web application utilizes deep learning to classify kidney ultrasound images</br>
|
| 506 |
+
into four categories: Normal, Cyst, Tumor, and Stone Class.
|
| 507 |
+
Built with Streamlit and powered by </br>a TensorFlow transfer learning
|
| 508 |
+
model based on <strong>VGG16</strong>
|
| 509 |
+
the app provides a simple and efficient way for users </br>
|
| 510 |
+
to upload kidney scans and receive instant predictions. The model analyzes the image
|
| 511 |
+
and classifies it based </br>on learned patterns, offering a confidence score for better interpretation.
|
| 512 |
+
</div>
|
| 513 |
+
</div>
|
| 514 |
+
""",
|
| 515 |
+
unsafe_allow_html=True,
|
| 516 |
+
)
|
| 517 |
+
uploaded_file = st.file_uploader("Choose a file", type=["png", "jpg", "jpeg"],key="upload-btn")
|
| 518 |
+
if uploaded_file is not None:
|
| 519 |
+
images = Image.open(uploaded_file)
|
| 520 |
+
# Rewind file pointer to the beginning
|
| 521 |
+
uploaded_file.seek(0)
|
| 522 |
+
|
| 523 |
+
file_content = uploaded_file.read() # Read file once
|
| 524 |
+
# Convert to base64 for HTML display
|
| 525 |
+
encoded_image = base64.b64encode(file_content).decode()
|
| 526 |
+
# Read and process image
|
| 527 |
+
pil_image = Image.open(uploaded_file).convert('RGB').resize((224, 224))
|
| 528 |
+
img_array = np.array(pil_image)
|
| 529 |
+
|
| 530 |
+
prediction = predict_image(images)
|
| 531 |
+
max_index = int(np.argmax(prediction[0]))
|
| 532 |
+
print(f"max index:{max_index}")
|
| 533 |
+
max_score = prediction[0][max_index]
|
| 534 |
+
predicted_class = np.argmax(prediction[0])
|
| 535 |
+
|
| 536 |
+
highlight_class = "highlight" # Special class for the highest confidence score
|
| 537 |
+
|
| 538 |
+
|
| 539 |
+
# Generate Grad-CAM
|
| 540 |
+
cam = generate_gradcam(pil_image, predicted_class)
|
| 541 |
+
|
| 542 |
+
# Create overlay
|
| 543 |
+
heatmap = cm.jet(cam)[..., :3]
|
| 544 |
+
heatmap = (heatmap * 255).astype(np.uint8)
|
| 545 |
+
overlayed_image = cv2.addWeighted(img_array, 0.6, heatmap, 0.4, 0)
|
| 546 |
+
|
| 547 |
+
# Convert to PIL
|
| 548 |
+
overlayed_pil = Image.fromarray(overlayed_image)
|
| 549 |
+
# Convert to base64
|
| 550 |
+
orig_b64 = convert_image_to_base64(pil_image)
|
| 551 |
+
overlay_b64 = convert_image_to_base64(overlayed_pil)
|
| 552 |
+
content = f"""
|
| 553 |
+
<div class="content-container">
|
| 554 |
+
<!-- Title -->
|
| 555 |
+
<!-- Recently Viewed Section -->
|
| 556 |
+
<div class="content-container2">
|
| 557 |
+
<div class="content-container3">
|
| 558 |
+
<img src="data:image/png;base64,{orig_b64}" alt="Uploaded Image">
|
| 559 |
+
</div>
|
| 560 |
+
<div class="content-container3">
|
| 561 |
+
<img src="data:image/png;base64,{overlay_b64}" class="result-image">
|
| 562 |
+
</div>
|
| 563 |
+
<div class="content-container4 {'highlight' if max_index == 0 else ''}">
|
| 564 |
+
<h3>{class_labels[0]}</h3>
|
| 565 |
+
<p>T Score: {prediction[0][0]:.2f}</p>
|
| 566 |
+
</div>
|
| 567 |
+
<div class="content-container5 {'highlight' if max_index == 1 else ''}">
|
| 568 |
+
<h3> {class_labels[1]}</h3>
|
| 569 |
+
<p>T Score: {prediction[0][1]:.2f}</p>
|
| 570 |
+
</div>
|
| 571 |
+
<div class="content-container6 {'highlight' if max_index == 2 else ''}">
|
| 572 |
+
<h3> {class_labels[2]}</h3>
|
| 573 |
+
<p>T Score: {prediction[0][2]:.2f}</p>
|
| 574 |
+
</div>
|
| 575 |
+
<div class="content-container7 {'highlight' if max_index == 3 else ''}">
|
| 576 |
+
<h3>{class_labels[3]}</h3>
|
| 577 |
+
<p>T Score: {prediction[0][3]:.2f}</p>
|
| 578 |
+
</div>
|
| 579 |
+
|
| 580 |
+
|
| 581 |
+
"""
|
| 582 |
+
|
| 583 |
+
# Close the gallery and content div
|
| 584 |
+
|
| 585 |
+
# Render the content
|
| 586 |
+
st.markdown(content, unsafe_allow_html=True)
|
| 587 |
+
else:
|
| 588 |
+
default_image_path = "images/download.jfif"
|
| 589 |
+
with open(image_path, "rb") as image_file:
|
| 590 |
+
encoded_image = base64.b64encode(image_file.read()).decode()
|
| 591 |
+
|
| 592 |
+
|
| 593 |
+
st.markdown(f"""
|
| 594 |
+
<div class="content-container">
|
| 595 |
+
<!-- Title -->
|
| 596 |
+
<!-- Recently Viewed Section -->
|
| 597 |
+
<div class="content-container2">
|
| 598 |
+
<div class="content-container3">
|
| 599 |
+
<img src="data:image/png;base64,{encoded_image}" alt="Default Image">
|
| 600 |
+
</div>
|
| 601 |
+
</div>
|
| 602 |
+
|
| 603 |
+
""",
|
| 604 |
+
unsafe_allow_html=True,
|
| 605 |
+
)
|
| 606 |
+
|
| 607 |
+
|