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import streamlit as st
import torch
import torch.nn as nn
from torchvision import transforms, datasets, models
from PIL import Image
st.markdown(
"""
<style>
/* Set background image for the entire app */
.stApp {
background: url('https://cdn.i-scmp.com/sites/default/files/images/methode/2016/05/06/078562fe-1286-11e6-95eb-aaf30b46b489_image_hires.JPG') no-repeat center center fixed;
background-size: cover;
}
.stApp h1 {
background: linear-gradient(to right, #4b6cb7, #182848);
color: white;
padding: 15px 25px;
border-radius: 15px;
font-size: 2.5em;
text-align: center;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.3);
max-width: 90%;
margin: 30px auto;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
/* Style for the button */
.stButton>button {
background-color: #4CAF50; /* Green */
color: white;
font-size: 1.2em;
border-radius: 10px;
padding: 10px 24px;
border: none;
}
/* Center the button */
.stButton {
display: flex;
justify-content: center;
}
/* Style for the output container */
.output-container {
background-color: lightpink;
color: black;
font-size: 1.5em;
padding: 15px;
border-radius: 10px;
margin-top: 20px;
box-shadow: 0 4px 8px rgba(0,0,0,0.1);
width: 100%;
margin-left: auto;
margin-right: auto;
text-align: center;
}
</style>
""",
unsafe_allow_html=True
)
# Title
st.title("Brain Tumor Classification")
st.write("")
# Class names
class_names = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']
# Load pre-trained ResNet18 model
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
num_of_classes = len(class_names)
num_of_features = model.fc.in_features
model.fc = nn.Linear(num_of_features, num_of_classes)
# Load trained model weights
model.load_state_dict(torch.load('resnet18_model (1).pth', map_location=torch.device('cpu')))
model.eval()
st.markdown(
"""
<div style='
background-color: weight:
font-weight: bold;
font-size: 25px;
color: black;
margin-bottom: 10px;
'>
📤 Upload a Scan Image
</div>
""",
unsafe_allow_html=True
)
# Image upload
uploaded_img = st.file_uploader("", type=["jpg", "jpeg", "png"])
if st.button("Submit"):
if uploaded_img is not None:
# Display uploaded image in a smaller size
image = Image.open(uploaded_img)
st.image(image, caption="**Uploaded Image**", width=200)
# Image transformations
sample_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.1776, 0.1776, 0.1776], std=[0.1735, 0.1735, 0.1735])
])
# Apply transformations
transformed_img = sample_transform(image).unsqueeze(0)
# Model inference
with torch.no_grad():
pred = model(transformed_img).argmax(dim=1).item()
# Stylish output box
st.markdown(
f"""
<div class="output-container">
🧠 <strong>Predicted Class:</strong> {class_names[pred]}
</div>
""",
unsafe_allow_html=True
)
else:
st.markdown(
"""
<div style='background-color: #f8d7da; padding: 10px; border-radius: 5px;'>
<h4 style='color: #721c24;'> ⚠️ Plese upload image </h4>
</div>
""",
unsafe_allow_html=True
) |